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International audience; This paper provides a theoretical framework for the study of full-duplex (FD) massive multiple-input multiple-output (MIMO) cellular networks over Rician self-interference (SI) and Rayleigh intended and other-interference fading channels. To facilitate bi-directional wireless functionality, we incorporate (i) a downlink (DL) linear zero-forcing with self-interference-nulling (ZF-SIN) precoding scheme at the FD base stations (BSs), and (ii) an uplink (UL) self-interference-aware (SIA) fractional power control mechanism at the FD user equipments (UEs). Linear ZF receivers are further utilized for signal detection in the UL. The results indicate that the UL rate bottleneck in the baseline FD single-antenna system can be elevated by several hundred times via exploiting massive MIMO. On the other hand, the findings may be viewed as a reality-check as the largest spectral efficiency gain from the FD massive MIMO cellular network over its half-duplex (HD) counterpart under state-of-the-art system parameters is shown to be in the region of ~40%.

This paper presents a stochastic geometry-based framework for the design and analysis of downlink multi-user multiple-input multiple-output (MIMO) heterogeneous cellular networks (HetNets) with linear zero-forcing (ZF) transmit precoding and receive combining, assuming Rayleigh fading channels and perfect channel state information (CSI). The generalized tiers of base stations (BSs) may differ in terms of their Poisson point process (PPP) spatial density, number of transmit antennas, transmit power, artificial-biasing weight, and number of user equipments (UEs) served per resource block. The spectral efficiency of a typical user equipped with multiple receive antennas is characterized using a non-direct moment-generating-function (MGF)-based methodology with closed-form expressions of the useful received signal and aggregate network interference statistics systematically derived. In addition, the area spectral efficiency is formulated under different space-division multiple-access (SDMA) and single-user beamforming (SUBF) transmission schemes. We examine the impact of different cellular network deployments, propagation conditions, antenna configurations, and MIMO setups on the achievable performance through theoretical and simulation studies. Based on state-of-the-art system parameters, the results highlight the inherent limitations of baseline single-input single-output (SISO) transmission and conventional sparse macro-cell deployment, as well as the promising potential of multi-antenna communications and small-cell solution in interference-limited cellular environments.

In this paper, power allocation over time-varying multiple-access interference channels is studied. Particularly, stochastic network sum-rate maximizing and max-min rate power allocation, and total power minimization problems are formulated, capturing the random nature of communication channels. Typically, a centralized controller must have perfect knowledge of global instantaneous channel state information for dynamic optimal power allocation; however, this may not be possible, because of the computational complexity and communication overheads/delays involved. Based on the second-order statistics of the channel state information, the stochastic problem formulations are transformed into their optimal deterministic representations in terms of ergodic capacity, while ensuring satisfactory quality of service via target outage probability. However, such deterministic reformulations happen to be non-convex and thus are computationally expensive. In turn, sub-optimal reformulations are derived and solved via iterative low-complexity algorithms. Simulation results demonstrate that the proposed deterministic sub-optimal power allocation reformulations closely coincide with their optimal deterministic and dynamic counterparts, with the proposed algorithms converging in a finite number of iterations.