We study opinion dynamics in a social network with stubborn agents who influence their neighbors but who themselves always stick to their initial opinion. We consider first the well-known DeGroot model. While it is known in the literature that this model can lead to consensus even in the presence of a stubborn agent, we show that the same result holds under weaker assumptions than has been previously reported. We then consider a recent extension of the DeGroot model in which the opinion of each agent is a random Bernoulli distributed variable, and by leveraging on the first result we establish that this model also leads to consensus, in the sense of convergence in probability, in the presence of a stubborn agent. Moreover, all agents opinions converge to that of the stubborn agent.

We study an opinion dynamics model in which each agent takes a random Bernoulli distributed action whose probability is updated at each discrete time step, and we prove that this model converges almost surely to consensus. We also provide a detailed critique of a claimed proof of this result in the literature. We generalize the result by proving that the assumption of irreducibility in the original model is not necessary. Furthermore, we prove as a corollary of the generalized result that the almost sure convergence to consensus holds also in the presence of a stubborn agent which never changes its opinion. In addition, we show that the model, in both the original and generalized cases, converges to consensus also in $r$th mean.

We discuss possible definitions of structural balance conditions in a network with preference orderings as node attributes. The main result is that for the case with three alternatives (A, B, C) we reduce the (3!)(3) = 216 possible configurations of triangles to 10 equivalence classes, and use these as measures of balance of a triangle towards possible extensions of structural balance theory. Moreover, we derive a general formula for the number of equivalent classes for preferences on n alternatives. Finally, we analyze a real-world data set and compare its empirical distribution of triangle equivalence classes to a null hypothesis in which preferences are randomly assigned to the nodes.

We address the problem of removing specular ground surface reflections and leakage/cross-talk from downward looking stepped frequency ground-penetrating radar (GPR) data. A new model for the ground-bounce and the leakage/cross-talk is introduced. An algorithm that jointly estimates these effects from collected data is presented. The algorithm has the sound foundation of a nonlinear least squares (LS) fit to the presented model. The minimization is performed in a cyclic manner where one step is a linear LS minimization and the other step is a non-linear LS minimization where the optimum can efficiently be found using, e.g., the chirp-transform algorithm. The results after applying the algorithm to measured GPR data, collected at a US army test range, are also shown

In this paper, we investigate downlink power control in massive multiple-input multiple-output (MIMO) networks with distributed antenna arrays. The base station (BS) in each cell consists of multiple antenna arrays, which are deployed in arbitrary locations within the cell. Due to the spatial separation between antenna arrays, the large-scale propagation effect is different from a user to different antenna arrays in a cell, which makes power control a challenging problem as compared to conventional massive MIMO. We assume that the BS in each cell obtains the channel estimates via uplink pilots. Based on the channel estimates, the BSs perform maximum ratio transmission for the downlink. We then derive a closed-form spectral efficiency (SE) expression, where the channels are subject to correlated fading. Utilizing the derived expression, we propose a max-min power control algorithm to ensure that each user in the network receives a uniform quality of service. Numerical results demonstrate that, for the network considered in this work, optimizing for max-min SE through the max-min power control improves the sum SE of the network as compared to the equal power allocation.

In this paper, we investigate optimal downlink power allocation in massive multiple-input multiple-output (MIMO) networks with distributed antenna arrays (DAAs) under correlated and uncorrelated channel fading. In DAA massive MIMO, a base station (BS) consists of multiple antenna sub-arrays. Notably, the antenna sub-arrays are deployed in arbitrary locations within a DAA massive MIMO cell. Consequently, the distance-dependent large-scale propagation coefficients are different from a user to these different antenna sub-arrays, which makes power control a challenging problem. We assume that the network operates in time-division duplex mode, where each BS obtains the channel estimates via uplink pilots. Based on the channel estimates, the BSs perform maximum-ratio transmission in the downlink. We then derive a closed-form signal-to-interference-plus-noise ratio (SINR) expression, where the channels are subject to correlated fading. Based on the SINR expression, we propose a network-wide max-min power control algorithm to ensure that each user in the network receives a uniform quality of service. Numerical results demonstrate the performance advantages offered by DAA massive MIMO. For some specific scenarios, DAA massive MIMO can improve the average per-user throughput up to 55%. Furthermore, we demonstrate that channel fading covariance is an important factor in determining the performance of DAA massive MIMO.

The achievable rates are investigated for multicell multi-user massive multiple-input multiple-output (MIMO) systems with underlay spectrum sharing. A general pilot sharing scheme and two pilot sequence designs (PSDs) are investigated via fully shared (PSD-1) and partially shared (PSD-2) uplink pilots. The number of simultaneously served primary users and secondary users (SUs) in the same time-frequency resource block by the PSD-1 is higher than that of PSD-2. The transmit power constraints for the SUs are derived to mitigate the secondary co-channel interference (CCI) inflicted at the primary base-station (PBS) subject to a predefined primary interference temperature (PIT). The optimal transmit power control coefficients for the SUs with max-min fairness and the common achievable rates are derived. The cumulative detrimental effects of channel estimation errors, CCI and intra-cell/inter-cell pilot contamination are investigated. The secondary transmit power constraint and the achievable rates for the perfect channel state information (CSI) case become independent of the PIT when the number of PBS antennas grows unbounded. Therefore, the primary and secondary systems can be operated independent of each other as both intra-cell and inter-cell interference can be asymptotically mitigated at the massive MIMO PBS and secondary base-station. Nevertheless, the achievable rates and secondary power constraints for the imperfect CSI case with PSD-1 are severely degraded due to the presence of intra-cell and inter-cell pilot contamination. These performance metrics depend on the PIT even in the asymptotic PBS antenna regime. Hence, the primary and secondary systems can no longer be operated independently for imperfect CSI with PSD-1. However, PSD-2 provides an achievable rate gain over PSD-1 despite the requirement of lengthier pilot sequences of the former than that of the latter.

A method for the computationally efficient sliding window time-updating of the Capon and APES spectral estimators based on the time-variant displacement structure of the data covariance matrix is presented. The proposed algorithm forms a natural extension of the computationally most efficient algorithm to date, and offers a significant computationalgain as compared to the computational complexity associated with the batch re-evaluation of the spectral estimates for each time-update.

In this paper, we present a computationally efficient sliding window time updating of the Capon and amplitude and phase estimation (APES) matched filterbank spectral estimators based on the time-variant displacement structure of the data covariance matrix. The presented algorithm forms a natural extension of the most computationally efficient algorithm to date, and offers a significant computational gain as compared to the computational complexity associated with the batch re-evaluation of the spectral estimates for each time-update. Furthermore, through simulations, the algorithm is found to be numerically superior to the time-updated spectral estimate formed from directly updating the data covariance matrix.

Simultaneous wireless information and power transfer techniques for multiway massive multiple-input multiple-output (MIMO) relay networks are investigated. By using two practically viable relay receiver designs, namely 1) the power splitting receiver and 2) the time switching receiver, asymptotic signal-to-interference-plus-noise ratio (SINR) expressions are derived for an unlimited number of antennas at the relay. These asymptotic SINRs are then used to derive asymptotic symmetric sum rate expressions in closed form. Notably, these asymptotic SINRs and sum rates become independent of radio frequency-to-direct current (RF-to-DC) conversion efficiency in the limit of infinitely many relay antennas. Moreover, tight average sum rate approximations are derived in closed form for finitely many relay antennas. The fundamental tradeoff between the harvested energy and the sum rate is quantified for both relay receiver structures. Notably, the detrimental impact of imperfect channel state information (CSI) on the MIMO detector/precoder is investigated, and thereby, the performance degradation caused by pilot contamination, which is the residual interference due to nonorthogonal pilot sequence usage in adjacent/cochannel systems, is quantified. The presence of cochannel interference (CCI) can be exploited to be beneficial for energy harvesting at the relay, and consequently, the asymptotic harvested energy is an increasing function of the number of cochannel interferers. Notably, in the genie-aided perfect CSI case, the detrimental impact of CCI for signal decoding can be cancelled completely whenever the number of relay antennas grows without bound. Nevertheless, the pilot contamination severely degrades the sum rate performance even for infinitely many relay antennas.

We consider a cell-free massive multiple-input multiple-output (CFmMIMO) network operating in dynamic time division duplex (DTDD). The switching point between the uplink (UL) and downlink (DL) data transmission phases can be adapted dynamically to the instantaneous quality-of-service (QoS) requirements in order to improve energy efficiency (EE). To this end, we formulate a problem of optimizing the DTDD switching point jointly with the UL and DL power control coefficients, and the large-scale fading decoding (LSFD) weights for EE maximization. Then, we propose an iterative algorithm to solve the formulated challenging problem using successive convex approximation with an approximate stationary solution. Simulation results show that optimizing switching points remarkably improves EE compared with baseline schemes that adjust switching points heuristically.

We propose a novel method for user-to-user interference (UUI) mitigation in dynamic time-division duplex multiple-input multiple-output communication systems with multi-antenna users. Specifically, we consider the downlink data transmission in the presence of UUI caused by a user that simultaneously transmits in uplink. Our method introduces an overhead for estimation of the user-to-user channels by transmitting pilots from the uplink user to the downlink users. Each downlink user obtains a channel estimate that is used to design a combining matrix for UUI mitigation. We analytically derive an achievable spectral efficiency for the downlink transmission in the presence of UUI with our mitigation technique. Through numerical simulations, we show that our method can significantly improve the spectral efficiency performance in cases of heavy UUI.

Large-scale massive MIMO network deployments can provide higher spectral efficiency and better coverage for future communication systems like 5G. Due to the large number of antennas at the base station, the system achieves stable channel quality and spatially separable channels to the different users. In this paper, linear, planar, circular and cylindrical arrays are used in the evaluation of a large-scale multi-cell massive MIMO network. The system-level performance is predicted using two different kinds of channel models. First, a ray-based deterministic tool is utilized in a real North American city environment. Second, an independent and identically distributed (i.i.d.) Rayleigh fading channel model is considered, as often used in previously published massive MIMO studies. The analysis is conducted in a 16-macro-cell network with outdoor and randomly distributed users. It is shown that the array configuration has a large impact on the throughput statistics. Although the system level performance with i.i.d. Rayleigh fading can be close to the deterministic prediction in some situations (e.g., with large linear arrays), significant differences are noticed when considering other types of arrays.

Massive MIMO network deployments are expected to be a key feature of the upcoming 5G communication systems. Such networks are able to achieve a high level of channel quality and can simultaneously serve multiple users with the same resources. In this paper, realistic massive MIMO channels are evaluated both in single and multi-cell environments. The favorable propagation property is evaluated in the single-cell scenario and provides perspectives on the minimal criteria required to achieve such conditions. The dense multi-cell urban scenario provides a comparison between linear, planar, circular, and cylindrical arrays to evaluate a large-scale multi-cell massive MIMO network. The system-level performance is predicted using two different kinds of channel models. First, a ray-based deterministic tool is utilized in a real North American city environment. Second, an independent and identically distributed (i.i.d.) Rayleigh fading channel model is considered, as often used in previously published massive MIMO studies. The analysis is conducted in a 16-macro-cell network with both randomly distributed outdoor and indoor users. It is shown that the physical array properties like the shape and configuration have a large impact on the throughput statistics. Although the system-level performance with i.i.d. Rayleigh fading can be close to the deterministic prediction in some situations (e.g., with large linear arrays), significant differences are noticed when considering other types of arrays. The differences in the performance of the various arrays utilizing the exact same network parameters and the same number of total antenna elements provide insights into the selection of these physical parameters for upcoming 5G networks.

In this paper we deal with the problem of the joint optimization of the precoders, equalizers and relay beamformer of a multiple-input multiple-output interfering relay channel. This network can be regarded az a generalized model for both one-way and two-way relay channels with/without direct interfering links. Unlike the conventional design procedures, we assume that the Channel State Information (CSI) is not known perfectly. The imperfect CSI is described using the norm bounded error framework. We use a system-wide Sum Mean Square Error (SMSE) based problem formulation which is constrained using the transmit power of the terminals and the relay node. The problem at hand, from a worst-case design perspective, is a multilinear, and hence, a nonconvex problem which is also semiinfinite in its constraints. We use a generalized version of the Peterson’s lemma to handle the semi-infiniteness and reduce the original problem to a single Linear Matrix Inequality (LMI). However, this LMI is not convex, and to resolve this issue we propose an iterative algorithm based on the alternating convex search methodology to solve the aforementioned problem. Finally simulation results, i.e., the convergence of the proposed algorithm and the SMSE properties, are included to asses the performance of the proposed algorithm.

In this paper, we deal with the problem of joint optimization of the source precoder, the relay beamformer and the destination equalizer in a nonregenerative relay network with only a partial knowledge of the Channel State Information (CSI).

We model the partial CSI using a deterministic norm bounded error model, and we use a system-wide mean square error performance measure which is constrained based on the transmit power regulations for both source and relay nodes.

Most conventional designs employ the average performance optimization, however, we solve this problem from a worst-case design perspective.

The original problem formulation is a semi-infinite trilinear optimization problem which is not convex.

To solve this problem we extend the existing theories to deal with the constraints which are semi-infinite in different independent complex matrix variables.

We show that the equivalent approximate problem is a set of linear matrix inequalities, that can be solved iteratively.

Finally simulation results assess the performance of the proposed scheme.

We formally generalize the sign-definiteness lemma to the case of complex-valued matrices and multiple norm-bounded uncertainties. This lemma has found many applications in the study of the stability of control systems, and in the design and optimization of robust transceivers in communications. We then present three different novel applications of this lemma in the area of multi-user multiple-input multiple-output (MIMO) robust transceiver optimization. Specifically, the scenarios of interest are: (i) robust linear beamforming in an interfering adhoc network, (ii) robust design of a general relay network, including the two-way relay channel as a special case, and (iii) a half-duplex one-way relay system with multiple relays. For these networks, we formulate the design problems of minimizing the (sum) MSE of the symbol detection subject to different average power budget constraints. We show that these design problems are non-convex (with bilinear or trilinear constraints) and semiinfinite in multiple independent uncertainty matrix-valued variables. We propose a two-stage solution where in the first step the semi-infinite constraints are converted to linear matrix inequalities using the generalized signdefiniteness lemma, and in the second step, we use an iterative algorithm based on alternating convex search (ACS). Via simulations we evaluate the performance of the proposed scheme.

This paper deals with the problem of discriminating samples that contain only noise from samples that contain a signal embedded in noise. The focus is on the case when the variance of the noise is unknown. We derive the optimal soft decision detector using a Bayesian approach. The complexity of this optimal detector grows exponentially with the number of observations and as a remedy, we propose a number of approximations to it. The problem under study is a fundamental one and it has applications in signal denoising, anomaly detection, and spectrum sensing for cognitive radio. We illustrate the results in the context of the latter.

In this paper, we create a unified framework for spectrum sensing of signals which have covariance matrices with known eigenvalue multiplicities. We derive the generalized likelihood-ratio test (GLRT) for this problem, with arbitrary eigenvalue multiplicities under both hypotheses. We also show a number of applications to spectrum sensing for cognitive radio and show that the GLRT for these applications, of which some are already known, are special cases of the general result.

We point out an error in a derivation in the recent paper [1], and provide a correct and much shorter calculation of the result in question. In passing, we also connect the results in [1] to the literature on array signal processing and on principal component analysis, and show that the main findings of [1] follow as special cases of standard results in these fields.

We consider spectrum sensing of signals encoded with an orthogonal space-time block code (OSTBC). We propose a CFAR detector based on knowledge of the eigenvalue multiplicities of the covariance matrix which are inherent owing to the OSTBC and derive theoretical performance bounds. In addition, we show that the proposed detector is robust to a carrier frequency offset, and propose a detector that deals with timing synchronization using the detector for the synchronized case as a building block. The proposed detectors are shown numerically to perform well.

We consider spectrum sensing of a second-order cyclostationary signal receivedat multiple antennas. The proposed detector exploits both the spatial andthe temporal correlation of the received signal, from knowledge of thefundamental period of the cyclostationary signal and the eigenvaluemultiplicities of the temporal covariance matrix. All other parameters, suchas the channel gains or the noise power, are assumed to be unknown. The proposeddetector is shown numerically to outperform state-of-the-art detectors forspectrum sensing of anOFDM signal, both when using a single antenna and with multiple antennas.

We consider spectrum sensing of OFDM signals in an AWGN channel. For the case of completely unknown noise and signal powers, we  derive a GLRT detector based on empirical second-order statistics of  the received data. The proposed GLRT detector exploits the  non-stationary correlation structure of the OFDM signal and does not  require any knowledge of the noise power or the signal power. The  GLRT detector is compared to state-of-the-art OFDM signal detectors,  and shown to improve the detection performance with 5 dB SNR in  relevant cases.

For the case of completely known noise power and signal power, we present a brief  derivation of the optimal Neyman-Pearson detector from first  principles. We compare the optimal detector to the energy  detector numerically, and show that the energy detector is  near-optimal (within 0.2 dB SNR) when the noise variance is  known. Thus, when the noise power is known, no substantial gain can  be achieved by using any other detector than the energy detector.

We consider spectrum sensing of OFDM signals in an AWGN channel. For  the case of completely known noise and signal powers, we set up  a vector-matrix model for an OFDM signal with a cyclic prefix and  derive the optimal Neyman-Pearson detector from first  principles. The optimal detector exploits the inherent correlation  of the OFDM signal incurred by the repetition of data in the cyclic  prefix, using knowledge of the length of the cyclic prefix and the  length of the OFDM symbol. We compare the optimal detector to the energy  detector numerically. We show that the energy detector is  near-optimal (within 1 dB SNR) when the noise variance is  known. Thus, when the noise power is known, no substantial gain can  be achieved by using any other detector than the energy detector.

For the case of completely unknown noise and signal powers, we  derive a generalized likelihood ratio test (GLRT) based onempirical second-order statistics of  the received data. The proposed GLRT detector exploits the  non-stationary correlation structure of the OFDM signal and does not  require any knowledge of the noise power or the signal power. The  GLRT detector is compared to state-of-the-art OFDM signal detectors,  and shown to improve the detection performance with 5 dB SNR in  relevant cases.

We consider detection of signals encoded with orthogonal space-time block codes (OSTBC), using multiple receive antennas. Such signals contain redundancy and they have a specific structure, that can be exploited for detection. We derive the optimal detector, in the Neyman-Pearson sense, when all parameters are known. We also consider unknown noise variance, signal variance and channel coefficients. We propose a number of GLRT based detectors for the different cases, that exploit the redundancy structure of the OSTBC signal. We also propose an eigenvalue-based detector for the case when all parameters are unknown. The proposed detectors are compared to the energy detector. We show that when only the noise variance is known, there is no gain in exploiting the structure of the OSTBC. However, when the noise variance is unknown there can be a significant gain.

In this work, we consider spectrum sensing of Gaussian signals with structured covariance matrices. We show that the optimal detector based on the probability distribution of the sample covariance matrix is equivalent to the optimal detector based on the raw data, if the covariance matrices are known. However, the covariance matrices are unknown in general. Therefore, we propose to estimate the unknown parameters using covariance matching estimation techniques (COMET). We also derive the optimal detector based on a Gaussian approximation of the sample covariance matrix, and show that this is closely connected to COMET.

Cognitive radio is a new concept of reusing a licensed spectrum in an unlicensed manner. The motivation for cognitive radio is various measurements of spectrum utilization, that generally show unused resources in frequency, time and space. These "spectrum holes" could be exploited by cognitive radios. Some studies suggest that the spectrum is extremely underutilized, and that these spectrum holes could provide ten times the capacity of all existing wireless devices together. The spectrum could be reused either during time periods where the primary system is not active, or in geographical positions where the primary system is not operating. In this paper, we deal primarily with the concept of geographical reuse, in a frequency-planned primary network. We perform an analysis of the potential for communication in a geographical spectrum hole, and in particular the achievable sum-rate for a secondary network, to some order of magnitude. Simulation results show that a substantial sum-rate could be achieved if the secondary users communicate over small distances. For a small number of secondary links, the sum-rate increases linearly with the number of links. However, the spectrum hole gets saturated quite fast, due to interference caused by the secondary users. A spectrum hole may look large, but it disappears as soon as someone starts using it.

This paper considers approximations of marginalization sums thatarise in Bayesian inference problems. Optimal approximations ofsuch marginalization sums, using a fixed number of terms, are analyzedfor a simple model. The model under study is motivated byrecent studies of linear regression problems with sparse parametervectors, and of the problem of discriminating signal-plus-noise samplesfrom noise-only samples. It is shown that for the model understudy, if only one term is retained in the marginalization sum, thenthis term should be the one with the largest a posteriori probability.By contrast, if more than one (but not all) terms are to be retained,then these should generally not be the ones corresponding tothe components with largest a posteriori probabilities.

In this paper we deal with spoofing detection in GNSS receivers. We derive the optimal genie detector when the true positions are perfectly known, and the observation errors are Gaussian, as a benchmark for other detectors. The system model considers three dimensional positions, and includes correlated errors. In addition, we propose several detectors that do not need any position knowledge, that outperform recently proposed detectors in many interesting cases.

We present a survey of state-of-the-art algorithms for spectrum  sensing in cognitive radio. The algorithms discussed range from  energy detection to sophisticated feature detectors. The feature  detectors that we present all have in common that they exploit some  known structure of the transmitted signal.  In particular we treat  detectors that exploit cyclostationarity properties of the signal,  and detectors that exploit a known eigenvalue structure of the  signal covariance matrix.  We also consider cooperative  detection. Specifically we present data fusion rules for soft and  hard combining, and discuss the energy efficiency of several  different sensing, sleeping and censoring schemes in detail.

The ever-increasing demand for higher data rates in wireless communications in the face of limited or underutilized spectral resources has motivated the introduction of cognitive radio. Traditionally, licensed spectrum is allocated over relatively long time periods and is intended to be used only by licensees. Various measurements of spectrum utilization have shown substantial unused resources in frequency, time, and space [1], [2]. The concept behind cognitive radio is to exploit these underutilized spectral resources by reusing unused spectrum in an opportunistic manner [3], [4]. The phrase cognitive radio is usually attributed to Mitola [4], but the idea of using learning and sensing machines to probe the radio spectrum was envisioned several decades earlier (cf., [5]).

The computational complexity of optimum decoding for an orthogonal space-time block code is quantified. Four equivalent techniques of optimum decoding which have the same computational complexity are specified. Modifications to the basic formulation in special cases are calculated and illustrated by means of examples.

The computational complexity of optimum decoding for an orthogonal space-time block code {cal G}_N satisfying {cal G}_N^H{cal G}_N=c(∑_{k=1}^Kos_ko^2)I_N where c is a positive integer is quantified. Four equivalent techniques of optimum decoding which have the same computational complexity are specified. Modifications to the basic formulation in special cases are calculated and illustrated by means of examples. This paper corrects and extends and unifies them with the results from the literature. In addition, a number of results from the literature are extended to the case c>1.

Grant-free multiple access (GFMA) mitigates the uplink handshake overhead to support low-latency communication by transmitting payload data together with the pilot (preamble). However, the channel capacity with random access is limited by the number of available orthogonal pilots and the incoordination among devices. We consider a delay-constrained GFMA system, where each device with randomly generated data traffic needs to deliver its data packets before some pre-determined deadline. The pilot selection problem is formulated to minimize the average packet drop rate of the worst user. A priority-sorting based centralized policy is derived by introducing a fairness promoting function. For decentralization, we propose a multi-agent policy optimization algorithm with improved sample efficiency by exploring the model structure. Simulation results show that our proposed scheme facilitates near-optimal coordination between devices by using only partial state information.

We develop a new algorithm for activity detection for grant-free multiple access in distributed multiple-input multiple-output (MIMO). The algorithm is a distributed version of the approximate message passing (AMP) based on a soft combination of likelihood ratios computed independently at multiple access points. The underpinning theoretical basis of our algorithm is a new observation that we made about the state evolution in the AMP. Specifically, with a minimum mean-square error denoiser, the state maintains a block-diagonal structure whenever the covariance matrices of the signals have such a structure. We show by numerical examples that the algorithm outperforms competing schemes from the literature.

The wireless channel is undergoing continuous changes, and the block-fading assumption, despite its popularity in theoretical contexts, never holds true in practical scenarios. This discrepancy is particularly critical for user activity detection in grant-free random access, where joint processing across multiple resource blocks is usually undesirable. In this paper, we propose employing a low-dimensional approximation of the channel to capture variations over time and frequency and robustify activity detection algorithms. This approximation entails projecting channel fading vectors onto their principal directions to minimize the approximation order. Through numerical examples, we demonstrate a substantial performance improvement achieved by the resulting activity detection algorithm.

Massive MIMO is considered to be one of the key technologies in the emerging 5G systems, but also a concept applicable to other wireless systems. Exploiting the large number of degrees of freedom (DoFs) of massive MIMO is essential for achieving high spectral efficiency, high data rates and extreme spatial multiplexing of densely distributed users. On the one hand, the benefits of applying massive MIMO for broadband communication are well known and there has been a large body of research on designing communication schemes to support high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT) is still a developing topic, as IoT connectivity has requirements and constraints that are significantly different from the broadband connections. In this paper we investigate the applicability of massive MIMO to IoT connectivity. Specifically, we treat the two generic types of IoT connections envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC). This paper fills this important gap by identifying the opportunities and challenges in exploiting massive MIMO for IoT connectivity. We provide insights into the trade-offs that emerge when massive MIMO is applied to mMTC or URLLC and present a number of suitable communication schemes. The discussion continues to the questions of network slicing of the wireless resources and the use of massive MIMO to simultaneously support IoT connections with very heterogeneous requirements. The main conclusion is that massive MIMO can bring benefits to the scenarios with IoT connectivity, but it requires tight integration of the physical-layer techniques with the protocol design. (C) 2019 Elsevier B.V. All rights reserved.

Deep learning (DL) is becoming popular as a new tool for many applications in wireless communication systems. However, for many classification tasks (e.g., modulation classification) it has been shown that DL-based wireless systems are susceptible to adversarial examples; adversarial examples are well-crafted malicious inputs to the neural network (NN) with the objective to cause erroneous outputs. In this paper, we extend this to regression problems and show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network. Specifically, we extend the fast gradient sign method (FGSM), momentum iterative FGSM, and projected gradient descent adversarial attacks in the context of power allocation in a maMIMO system. We benchmark the performance of these attacks and show that with a small perturbation in the input of the NN, the white-box attacks can result in infeasible solutions up to 86%. Furthermore, we investigate the performance of black-box attacks. All the evaluations conducted in this work are based on an open dataset and NN models, which are publicly available.

A cell-free Massive multiple-input multiple-output (MIMO) uplink is considered, where the access points (APs) are connected to a central processing unit (CPU) through limited-capacity wireless microwave links. The quantized version of the weighted signals are available at the CPU, by exploiting the Bussgang decomposition to model the effect of quantization. A closed-form expression for spectral efficiency is derived taking into account the effects of channel estimation error and quantization distortion. The energy efficiency maximization problem is considered with per-user power, backhaul capacity and throughput requirement constraints. To solve this non-convex problem, we decouple the original problem into two sub-problems, namely, receiver filter coefficient design, and power allocation. The receiver filter coefficient design is formulated as a generalized eigenvalue problem whereas a successive convex approximation (SCA) and a heuristic sub-optimal scheme are exploited to convert the power allocation problem into a standard geometric programming (GP) problem. An iterative algorithm is proposed to alternately solve each sub-problem. Complexity analysis and convergence of the proposed schemes are investigated. Numerical results indicate the superiority of the proposed algorithms over the case of equal power allocation. 

We investigate the energy efficiency performance of cell-free Massive multiple-input multiple-output (MIMO), where the access points (APs) are connected to a central processing unit (CPU) via limited-capacity links. Thanks to the distributed maximum ratio combining (MRC) weighting at the APs, we propose that only the quantized version of the weighted signals are sent back to the CPU. Considering the effects of channel estimation errors and using the Bussgang theorem to model the quantization errors, an energy efficiency maximization problem is formulated with per-user power and backhaul capacity constraints as well as with throughput requirement constraints. To handle this non-convex optimization problem, we decompose the original problem into two sub-problems and exploit a successive convex approximation (SCA) to solve original energy efficiency maximization problem. Numerical results confirm the superiority of the proposed optimization scheme.

This paper investigates the performance of limited-fronthaul cell-free massive multiple-input multiple-output (MIMO) taking account the fronthaul quantization and imperfect channel acquisition. Three cases are studied, which we refer to as Estimate&Quantize, Quantize&Estimate, and Decentralized, according to where channel estimation is performed and exploited. Maximum-ratio combining (MRC), zero-forcing (ZF), and minimum mean-square error (MMSE) receivers are considered. The Max algorithm and the Bussgang decomposition are exploited to model optimum uniform quantization. Exploiting the optimal step size of the quantizer, analytical expressions for spectral and energy efficiencies are presented. Finally, an access point (AP) assignment algorithm is proposed to improve the performance of the decentralized scheme. Numerical results investigate the performance gap between limited fronthaul and perfect fronthaul cases, and demonstrate that exploiting relatively few quantization bits, the performance of limited-fronthaul cell-free massive MIMO closely approaches the perfect-fronthaul performance.

Limited-backhaul cell-free Massive multiple-input multiple-output (MIMO), in which the fog radio access network (F-RAN) is implemented to exchange the information between access points (APs) and the central processing unit (CPU), is investigated. We introduce a novel approach where the APs estimate the channel and send back the quantized version of the estimated channel and the quantized version of the received signal to the central processing unit. The Max algorithm and the Bussgang theorem are exploited to model the optimum uniform quantization. The ergodic achievable rates are derived. We show that exploiting microwave wireless backhaul links and using a small number of hits to quantize the estimated channel and the received signal, the performance of limited-backhaul cell-free Massive MIMO closely approaches the performance of cell-free Massive MIMO with perfect backhaul links.

Many real-world scenarios for massive machine-type communication involve sensors monitoring a physical phenomenon. As a consequence, the activity pattern of these sensors will be correlated. In this letter, we study how the correlation of user activities can be exploited to improve detection performance in grant-free random access systems where the users transmit pilot-hopping sequences and the detection is performed based on the received energy. We show that we can expect considerable performance gains by adding regularizers, which take the activity correlation into account, to the non-negative least squares, which has been shown to work well for independent user activity.

Reciprocity-based time-division duplex (TDD) Massive MIMO (multiple-input multiple-output) systems utilize channel estimates obtained in the uplink to perform precoding in the downlink. However, this method has been criticized of breaking down, in the sense that the channel estimates are not good enough to spatially separate multiple user terminals, at low uplink reference signal signal-to-noise ratios, due to insufficient channel estimation quality. Instead, codebook-based downlink precoding has been advocated for as an alternative solution in order to bypass this problem. We analyze this problem by considering a “grid-of-beams world” with a finite number of possible downlink channel realizations. Assuming that the terminal accurately can detect the downlink channel, we show that in the case where reciprocity holds, carefully designing a mapping between the downlink channel and the uplink reference signals will perform better than both the conventional TDD Massive MIMO and frequency-division duplex (FDD) Massive MIMO approach. We derive elegant metrics for designing this mapping, and further, we propose algorithms that find good sequence mappings.

In this paper, we study an active user detection problem for massive machine type communications (mMTC). The users transmit pilot-hopping sequences and detection of active users is performed based on the received energy. We utilize the channel hardening and favorable propagation properties of massive multiple- input multipleoutput (MIMO) to simplify the user detection. We propose and compare a number of different user detection methods and find that using non- negative least squares (NNLS) is well suited for the task at hand as it achieves good results as well as having the benefit of not having to specify further parameters.

In this paper we study the benefits that Internet-of-Things (IoT) devices will have from connecting to a massive multiple-input-multiple-output (MIMO) base station. In particular, we study how many users that could be simultaneously spatially multiplexed and how much the range can be increased by deploying massive base station arrays. We also investigate how the devices can scale down their uplink power as the number of antennas grows with retained rates. We consider the uplink and utilize upper and lower bounds on known achievable rate expressions to study the effects of the massive arrays. We conduct a case study where we use simulations in the settings of existing IoT systems to draw realistic conclusions. We find that the gains which ultra narrowband systems get from utilizing massive MIMO are limited by the bandwidth and therefore those systems will not be able to spatially multiplex any significant number of users. We also conclude that the power scaling is highly dependent on the nominal signal-to-noise ratio (SNR) in the single-antenna case.

In this paper, we propose a method of improving the channel estimates for non-coherent multi-antenna terminals, which are terminals that cannot control the relative phase between its antenna ports, with channels that can be considered constant over multiple time slots. The terminals have multiple antennas and are free to choose whichever antenna they want to use in each time slot. An unknown phase shift is introduced in each time slot as we cannot guarantee that the terminals are phase coherent across time slots. We compare three different clustering techniques that we use to detect the active antenna. We also compare a set of different statistical and heuristic estimators for the channels and the phase shifts. We evaluate the methods by using correlated Rayleigh fading and three different bounds on the uplink capacity. The accuracy of the capacity bounds are verified with bit-error-rate simulations. With our proposed methods we can have an SNR improvement of approximately 2 dB at 1 bit/s/Hz.

In this paper we propose a method of improving channel estimates for non-coherent terminals with channels that can be considered constant over multiple time slots. The terminals have multiple antennas and are free to choose whichever antenna they want to use in each time slot. An unknown phase shift is introduced in each time slot as we cannot guarantee that the terminals are phase coherent across time slots. The proposed methods of improving channel estimates are a combination of clustering and heuristic methods. With our proposed methods we can have an improvement of 1.5 dB at 1 bit/s/Hz.

A potential showstopper for reciprocity-based beamforming is that the uplink SNR often is much smaller than the downlink SNR, making it hard to estimate channels on the uplink. We analyze this problem by considering a "grid-of-beams world" with a finite number of possible channel realizations. We assume that the terminal can accurately detect the channel and we propose a method of improving the channel detection from uplink pilots by designing a mapping between the channel and the pilots. We find a simple metric that is to be minimized to maximize performance. Further, we propose an algorithm that draws pilot sequences from a distribution aimed to minimize the metric. We see that we can come close to optimal performance, which requires long sequences, with significantly shorter sequences.

We provide the optimal receive combining strategy for federated learning in multiple-input multiple-output (MIMO) systems. Our proposed algorithm allows the clients to perform individual gradient sparsification which greatly improves performance in scenarios with heterogeneous (non i.i.d.) training data. The proposed method beats the benchmark by a wide margin.