Physical layer multicasting is an efficient transmission technique that exploits the beamforming potential at the transmitting nodes and the broadcast nature of the wireless channel, together with the demand for the same content from several UEs. This paper addresses the max-min fair multigroup multicast beamforming optimization, which is an NP-hard problem. We propose a novel iterative elimination procedure coupled with semidefinite relaxation (SDR) to find the near-global optimum rank-1 beamforming vectors in a cell-free massive MIMO (multiple-input multiple-output) network setup. The proposed optimization procedure shows significant improvements in computational complexity and spectral efficiency performance compared to the SDR followed by the commonly used randomization procedure and the state-of-the-art difference-of-convex approximation algorithm. The significance of the proposed procedure is that it can be utilized as a rank reduction method for any problem in conjunction with SDR.

Federated learning (FL) is a distributed learning paradigm wherein users exchange FL models with a server instead of raw datasets, thereby preserving data privacy and reducing communication overhead. However, the increased number of FL users may hinder completing large-scale FL over wireless networks due to high imposed latency. Cell-free massive multiple-input multiple-output (CFmMIMO) is a promising architecture for implementing FL because it serves many users on the same time/frequency resources. While CFmMIMO enhances energy efficiency through spatial multiplexing and collaborative beamforming, it remains crucial to meticulously allocate uplink transmission powers to the FL users. In this paper, we propose an uplink power allocation scheme in FL over CFmMIMO by considering the effect of each user's power on the energy and latency of other users to jointly minimize the users' uplink energy and the latency of FL training. The proposed solution algorithm is based on the coordinate gradient descent method. Numerical results show that our proposed method outperforms the well-known max-sum rate by increasing up to 27% and max-min energy efficiency of the Dinkelbach method by increasing up to 21 % in terms of test accuracy while having limited uplink energy and latency budget for FL over CFmMIMO.

Co-channel interference poses a challenge in any wireless communication network where the time-frequency resources are reused over different geographical areas. The interference is particularly diverse in cell-free massive multiple-input multiple-output (MIMO) networks, where a large number of user equipments (UEs) are multiplexed by a multitude of access points (APs) on the same time-frequency resources. For realistic and scalable network operation, only the interference from UEs belonging to the same serving cluster of APs can be estimated in real-time and suppressed by precoding/combining. As a result, the unknown interference arising from scheduling variations in neighboring clusters makes the rate adaptation hard and can lead to outages. This paper aims to model the unknown interference power in the uplink of a cell-free massive MIMO network. The results show that the proposed method effectively describes the distribution of the unknown interference power and provides a tool for rate adaptation with guaranteed target outage.

This paper considers a mmWave cell-free massive MIMO (multiple-input multiple-output) network composed of a large number of geographically distributed access points (APs) simultaneously serving multiple user equipments (UEs) via coherent joint transmission. We address UE mobility in the downlink (DL) with imperfect channel state information (CSI) and pilot training. Aiming at extending traditional handover concepts to the challenging AP-UE association strategies of cell-free networks, distributed algorithms for joint pilot assignment and cluster formation are proposed in a dynamic environment considering UE mobility. The algorithms provide a systematic procedure for initial access and update of the serving APs and assigned pilot sequence to each UE. The principal goal is to limit the necessary number of AP and pilot changes, while limiting computational complexity. We evaluate the performance, in terms of spectral efficiency (SE), with maximum ratio and regularized zero-forcing precoding. Results show that our proposed distributed algorithms effectively identify the essential AP-UE association refinements with orders-of-magnitude lower computational time compared to the state-of-the-art. It also provides a significantly lower average number of pilot changes compared to an ultra-dense network (UDN). Moreover, we develop an improved pilot assignment procedure that facilitates massive access to the network in highly loaded scenarios.

The existence of unknown interference is a prevalent problem in wireless communication networks. Especially in multi-user multiple-input multiple-output (MIMO) networks, where a large number of user equipments are served on the same time-frequency resources, the outage performance may be dominated by the unknown interference arising from scheduling variations in neighboring cells. In this letter, we propose a Bayesian method for modeling the unknown interference power in the uplink of a cellular network. Numerical results show that our method accurately models the distribution of the unknown interference power and can be effectively used for rate adaptation with guaranteed target outage performance.

This paper addresses mobility management in the downlink of a mmWave cell-free massive MIMO (multiple-input multiple-output) network with imperfect channel knowledge obtained from pilot training. The network consists of a large number of geographically distributed access points (APs) simultaneously serving multiple user equipments (UEs) via coherent joint transmission. The objective is to extend traditional handover concepts to the challenging AP-UE association strategies of cell-free networks. To this end, we propose a distributed algorithm for joint pilot assignment and cluster formation in a dynamic environment considering UE mobility. The primary goal is to limit the necessary number of AP and pilot changes, with reasonable computational complexity. We evaluate the performance in terms of the spectral efficiency with maximum ratio and regularized zero-forcing precoding. Results show that our proposed distributed algorithm effectively identifies the essential AP-UE association refinements. Moreover, it provides a significantly lower average number of pilot changes compared to an ultra-dense network.

This paper considers a cell-free massive multiple-input multiple-output (MIMO) system that consists of a large number of geographically distributed access points (APs) serving multiple users via coherent joint transmission. The downlink performance of the system is evaluated, with maximum ratio and regularized zero-forcing precoding, under two optimization objectives for power allocation: sum spectral efficiency (SE) maximization and proportional fairness. We present iterative centralized algorithms for solving these problems. Aiming at a less computationally complex and also distributed scalable solution, we train a deep neural network (DNN) to approximate the same network-wide power allocation. Instead of training our DNN to mimic the actual optimization procedure, we use a heuristic power allocation, based on large-scale fading (LSF) parameters, as the pre-processed input to the DNN. We train the DNN to refine the heuristic scheme, thereby providing higher SE, using only local information at each AP. Another distributed DNN that exploits side information assumed to be available at the central processing unit is designed for improved performance. Further, we develop a clustered DNN model where the LSF parameters of a small number of APs, forming a cluster within a relatively large network, are used to jointly approximate the power coefficients of the cluster.

This paper considers a cell-free massive MIMO (multiple-input multiple-output) system that consists of a large number of geographically distributed access points (APs) simultaneously serving multiple user equipments (UEs) on the same time-frequency resources via coherent joint transmission. The performance of the system is evaluated, with maximum ratio and regularized zero-forcing precoding, in terms of the achievable spectral efficiency (SE) under two optimization objectives for the downlink power allocation problem: sum-SE and proportional fairness. Aiming at a less computationally complex as well as a distributed scalable solution, we train a deep neural network (DNN) to perform approximately the same network-wide power allocation. Instead of training our DNN to mimic the actual optimization procedure, we use a heuristic power allocation based on large-scale fading parameters as the input to the DNN. The heuristic input provides better dynamic range while preserving the ratios among the DNN inputs. This allows the use of a simplified structure for the DNN while achieving higher SEs compared to the heuristic scheme.

This paper investigates interference cancellation in an Uplink Multi-User (MU) Multiple Input Multiple Output (MIMO) system with imperfect Channel State Information (CSI), in which a Full Duplex (FD) Decode and Forward (DF) relay is used. Least Squares (LS) approach is considered to estimate the channels. In order to decode the symbols at the relay, the received MU interference is cancelled by applying Block Diagonalization (BD) based processors. Moreover, an equivalent relay is used to suppress the relay's self-interference signal. Afterwards, Minimum Mean Squared Error (MMSE) technique is adopted for the information extraction at the relay. A similar procedure is applied at the Base Station (BS) for MU interference cancellation and information extraction. Simulation results provide the appropriate number of users, antennas and pilot powers for each setup in order to achieve a relatively high sum rate.

This paper considers a multi-pair two-way full-duplex relaying system with multiple-input-multiple-output (MIMO) users. Each pair of users exchange information with the aid of a massive MIMO decode-and-forward (DF) relay. Low-complexity processing at the relay based on maximum ratio combining/maximum ratio transmission (MRC/MRT) is presented, and the system is evaluated in terms of the achievable sum-spectral efficiency. The direct link between all user nodes is considered non-negligible, which is suitable for urban environments, however, we assume that the users are unaware of the direct link and so is treated as interference. Moreover, we consider correlated antenna arrays at the relay and user nodes and the detrimental effect of spatial correlation is investigated.

In this paper, we consider a multi-pair two-way full-duplex relaying system with multiple-input-multiple-output (MIMO) users. Each pair of users exchange information with the aid of a massive MIMO amplify-and-forward (AF) relay, and correlation between the antennas both at the users as well as the relay is considered. The direct link between all user nodes is assumed to be non-negligible, which is suitable for practical urban scenarios. The low-complexity transceiver design at the relay based on maximum ratio combining/maximum ratio transmission (MRC[MRT) processing is presented. The performance of the system is evaluated in terms of the achievable sum-spectral efficiency under two communication schemes. The first scheme attempts to make use of the joint benefits of the relayed and direct links, while in the second scheme the direct link is considered as interference. Comparison analysis show that the first scheme outperforms the second in the presence of a strong direct link. Moreover, the detrimental effect of spatial correlation between the antennas at each user node is investigated.

In this paper, we propose a model of a full duplex (FD) massive MIMO AF relay assisting D2D and cellular users simultaneously in presence of cross-tier interference and imperfect channel state information (CSI). Zero-forcing reception (ZFR)/zero-forcing transmission (ZFT) processing technique is used in the relay. The asymptotic spectral efficiency (SE) under four power scaling schemes is derived and validated by numerical results. Results show that the loop interference due to the FD nature of the relay can be eliminated by scaling down the transmitted power of the sources and the relay.

This paper addresses interference cancellation in uplink Multi User (MU) Multiple Input Multiple Output (MIMO) system with an Amplify and Forward (AF) Full Duplex (FD) relay. An equivalent relay model is adopted in order to suppress the self-interference. Moreover, Block Diagonalization (BD) is applied to design post-processor filters to null the MU interference, and extract the direct and relayed links signals. Afterwards, we perform Minimum Mean Squared Error(MMSE) combination of the extracted signals. Simulation results show that the proposed scheme increases the achievable sum rate of the system and highlight the significant diversity gain provided by the direct and relayed links.