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  • 601.
    Zappone, Alessio
    et al.
    TU Dresden, Dresden, Germany.
    Björnson, Emil
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Sanguinetti, Luca
    University of Pisa, Italy; CentraleSupelec, France.
    Jorswieck, Eduard
    TU Dresden, Dresden, Germany.
    A Framework for Globally Optimal Energy-Efficient Resource Allocation in Wireless Networks2016Conference paper (Refereed)
  • 602.
    Zappone, Alessio
    et al.
    University of Cassino and Southern Lazio, Italy.
    Björnson, Emil
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Sanguinetti, Luca
    University of Pisa, Italy; CentraleSupelec, France.
    Jorswieck, Eduard
    Technical University of Dresden, Germany.
    Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks2017In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 65, no 11, p. 2844-2859Article in journal (Refereed)
    Abstract [en]

    The characterization of the global maximum of energy efficiency (EE) problems in wireless networks is a challenging problem due to their nonconvex nature in interference channels. The aim of this paper is to develop a new and general framework to achieve globally optimal solutions. First, the hidden monotonic structure of the most common EE maximization problems is exploited jointly with fractional programming theory to obtain globally optimal solutions with exponential complexity in the number of network links. To overcome the high complexity, we also propose a framework to compute suboptimal power control strategies with affordable complexity. This is achieved by merging fractional programming and sequential optimization. The proposed monotonic framework is used to shed light on the ultimate performance of wireless networks in terms of EE and also to benchmark the performance of the lower-complexity framework based on sequential programming. Numerical evidence is provided to show that the sequential fractional programming framework achieves global optimality in several practical communication scenarios.

  • 603.
    ZeSong, Fei
    et al.
    Beijing Institute of Technology, China.
    ChengWen, Xing
    Beijing Institute of Technology, China.
    Na, Li
    Beijing Institute of Technology, China.
    YanTao, Han
    Beijing Institute of Technology, China.
    Danev, Danyo
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    JingMing, Kuang
    Beijing Institute of Technology, China.
    Power allocation for OFDM-based cognitive heterogeneous networks2013In: Science China Information Sciences, ISSN 1674-733X, Vol. 56, no 4, p. 1-10Article in journal (Refereed)
    Abstract [en]

    In this paper, the capacity maximization and the spectrum utilization efficiency improvement are investigated for the Pico cells in broadband heterogeneous networks. In frequency-reuse model, the users attached to Macro base station are usually viewed as primary users, and those attached to Pico base station should be regarded as cognitive radio (CR) users. As both the primary users and the CR users communicate in parallel frequency bands, the performance of the system is limited by the mutual inter-carrier interference (ICI). In order to control ICI and maximize the achievable transmission rate of the CR users, an effective power allocation scheme is proposed to maximize the transmission rate of the CR users under a given interference threshold prescribed by the primary users. By transforming this suboptimal solution into an innovative matrix expression, the algorithm is easier to perform in practice. The simulation results demonstrate that the proposed algorithm provides a large performance gain in Pico cell capacity over the non-cooperative and equal power allocation schemes.

  • 604.
    Zhang, Jiayi
    et al.
    Beijing Jiaotong University, Peoples R China.
    Dai, Linglong
    Tsinghua University, Peoples R China.
    Zhang, Xinlin
    Chalmers, Sweden.
    Björnson, Emil
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Wang, Zhaocheng
    Tsinghua University, Peoples R China.
    Achievable Rate of Rician Large-Scale MIMO Channels With Transceiver Hardware Impairments2016In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 65, no 10, p. 8800-8806Article in journal (Refereed)
    Abstract [en]

    Transceiver hardware impairments (e.g., phase noise, inphase/quadrature-phase imbalance, amplifier nonlinearities, and quantization errors) have obvious degradation effects on the performance of wireless communications. While prior works have improved our knowledge of the influence of hardware impairments of single-user multiple-input multiple-output ( MIMO) systems over Rayleigh fading channels, an analysis encompassing the Rician fading channel is not yet available. In this paper, we pursue a detailed analysis of regular and large-scale (LS) MIMO systems over Rician fading channels by deriving new closed-form expressions for the achievable rate to provide several important insights for practical system design. More specifically, for regular MIMO systems with hardware impairments, there is always a finite achievable rate ceiling, which is irrespective of the transmit power and fading conditions. For LS-MIMO systems, it is interesting to find that the achievable rate loss depends on the Rician K-factor, which reveals that the favorable propagation in LS-MIMO systems can remove the influence of hardware impairments. However, we show that the nonideal LS-MIMO system can still achieve high spectral efficiency due to its huge degrees of freedom.

  • 605.
    Zhang, Jiayi
    et al.
    Beijing Jiaotong Univ, Peoples R China; Southeast Univ, Peoples R China.
    Wei, Yinghua
    Beijing Jiaotong Univ, Peoples R China.
    Björnson, Emil
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Han, Yu
    Southeast Univ, Peoples R China.
    Jin, Shi
    Southeast Univ, Peoples R China.
    Performance Analysis and Power Control of Cell-Free Massive MIMO Systems with Hardware Impairments2018In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 55302-55314Article in journal (Refereed)
    Abstract [en]

    As an interesting network architecture for future wireless communication systems, cell-free (CF) massive multiple-input multiple-output (MIMO) distributes an excess number of access points (APs) with single or multiple antennas to cooperatively communicate with several user equipments (UEs). To realize CF massive MIMO in production, hardware impairments become a crucial problem since cheaper and low-quality antennas are needed to ensure economic and energy feasibility. In this paper, we propose a framework for performance analysis in the CF massive MIMO with classical hardware distortion models. For both uplink and downlink, closed-form spectral and energy efficiency expressions are derived, respectively. Based on these results, we provide significant insights into the practical impact of hardware impairments on CF massive MIMO. For example, the impact of hardware distortion at the APs asymptotically vanishes. Furthermore, in order to ensure uniformly good service to the users, we propose a max-min power control algorithm to maximize the minimum UE rate. Via analytical and numerical results, we prove that CF massive MIMO can tolerate hardware impairments without performance reduction.

  • 606.
    Zhang, Jiayi
    et al.
    Beijing Jiaotong Univ, Peoples R China.
    Wei, Yinghua
    Beijing Jiaotong Univ, Peoples R China.
    Björnson, Emil
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Han, Yu
    Southeast Univ, Peoples R China.
    Li, Xu
    Beijing Jiaotong Univ, Peoples R China.
    Spectral and Energy Efficiency of Cell-Free Massive MIMO Systems with Hardware Impairments2017In: 2017 9TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS AND SIGNAL PROCESSING (WCSP), IEEE , 2017Conference paper (Refereed)
    Abstract [en]

    Cell-free massive multiple-input multiple-output (MIMO), with a large number of distributed access points (APs) that jointly serve the user equipments (UEs), is a promising network architecture for future wireless communications. To reduce the cost and power consumption of such systems, it is important to utilize low-quality transceiver hardware at the APs. However, the impact of hardware impairments on cell-free massive MIMO has thus far not been studied. In this paper, we take a first look at this important topic by utilizing well-established models of hardware distortion and deriving new closed-form expressions for the spectral and energy efficiency. These expressions provide important insights into the practical impact of hardware impairments and also how to efficiently deploy cell-free systems. Furthermore, a novel hardware-quality scaling law is presented. It proves that the impact of hardware impairments at the APs vanish as the number of APs grows. Numerical results validate that cell-free massive MIMO systems are inherently resilient to hardware impairments.

  • 607.
    Zhang, Jiayi
    et al.
    Beijing Jiaotong Univ, Peoples R China.
    Xue, Xipeng
    Beijing Jiaotong Univ, Peoples R China.
    Björnson, Emil
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Ai, Bo
    Beijing Jiaotong Univ, Peoples R China.
    Jin, Shi
    Southeast Univ, Peoples R China.
    Spectral Efficiency of Multipair Massive MIMO Two-Way Relaying With Hardware Impairments2018In: IEEE Wireless Communications Letters, ISSN 2162-2337, E-ISSN 2162-2345, Vol. 7, no 1, p. 14-17Article in journal (Refereed)
    Abstract [en]

    We consider a multipair massive multiple-input multiple-output (MIMO) two-way relaying system, where multiple pairs of single-antenna devices exchange data with the help of a relay employing a large number of antennas N. The relay consists of low-cost components that suffer from hardware impairments. A large-scale approximation of the spectral efficiency with maximum ratio processing is derived in closed form, and the approximation is tight as N -amp;gt; infinity. It is revealed that for a fixed hardware quality, the impact of the hardware impairments vanishes asymptotically when N grows large. Moreover, the impact of the impairments may even vanish when the hardware quality is gradually decreased with N, if a scaling law is satisfied. Finally, numerical results validate that multipair massive MIMO two-way relaying systems are robust to hardware impairments at the relay.

  • 608.
    Zhang, Xinlin
    et al.
    Chalmers, Sweden.
    Matthaiou, Michail
    Chalmers, Sweden; Queens University of Belfast, North Ireland.
    Coldrey, Mikael
    Ericsson AB, Sweden.
    Björnson, Emil
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Energy Efficiency Optimization in Hardware-Constrained Large-Scale MIMO Systems2014In: 2014 11TH INTERNATIONAL SYMPOSIUM ON WIRELESS COMMUNICATIONS SYSTEMS (ISWCS), IEEE , 2014, p. 992-996Conference paper (Refereed)
    Abstract [en]

    Large-scale multiple-input multiple-output (MIMO) communication systems can bring substantial improvement in spectral efficiency and/or energy efficiency, due to the excessive degrees-of-freedom and huge array gain. However, large-scale MINI is expected to deploy lower-cost radio frequency (RF) components, which are particularly prone to hardware impairments. Unfortunately, compensation schemes are not able to remove the impact of hardware impairments completely, such that a certain amount of residual impairments always exists. In this paper, we investigate the impact of residual transmit RF impairments (RTRI) on the spectral and energy efficiency of training-based point-to-point large-scale MIMO systems, and seek to determine the optimal training length and number of antennas which maximize the energy efficiency. We derive deterministic equivalents of the signal-to-noise-and-interference ratio (SINR) with zero-forcing (ZF) receivers, as well as the corresponding spectral and energy efficiency, which are shown to be accurate even for small number of antennas. Through an iterative sequential optimization, we find that the optimal training length of systems with RTRI can be smaller compared to ideal hardware systems in the moderate SNR regime, while larger in the high SNR regime. Moreover, it is observed that RTRI can significantly decrease the optimal number of transmit and receive antennas.

  • 609.
    Zhang, Xinlin
    et al.
    Chalmers, Sweden.
    Matthaiou, Michail
    Chalmers, Sweden; Queens University of Belfast, North Ireland.
    Coldrey, Mikael
    Ericsson Research Gothenburg, Sweden.
    Björnson, Emil
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Impact of Residual Transmit RF Impairments on Training-Based MIMO Systems2015In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 63, no 8, p. 2899-2911Article in journal (Refereed)
    Abstract [en]

    Radio-frequency (RF) impairments, which intimately exist in wireless communication systems, can severely limit the performance of multiple-input-multiple-output (MIMO) systems. Although we can resort to compensation schemes to mitigate some of these impairments, a certain amount of residual impairments always persists. In this paper, we consider a training-based point-to-point MIMO system with residual transmit RF impairments (RTRI) using spatial multiplexing transmission. Specifically, we derive a new linear channel estimator for the proposed model, and show that RTRI create an estimation error floor in the high signal-to-noise ratio (SNR) regime. Moreover, we derive closed-form expressions for the signal-to-noise-plus-interference ratio (SINR) distributions, along with analytical expressions for the ergodic achievable rates of zero-forcing, maximum ratio combining, and minimum mean-squared error receivers, respectively. In addition, we optimize the ergodic achievable rates with respect to the training sequence length and demonstrate that finite dimensional systems with RTRI generally require more training at high SNRs than those with ideal hardware. Finally, we extend our analysis to large-scale MIMO configurations, and derive deterministic equivalents of the ergodic achievable rates. It is shown that, by deploying large receive antenna arrays, the extra training requirements due to RTRI can be eliminated. In fact, with a sufficiently large number of receive antennas, systems with RTRI may even need less training than systems with ideal hardware.

  • 610.
    Zheng, Ma
    et al.
    Southwest Jiaotong University, Peoples R China .
    Persson, Daniel
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    PingZhi, Fan
    Southwest Jiaotong University, Peoples R China .
    Multiple symbols soft-decision metrics for coded frequency-shift keying signals2013In: SCIENCE CHINA-INFORMATION SCIENCES, ISSN 1674-733X, Vol. 56, no 2Article in journal (Refereed)
    Abstract [en]

    This paper derives a novel multiple symbols soft-decision metrics for frequency-shift keying signals which are affected by additive symmetric alpha-stable (S alpha S) noise and fading. The approximate metric, which is for the case where channel state information (phases, amplitudes, and noise dispersion parameter) is unknown is obtained based on a generalized-likelihood ratio (GLR) approach. The metric is obtained in closed form and proved to be effective. The performances of the multiple symbols soft-decision metrics are compared numerically for a turbo-coded system. The proposed multiple symbols metric provides substantial improvement over earlier single-symbol metrics.

  • 611.
    Örn, Sara
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Realistic Multi-Cell Interference Coordination in 4G/LTE2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In the LTE mobile system, all cells use the same set of frequencies. This means that a user could experience interference from other cells. A method that has been studied in order to reduce this interference and thereby increase data rate or system throughput is to coordinate scheduling between cells. Good results of this have been found in different studies. However, the interference is generally assumed to be known. Studies using estimated interference and simulating more than one cluster of cells have found almost no gain.

    This thesis will focus on how to use information from coordinated scheduling and other traffic estimates to do better interference estimation and link adaption. The suggested method is to coordinate larger clusters and use the coordination information, as well as estimates of which cells will be transmitting, to make estimates of interference from other cells. The additional information from interference estimation is used in the link adaptation. Limitations in bandwidth of the backhaul needed to send data between cells are considered, as well as the delay it may introduce. A limitation of the scope is that MIMO or HetNet scenarios have not been simulated.

    The suggested method for interference estimation and link adaptation have been implemented and simulated in a system simulator. The method gives a less biased estimate of SINR, but there are no gains in user bit rate. The lesser bias is since the method is better at predicting high SINR than the base estimate is. The lack of gains regarding user bit rate may result from the fact that in the studied scenarios, users where not able to make use of the higher estimated SINR since the base estimate is already high.

    The conclusion is that the method might be useful in scenarios where there are not full load, but the users either have bad channel quality or are able to make use of very high SINR. Such scenarios could be HetNet or MIMO scenarios, respectively.

  • 612.
    Östlund, Pierre
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Design, implementation och simulering av ett MAC-protokoll för mobila trådlösa sensornätverk2014Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [sv]

    Trådlösa sensornätverk byggs upp av trådlösa sensorer, som gemensamt arbetar för att lösa en viss uppgift. Ett exempel på en sådan uppgift kan vara insamling av pollennivåer i luften över en stor yta. Sensornoderna vidarebefordrar datan sinsemellan tills den når en datainsamlingsnod någonstans i nätverket där den sedan lagras och efterbehandlas. Generellt gäller att sensornoder är små, billiga, kommunicerar trådlöst och har en väldigt lång livslängd. Traditionellt sett har sen- sornoder också antagits vara statiska (stillastående), vilket medför begränsningar om noderna bärs av exempelvis människor eller monteras på fordon.

    I detta examensarbete presenteras matmac , ett mac-protokoll som designats för att hantera mobila noder i trådlösa sensornätverk. En referensimplementa- tion av matmac har implementerats i operativsystemet Contiki och utvärderats med varierande konfigurationsparametrar, rörelsehastigheter och dataintensitet i simulatorn Cooja. Resultatet från utvärderingen visar att mekanismerna för mo- bilitetshantering i matmac främjar sensornodernas förmåga att pålitligt överföra data trots att de är mobila. 

  • 613.
    Özdogan, Özgecan
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Björnson, Emil
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Massive MIMO With Spatially Correlated Rician Fading Channels2019In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 67, no 5, p. 3234-3250Article in journal (Refereed)
    Abstract [en]

    This paper considers multi-cell massive multiple-input multiple-output systems, where the channels are spatially correlated Rician fading. The channel model is composed of a deterministic line-of-sight path and a stochastic non-line-of-sight component describing a practical spatially correlated multipath environment. We derive the statistical properties of the minimum mean squared error (MMSE), element-wise MMSE, and least-square channel estimates for this model. Using these estimates for maximum ratio combining and precoding, rigorous closed-form uplink (UL) and downlink (DL) achievable spectral efficiency (SE) expressions are derived and analyzed. The asymptotic SE behavior, when using the different channel estimators, are also analyzed. The numerical results show that the SE is higher when using the MMSE estimator than that of the other estimators, and the performance gap increases with the number of antennas.

  • 614.
    Özdogan, Özgecan
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Björnson, Emil
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Larsson, Erik G
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Uplink Spectral Efficiency of Massive MIMO with Spatially Correlated Rician Fading2018In: 2018 IEEE 19TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC), IEEE , 2018, p. 216-220Conference paper (Refereed)
    Abstract [en]

    This paper considers the uplink (UL) of a multicell Massive MIMO (multiple-input multiple-output) system with spatially correlated Rician fading channels. The channel model is composed of a deterministic line-of-sight (LoS) path and a stochastic non-line-of-sight (NLoS) component describing a spatially correlated multipath environment. We derive the statistical properties of the minimum mean squared error (MMSE) and least-square (LS) channel estimates for this model. Using these estimates for maximum ratio (MR) combining, rigorous closed-form UL spectral efficiency (SE) expressions are derived. Numerical results show that the SE is higher when using the MMSE estimator than the LS estimator, and the performance gap increases with the number of antennas. Moreover, Rician fading provides higher achievable SEs than Rayleigh fading since the LoS path improves the sum SE.

  • 615.
    Özdogan, Özgecan
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Björnson, Emil
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Zhang, Jiayi
    Beijing Jiaotong Univ, Peoples R China.
    Cell-Free Massive MIMO with Rician Fading: Estimation Schemes and Spectral Efficiency2018In: 2018 CONFERENCE RECORD OF 52ND ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS, IEEE , 2018, p. 975-979Conference paper (Refereed)
    Abstract [en]

    As the cell sizes in cellular networks shrink, the inter-cell interference becomes more of an issue. Instead of operating each cell autonomously, we can connect all the access points (APs) together to form a cell-free massive MIMO (multiple-input multiple-output) system that can alleviate interference by spatial processing. Previous studies have focused on Rayleigh fading channels, but in densely deployed systems, it is likely that some of the users will have line-of-sight (LoS) propagation to some of the APs. In this paper, we model this by arbitrarily distributed Rician fading channels. Two types of channel estimators are considered: a classical least-square (LS) estimator and a Bayesian minimum mean square error (MMSE) estimator. We derive closed-form spectral efficiency (SE) expressions for the uplink (UL) and downlink (DL) when using each of these estimators for maximum ratio (MR) processing. The performance difference is evaluated numerically to figure out under which conditions it is beneficial to know the channel statistics when estimating a channel.

  • 616.
    Čirkić, Mirsad
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Efficient MIMO Detection Methods2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    For the past decades, the demand in transferring large amounts of data rapidly and reliably has been increasing drastically. One of the more promising techniques that can provide the desired performance is multiple-input multiple-output (MIMO) technology where multiple antennas are placed at both the transmitting and receiving side of the communication link. This performance potential is extremely high when the dimensions of the MIMO system are increased to an extreme (in the number of hundreds or thousands of antennas). One major implementation difficulty of the MIMO technology is the signal separation (detection) problem at the receiving side of the MIMO link, which holds for medium-size MIMO systems and even more so for large-size systems. This is due to the fact that the transmitted signals interfere with each other and that separating them can be very difficult if the MIMO channel conditions are not beneficial, i.e., the channel is not well-conditioned.

    The main problem of interest is to develop algorithms for practically feasible MIMO implementations without sacrificing the promising performance potential that such systems bring. These methods involve inevitably different levels of approximation. There are computationally cheap methods that come with low accuracy and there are computationally expensive methods that come with high accuracy. Some methods are more applicable in medium-size MIMO than in large-size MIMO and vice versa. Some simple methods for instance, which are typically inaccurate for medium-sized settings, can achieve optimal accuracy for certain large-sized settings that offer close-to-orthogonal spatial signatures. However, when the dimensions are overly increased, then even these (previously) simple methods become computationally burdensome. In different MIMO setups, the difficulty in detection shifts since methods with optimal accuracy are not the same. Therefore, devising one single algorithm which is well-suited for feasible MIMO implementations in all settings is not easy.

    This thesis addresses the general MIMO detection problem in two ways. One part treats a development of new and more efficient detection techniques for the different MIMO settings. The techniques that are proposed in this thesis demonstrate unprecedented performance in many relevant cases. The other part revolves around utilizing already proposed detection algorithms and their advantages versus disadvantages in an adaptive manner. For well-conditioned channels, low-complexity detection methods are often sufficiently accurate. In such cases, performing computationally very expensive optimal detection would be a waste of computational power. This said, for MIMO detection in a coded system, there is always a trade-off between performance and complexity. Intuitively, computational resources should be utilized more efficiently by performing optimal detection only when it is needed, and something simpler when it is not. However, it is not clear whether this is true or not. In trying to answer this, a general framework for adaptive computational-resource allocation to different (“simple” and “difficult”) detection problems is proposed. This general framework is applicable to any MIMO detector and scenario of choice, and it is exemplified using one particular detection method for which specific allocation techniques are developed and evaluated.

    List of papers
    1. Allocation of Computational Resources for Soft MIMO Detection
    Open this publication in new window or tab >>Allocation of Computational Resources for Soft MIMO Detection
    2011 (English)In: IEEE Journal of Selected Topics in Signal Processing, ISSN 1932-4553, Vol. 5, no 8, p. 1451-1461Article in journal (Refereed) Published
    Abstract [en]

    We consider soft MIMO detection for the case of block fading. That is, the transmitted codeword spans over several independent channel realizations and several instances of the detection problem must be solved for each such realization. We develop methods that adaptively allocate computational resources to the detection problems of each channel realization, under a total per-codeword complexity constraint. Our main results are a formulation of the problem as a mathematical optimization problem with a well-defined objective function and constraints, and algorithms that solve this optimization problem efficiently computationally.

    Place, publisher, year, edition, pages
    IEEE conference proceedings, 2011
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:liu:diva-69612 (URN)10.1109/JSTSP.2011.2162719 (DOI)000297348500006 ()
    Note
    ©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Mirsad Čirkić, Daniel Persson and Erik G. Larsson, Allocation of Computational Resources for Soft MIMO Detection, 2011, accepted IEEE Journal of Selected Topics in Signal Processing Available from: 2011-07-06 Created: 2011-07-06 Last updated: 2016-08-31
    2. Approximating the LLR Distribution for a Class of Soft-Output MIMO Detectors
    Open this publication in new window or tab >>Approximating the LLR Distribution for a Class of Soft-Output MIMO Detectors
    2012 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 12, p. 6421-6434Article in journal (Refereed) Published
    Abstract [en]

    We present approximations of the LLR distribution for a class of fixed-complexity soft-output MIMO detectors, such as the optimal soft detector and the soft-output via partial marginalization detector. More specifically, in a MIMO AWGN setting, we approximate the LLR distribution conditioned on the transmitted signal and the channel matrix with a Gaussian mixture model (GMM). Our main results consist of an analytical expression of the GMM model (including the number of modes and their corresponding parameters) and a proof that, in the limit of high SNR, this LLR distribution converges in probability towards a unique Gaussian distribution.

    Place, publisher, year, edition, pages
    Institute of Electrical and Electronics Engineers (IEEE), 2012
    Keywords
    Fixed-complexity sphere-decoder; Gaussian mixture model; LLR distribution; MIMO detection; partial marginalization
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-87205 (URN)10.1109/TSP.2012.2217336 (DOI)000311805000024 ()
    Note

    On the defence date of the Licentiate Thesis the status of this article was Manuscript and the title was Approximating the LLR Distribution for the Optimal and Partial Marginalization MIMO Detectors.

    Available from: 2013-01-14 Created: 2013-01-14 Last updated: 2017-12-06Bibliographically approved
    3. SUMIS: Near-Optimal Soft-In Soft-Out MIMO Detection with Low and Fixed Complexity
    Open this publication in new window or tab >>SUMIS: Near-Optimal Soft-In Soft-Out MIMO Detection with Low and Fixed Complexity
    2014 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 12, p. 3084-3097Article in journal (Refereed) Published
    Abstract [en]

    The fundamental problem of interest here is soft-input soft-output multiple-input multiple-output (MIMO) detection. We propose a method, referred to as subspace marginalization with interference suppression (SUMIS), that yields unprecedented performance at low and fixed (deterministic) complexity. Our method provides a well-defined tradeoff between computational complexity and performance. Apart from an initial sorting step consisting of selecting channel-matrix columns, the algorithm involves no searching nor algorithmic branching; hence the algorithm has a completely predictable run-time and allows for a highly parallel implementation. We numerically assess the performance of SUMIS in different practical settings: full/partial channel state information, sequential/iterative decoding, and low/high rate outer codes. We also comment on how the SUMIS method performs in systems with a large number of transmit antennas.

    Place, publisher, year, edition, pages
    IEEE Signal Processing Society, 2014
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:liu:diva-103671 (URN)10.1109/TSP.2014.2303945 (DOI)000338122400005 ()
    Available from: 2014-01-22 Created: 2014-01-22 Last updated: 2017-12-06
    4. On the Complexity of Very Large Multi-User MIMO Detection
    Open this publication in new window or tab >>On the Complexity of Very Large Multi-User MIMO Detection
    2014 (English)In: 2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), IEEE, IEEE Press, 2014, p. 55-59Conference paper, Published paper (Refereed)
    Abstract [en]

    This work discusses efficient techniques for detection in large-size multi-user multiple-input multiple-output (MIMO) systems that are highly overdetermined. We exemplify the application of conjugate gradient methods in the setup of our interest and compare its performance with respect to methods based on the Neumann series expansion. We bring to light some important insights on the performance versus complexity tradeoffs that have not been uplifted before.

    Place, publisher, year, edition, pages
    IEEE Press, 2014
    Series
    IEEE International Workshop on Signal Processing Advances in Wireless Communications, ISSN 2325-3789
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:liu:diva-103672 (URN)10.1109/SPAWC.2014.6941316 (DOI)000348859000012 ()978-1-4799-4903-8 (ISBN)
    Conference
    IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
    Available from: 2014-01-22 Created: 2014-01-22 Last updated: 2016-09-13Bibliographically approved
  • 617.
    Čirkić, Mirsad
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Optimization of Computational Resources for MIMO Detection2011Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    For the past decades, the demand in transferring large amounts of data rapidly and reliably has been increasing drastically. One of the more promising techniques that can provide the desired performance is the multiple-input multiple-output (MIMO) technology where multiple antennas are placed at both the transmitting and receiving side of the communication link. One major implementation difficulty of the MIMO technology is the signal separation (detection) problem at the receiving side of the MIMO link. This is due to the fact that the transmitted signals interfere with each other and that separating them can be very difficult if the MIMO channel conditions are not beneficial, i.e., the channel is not well-conditioned.

    For well-conditioned channels, low-complexity detection methods are often sufficiently accurate. In such cases, performing computationally very expensive optimal detection would be a waste of computational power. This said, for MIMO detection in a coded system, there is always a trade-off between performance and complexity. The fundamental question is, can we save computational resources by performing optimal detection only when it is needed, and something simpler when it is not? This is the question that this thesis aims to answer. In doing so, we present a general framework for adaptively allocating computational resources to different (“simple” and“difficult”) detection problems. This general framework is applicable to any MIMO detector and scenario of choice, and it is exemplified using one particular detection method for which specific allocation techniques are developed and evaluated.

    List of papers
    1. Allocation of Computational Resources for Soft MIMO Detection
    Open this publication in new window or tab >>Allocation of Computational Resources for Soft MIMO Detection
    2011 (English)In: IEEE Journal of Selected Topics in Signal Processing, ISSN 1932-4553, Vol. 5, no 8, p. 1451-1461Article in journal (Refereed) Published
    Abstract [en]

    We consider soft MIMO detection for the case of block fading. That is, the transmitted codeword spans over several independent channel realizations and several instances of the detection problem must be solved for each such realization. We develop methods that adaptively allocate computational resources to the detection problems of each channel realization, under a total per-codeword complexity constraint. Our main results are a formulation of the problem as a mathematical optimization problem with a well-defined objective function and constraints, and algorithms that solve this optimization problem efficiently computationally.

    Place, publisher, year, edition, pages
    IEEE conference proceedings, 2011
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:liu:diva-69612 (URN)10.1109/JSTSP.2011.2162719 (DOI)000297348500006 ()
    Note
    ©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Mirsad Čirkić, Daniel Persson and Erik G. Larsson, Allocation of Computational Resources for Soft MIMO Detection, 2011, accepted IEEE Journal of Selected Topics in Signal Processing Available from: 2011-07-06 Created: 2011-07-06 Last updated: 2016-08-31
    2. Approximating the LLR Distribution for a Class of Soft-Output MIMO Detectors
    Open this publication in new window or tab >>Approximating the LLR Distribution for a Class of Soft-Output MIMO Detectors
    2012 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 12, p. 6421-6434Article in journal (Refereed) Published
    Abstract [en]

    We present approximations of the LLR distribution for a class of fixed-complexity soft-output MIMO detectors, such as the optimal soft detector and the soft-output via partial marginalization detector. More specifically, in a MIMO AWGN setting, we approximate the LLR distribution conditioned on the transmitted signal and the channel matrix with a Gaussian mixture model (GMM). Our main results consist of an analytical expression of the GMM model (including the number of modes and their corresponding parameters) and a proof that, in the limit of high SNR, this LLR distribution converges in probability towards a unique Gaussian distribution.

    Place, publisher, year, edition, pages
    Institute of Electrical and Electronics Engineers (IEEE), 2012
    Keywords
    Fixed-complexity sphere-decoder; Gaussian mixture model; LLR distribution; MIMO detection; partial marginalization
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-87205 (URN)10.1109/TSP.2012.2217336 (DOI)000311805000024 ()
    Note

    On the defence date of the Licentiate Thesis the status of this article was Manuscript and the title was Approximating the LLR Distribution for the Optimal and Partial Marginalization MIMO Detectors.

    Available from: 2013-01-14 Created: 2013-01-14 Last updated: 2017-12-06Bibliographically approved
  • 618.
    Čirkić, Mirsad
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Near-Optimal Soft-Output Fixed-Complexity MIMO Detection via Subspace Marginalization and Interference Suppression2012In: 2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE Signal Processing Society, 2012, , p. 4p. 2805-2808Conference paper (Refereed)
    Abstract [en]

    The fundamental problem of our interest here is soft MIMO detection. We propose a method that yields excellent performance, atlow and at fixed (deterministic) complexity. Our method provides a well-defined tradeoff between computational complexity and performance. Apart from an initial step consisting of selecting columns,the algorithm involves no searching nor algorithmic branching; hence the algorithm has a completely predictable run-time, and it is readily and massively parallelizable.

  • 619.
    Čirkić, Mirsad
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    On the Complexity of Very Large Multi-User MIMO Detection2014In: 2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), IEEE, IEEE Press, 2014, p. 55-59Conference paper (Refereed)
    Abstract [en]

    This work discusses efficient techniques for detection in large-size multi-user multiple-input multiple-output (MIMO) systems that are highly overdetermined. We exemplify the application of conjugate gradient methods in the setup of our interest and compare its performance with respect to methods based on the Neumann series expansion. We bring to light some important insights on the performance versus complexity tradeoffs that have not been uplifted before.

  • 620.
    Čirkić, Mirsad
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    SUMIS: Near-Optimal Soft-In Soft-Out MIMO Detection with Low and Fixed Complexity2014In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 12, p. 3084-3097Article in journal (Refereed)
    Abstract [en]

    The fundamental problem of interest here is soft-input soft-output multiple-input multiple-output (MIMO) detection. We propose a method, referred to as subspace marginalization with interference suppression (SUMIS), that yields unprecedented performance at low and fixed (deterministic) complexity. Our method provides a well-defined tradeoff between computational complexity and performance. Apart from an initial sorting step consisting of selecting channel-matrix columns, the algorithm involves no searching nor algorithmic branching; hence the algorithm has a completely predictable run-time and allows for a highly parallel implementation. We numerically assess the performance of SUMIS in different practical settings: full/partial channel state information, sequential/iterative decoding, and low/high rate outer codes. We also comment on how the SUMIS method performs in systems with a large number of transmit antennas.

  • 621.
    Čirkić, Mirsad
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Persson, Daniel
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Allocation of Computational Resources for Soft MIMO Detection2011In: IEEE Journal of Selected Topics in Signal Processing, ISSN 1932-4553, Vol. 5, no 8, p. 1451-1461Article in journal (Refereed)
    Abstract [en]

    We consider soft MIMO detection for the case of block fading. That is, the transmitted codeword spans over several independent channel realizations and several instances of the detection problem must be solved for each such realization. We develop methods that adaptively allocate computational resources to the detection problems of each channel realization, under a total per-codeword complexity constraint. Our main results are a formulation of the problem as a mathematical optimization problem with a well-defined objective function and constraints, and algorithms that solve this optimization problem efficiently computationally.

  • 622.
    Čirkić, Mirsad
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Persson, Daniel
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    New Results on Adaptive Computational Resource Allocation in Soft MIMO Detection2011In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE conference proceedings, 2011, p. 2972-2975Conference paper (Refereed)
    Abstract [en]

    The fundamental problem of our interest is soft MIMO detection for  the case of block fading, i.e., when the transmitted codeword spans  over several independent channel realizations. We develop methods  that adaptively allocate computational resources to the detection  problems of each channel realization, under a total per-codeword  complexity constraint. The new results consist of a new algorithm, a  new performance measure, and a thorough complexity discussion.

  • 623.
    Čirkić, Mirsad
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Persson, Daniel
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Optimization of Computational Resource Allocation for Soft MIMO Detection2009In: Proceedings of the 43rd Asilomar Conference on Signals, Systems, and Computers (ACSSC'09), IEEE , 2009, p. 1488-1492Conference paper (Refereed)
    Abstract [en]

    We consider soft MIMO detection for the case of block fading. That is, the transmitted codeword spans over several independent channel realizations and several instances of the detection problem must be solved for each such realization. We develop methods that adaptively allocate the computational resources to the detection problems of each channel realization, under a total per-codeword complexity constraint. Our main results are a formulation of the problem as a mathematical optimization problem and a greedy algorithm to approximate it in a computationally feasible fashion.

  • 624.
    Čirkić, Mirsad
    et al.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Persson, Daniel
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Erik G.
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    Larsson, Jan-Åke
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology.
    Gaussian Approximation of the LLR Distribution for the ML and Partial Marginalization MIMO detectors2011In: Proceedings of the IEEE International Conference on Acoustics, Speech and SignalProcessing (ICASSP), IEEE conference proceedings, 2011, p. 3232-3235Conference paper (Refereed)
    Abstract [en]

    We derive a Gaussian approximation of the LLR distribution  conditioned on the transmitted signal and the channel matrix for the  soft-output via partial marginalization MIMO detector. This detector  performs exact ML as a special case. Our main results consist of  discussing the operational meaning of this approximation and a proof  that, in the limit of high SNR, the LLR distribution of interest  converges in probability towards a Gaussian distribution.

10111213 601 - 624 of 624
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