In this era of rapid wireless technological advancements, wireless connectivity between humans, humans with machines, and machines with machines is gradually becoming an absolute necessity. The initial motivation for wireless connectivity was to enable voice communication between humans over a geo-graphical area. Thanks to cellular communications advancements in the past decade, cellular wireless connectivity has become a global success, starting from 1G to the present generation 5G. However, the needs of humans often evolve with time, and now the world is witnessing an ever-growing demand for the internet with high data rates besides reliable voice communication. Current cellular networks suffer from non-uniform data rates across a cell, i.e., users at the cell center and the cell edges experience significant variations in signal-to-noise ratio, making the cellular technology less reliable to meet the future data demands. Moreover, cellular networks operating as cells, i.e., an access point (AP, the term we would use instead of base station) serving the users within its geographical location, cannot leverage the network’s total capacity without cooperation among APs of the neighboring cells. One potential solution is moving away from the cell to cell-free networks wherein all the APs will serve all the users within the geographical coverage area. Thus, there is a need for a paradigm shift in how cellular networks operate. Towards the goal mentioned above to fully leverage the network capacity, the Cell-Free Massive multiple-input-multiple-output (MIMO) technology is expected to be the next potential technology beyond 5G combining the benefits of Massive MIMO and cell-free distributed architectures. 

Distributed architectures require distributed signal processing algorithms, and also energy consumption of the network is crucial. Keeping in view the practical ease in deployment, we consider a sequentially connected Cell-Free Massive MIMO network called a “radio stripe”. In the first part of the thesis, we focus on developing an optimal sequential algorithm in the sense of mean-square-error (MSE) which has the same performance as that of centralized Cell-Free Massive MIMO implementation with the minimum MSE (MMSE) receiver. We also develop an optimal sequential algorithm that decentralizes the centralized bit LLR computation. Another attractive aspect of these proposed algorithms is that the fronthaul (number of real symbols required by the central processing unit (CPU) to decode the transmitted signal) is independent of the number of APs. On the contrary, centralized implementation fronthaul is dependent on the number of APs, causing scalability problems with the increase in APs. 

In the second part of the thesis, we develop an algorithm focused on maximizing the energy efficiency of the RadioWeave network in an underlay spectrum sharing. RadioWeave is a technology envisioned to combine Cell-Free Massive MIMO and possibly large intelligent surfaces. We first present the energy efficiency problem, which is non-convex in its original form. Then, a convex lower bound on the problem is provided with an iterative algorithm to solve the problem efficiently.  

RadioWeaves network operates a large number ofdistributed antennas using cell-free architecture to provide highdata rates and support a large number of users. Operating thisnetwork in an energy-efficient manner in the limited availablespectrum is crucial. Therefore, we consider energy efficiency(EE) maximization of a RadioWeaves network that shares spectrumwith a collocated primary network in underlay mode.To simplify the problem, we lower bound the non-convex EEobjective function to form a convex problem. We then propose adownlink power allocation policy that maximizes the EE of thesecondary RadioWeaves network subject to power constraint ateach access point and interference constraint at each primaryuser. Our numerical results investigate the secondary system’sperformance in interference, power, and EE constrained regimeswith correlated fading channels. Furthermore, they show that theproposed power allocation scheme performs significantly betterthan the simpler equal power allocation scheme.

RadioWeaves, in which distributed antennas with integrated radio and compute resources serve a large number of users, is envisioned to provide high data rates in next-generation wireless systems. In this paper, we develop a physical layer abstraction model to evaluate the performance of different RadioWeaves deployment scenarios. This model helps speed up system-level simulators of the RadioWeaves and is made up of two blocks. The first block generates a vector of signalto-interference-plus-noise ratios (SINRs) corresponding to each coherence block, and the second block predicts the packet error rate corresponding to the SINRs generated. The vector of SINRs generated depends on different parameters such as the number of users, user locations, antenna configurations, and precoders. We have also considered different antenna gain patterns, such as omni-directional and directional microstrip patch antennas. Our model exploits the benefits of exponential effective SINR mapping (EESM). We study the robustness and accuracy of the EESM for RadioWeaves.

This paper presents a novel strategy to decentralize the soft detection procedure in an uplink cell-free massive multiple-input-multiple-output network. We propose efficient approaches to compute the a posteriori probability-per-bit, exactly or approximately, when having a sequential fronthaul. More precisely, each access point (AP) in the network computes partial sufficient statistics locally, fuses it with received partial statistics from another AP, and then forward the result to the next AP. Once the sufficient statistics reach the central processing unit, it performs the soft demodulation by computing the log-likelihood ratio (LLR) per bit, and then a channel decoding algorithm (e.g., a Turbo decoder) is utilized to decode the bits. We derive the distributed computation of LLR analytically.

Cell-free massive multiple-input-multiple-output (mMIMO) is an emerging technology for beyond 5G with its promising features such as higher spectral efficiency and superior spatial diversity as compared to conventional multiple-input-multiple-output (MIMO) technology. The main working principle of cell-free mMIMO is that many distributed access points (APs) cooperate simultaneously to serve all the users within the network without creating cell boundaries. This paper considers the uplink of a cell-free mMIMO system utilizing the radio stripe network architecture with a sequential fronthaul between the APs. A novel uplink sequential processing algorithm is developed, which is proved to be optimal in both the maximum spectral efficiency (SE) and the minimum mean square error (MSE) sense. A detailed quantitative analysis of the fronthaul requirement or signaling of the proposed algorithm and its comparison with competing sub-optimal algorithms is provided. Key conclusions and implications are summarized in the form of corollaries. Based on the analytical and numerical simulation results, we conclude that the proposed scheme can significantly reduce the fronthaul signaling, without compromising the communication performance.

Cell-free Massive MIMO (mMIMO) is envisaged to be a next-generation technology beyond 5G with its high spectral efficiency and superior spatial diversity as compared to that of conventional MIMO technology. The main principle is that many distributed access points (APs) cooperate to simultaneously serve all the users within the network without creating cell boundaries. This paper considers the uplink of a cell-free mMIMO system utilizing the radio stripe network architecture. We propose a novel sequential processing algorithm with normalized linear minimum mean square error (N-LMMSE) combining at every AP. This algorithm enables interference suppression in cell-free mMIMO while keeping the cost and front-haul requirements low. The spectral efficiency of the proposed algorithm is computed and analyzed. We conclude that it provides an attractive trade-off between low front-haul requirements and high spectral efficiency.