In this paper, the spectral efficiency of network assisted full-duplex communications (NAFD) in cell-free (CF) massive multiple-input multiple-output (MIMO) network with imperfect channel state information is investigated under spatial correlated channels. Based on large dimensional random matrix theory, the deterministic equivalents for the uplink sum-rate with minimum-mean-square-error receiver as well as the downlink sum-rate with zero-forcing and regularized zero-forcing beamforming are presented. Numerical results show that under various environmental settings, the deterministic equivalents are accurate in both a large-scale system and system with a finite number of antennas. It is also shown that with the downlink-to-uplink interference cancellation, the uplink spectral efficiency of CF massive MIMO with NAFD could be improved. The spectral efficiencies of NAFD with different duplex configurations such as in-band full-duplex, and half-duplex are compared. With the same total numbers of transmit and receive antennas, NAFD with half-duplex remote antenna units offers a higher spectral efficiency. To alleviate the uplink-to-downlink interference, a novel genetic algorithm based user scheduling strategy (GAS) is proposed. Simulation results show that the achievable downlink sum-rate by using the GAS is greatly improved compared to that by using the random user scheduling.

In this paper, the downlink spectral efficiency of multicell multiuser distributed massive MIMO systems with linear beamforming is studied in the presence of pilot contamination. According to the levels of effective channel gain information at user side, we provide the lower bound and upper bound on user ergodic achievable downlink rate. Due to the different access distance from each user to different remote antenna units, the entries of user channel vectors are no longer identically distributed in distributed massive MIMO systems, which makes the spectral efficiency analysis challenging. Using the properties of Gamma distributions together with the approximate methods for nonisotropic vectors, we derive tractable, but accurate closed-form expressions for the rate bounds with maximum ratio transmission (MRT) and zero-forcing (ZF) beamforming in distributed massive MIMO systems. Based on these expressions, user ultimate achievable rates are also given when the ratio of the total number of transmit antennas to the number of users goes to infinity. It is shown that MRT and ZF beamforming achieve the same ultimate rate no matter what levels of effective channel gain information at user side. Numerical results show that ZF achieves better performance gain and faster convergence speed than MRT. When the coherence interval is large, the downlink beamforming training scheme is more preferable for the distributed massive MIMO systems.

The fifth generation (5G) communication system aims to support diverse services such as enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC). In order to enable the coexistence of eMBB and URLLC services on a common physical infrastructure, network slicing incorporated in the distributed massive multiple-input multiple-output (DM-MIMO) system is regarded as a promising solution. In this paper, a non-orthogonal slicing scheme is proposed to improve the spectral efficiency, and the short packet transmission is adopted for URLLC to meet the low latency requirement. Through a joint optimization of beamforming and remote radio unit (RRU) selection, we investigate the energy efficiency (EE) maximization problem for the downlink DM-MIMO system. The formulated problem is a mixed integer nonlinear program (MINLP) that we solve by applying some efficient approaches, such as the Dinkelbach method, the reweighted $\ell _{1}$ -norm and difference of convex (DC) programming. By combining these approaches in one iteration, a double-loop procedure based on semi-definite relaxation (SDR) and successive convex approximation (SCA) is employed to find the suboptimal solution of the problem. Simulation results show that the proposed scheme significantly improves the EE of the DM-MIMO system and efficiently manages resource allocation.

This letter proposes a deep learning based pilot design scheme to minimize the sum mean square error (MSE) of channel estimation for multi-user distributed massive multiple-input multiple-output (MIMO) systems. The pilot signal of each user is expressed as a weighted superposition of orthonormal pilot sequence basis, where the power assigned to each pilot sequence is the corresponding weight. A multi-layer fully connected deep neural network (DNN) is designed to optimize the power allocated to each pilot sequence to minimize the sum MSE, which takes the channel large-scale fading coefficients as input and outputs the pilot power allocation vector. The loss function of the DNN is defined as the sum MSE, and we leverage the unsupervised learning strategy to train the DNN. Simulation results show that the proposed scheme achieves better sum MSE performance than other methods with low complexity.

In this paper, we investigate the problem of sum rate maximization by guaranteeing the quality of service (QoS) of both uplink and downlink in cell-free with network-assisted full Duplexing (NAFD), where the users operate in half-duplex mode and access points (APs) operate in either full-duplex mode or half-duplex mode. In the considered network, the central processor unit (CPU) sends the compressed beamformed signals to the transmit-APs (T-APs) over the downlink fronthaul, and the T-APs forward the signals to downlink users. At the same time, the receive-APs (R-APs) compress the signals transmitted by uplink users and forward them to the CPU via uplink fronthaul. We aim to maximize the spectral efficiency and the number of users that should be admitted by the network, where users’ requirements of both downlink and uplink signal-to-interference-plus-noise (SINR) constraints, fronthaul capacity constraints, energy harvesting constraints and simultaneous wireless information and power transfer (SWIPT) ratio design are considered. A successive convex approximation-based algorithm is proposed to solve the highly coupled problem, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) conditions. We conduct a comprehensive comparison between the NAFD scheme and the traditional co-frequency co-time full duplex (CCFD) scheme and time division duplex (TDD) scheme and offer some valuable opinions about the system design.

Low latency, high reliability, and high data rate are three performance indicators of Ultra-reliable and low latency communication (URLLC) systems, but they are not able to be met simultaneously. Based on the determined latency and reliability requirements, this paper investigates the problem of the weighted sum rate (WSR) in cell-free massive multi-input multi-output (MIMO) system with multiple access points (APs). User-centric access point selection is considered to further reduce the computational complexity for AP used with acceptable performance loss. The transmitting beamforming is designed to mitigate inter-user interference so as to maximize the WSR under the backhaul and transmitting power constraints. Since the proposed problem is non-convex, two algorithms are proposed to solve the problem under the general SINR regime and the high SINR regime, respectively. For the former, we first approximate the objective function with the difference of convex (DC) programming and reformulate the problem into a semi-definite relaxation (SDR) form. Afterward, auxiliary variables are introduced in accordance with the successive convex approximation (SCA) method to further transform the nonconvex problem into a convex one. In the end, the sub-optimal WSR is obtained by iteratively solving a final convex problem. For the latter, the well-known WMMSE method is applied to address the WSR problem. Numerical results demonstrate the efficiency of our proposed algorithm, e.g., the maximum performance gain of the proposed algorithm is about 25% compared to the classical MMSE method. And the user-centric approach achieves 8 times computational complexity reduction with 4% performance loss.