Self-Optimized Precoding and Power Control in Cellular Networks

International audienceIn this paper, we propose an autonomous radio resource allocation and optimization scheme that chooses the transmit power and precoding vector among codebooks for multiple antennas transmitters to improve spectral and power efficiency and provide user fairness. Network self-optimization is an essential feature for supporting the cell densification in future wireless cellular systems. The proposed self-optimization is inspired by Gibbs sampler. We show that it can be implemented in a distributed manner and nevertheless achieves system-wide optimization which improves network throughput, power utilization efficiency, and overall service fairness. In addition, we extend the work and include power pricing to parametrize and enhance energy efficiency further. Simulation results show that the proposed scheme can outperform today's default modes of operation in network throughput, energy efficiency, and user fairness

Inter-tier Interference Suppression in Heterogeneous Cloud Radio Access Networks

Incorporating cloud computing into heterogeneous networks, the heterogeneous cloud radio access network (H-CRAN) has been proposed as a promising paradigm to enhance both spectral and energy efficiencies. Developing interference suppression strategies is critical for suppressing the inter-tier interference between remote radio heads (RRHs) and a macro base station (MBS) in H-CRANs. In this paper, inter-tier interference suppression techniques are considered in the contexts of collaborative processing and cooperative radio resource allocation (CRRA). In particular, interference collaboration (IC) and beamforming (BF) are proposed to suppress the inter-tier interference, and their corresponding performance is evaluated. Closed-form expressions for the overall outage probabilities, system capacities, and average bit error rates under these two schemes are derived. Furthermore, IC and BF based CRRA optimization models are presented to maximize the RRH-accessed users' sum rates via power allocation, which is solved with convex optimization. Simulation results demonstrate that the derived expressions for these performance metrics for IC and BF are accurate; and the relative performance between IC and BF schemes depends on system parameters, such as the number of antennas at the MBS, the number of RRHs, and the target signal-to-interference-plus-noise ratio threshold. Furthermore, it is seen that the sum rates of IC and BF schemes increase almost linearly with the transmit power threshold under the proposed CRRA optimization solution