Distributed Optimization for Coordinated Beamforming in Multicell Multigroup Multicast Systems: Power Minimization and SINR Balancing

Abstract

Abstract-This paper considers coordinated multicast beamforming in a multi-cell multigroup multiple-input single-output system. Each base station (BS) serves multiple groups of users by forming a single beam with common information per group. We propose centralized and distributed beamforming algorithms for two different optimization targets. The first objective is to minimize the total transmission power of all the BSs while guaranteeing the user-specific minimum quality-of-service targets. The semidefinite relaxation (SDR) method is used to approximate the non-convex multicast problem as a semidefinite program (SDP), which is solvable via centralized processing. Subsequently, two alternative distributed methods are proposed. The first approach turns the SDP into a two-level optimization via primal decomposition. At the higher level, inter-cell interference powers are optimized for fixed beamformers while the lower level locally optimizes the beamformers by minimizing BS-specific transmit powers for the given inter-cell interference constraints. The second distributed solution is enabled via an alternating direction method of multipliers, where the inter-cell interference optimization is divided into a local and a global optimization by forcing the equality via consistency constraints. We further propose a centralized and a simple distributed beamforming design for the signal-to-interference-plus-noise ratio (SINR) balancing problem in which the minimum SINR among the users is maximized with given per-BS power constraints. This problem is solved via the bisection method as a series of SDP feasibility problems. The simulation results show the superiority of the proposed coordinated beamforming algorithms over traditional non-coordinated transmission schemes, and illustrate the fast convergence of the distributed methods.

A Survey of Optimization Approaches for Wireless Physical Layer Security

“…• Distributed antenna systems: In [236], the beamforming and AN vectors are jointly optimized to minimize the total transmission power while providing QoS for reliable communication and efficient power transfer in a given time slot, in which the capacity-limited backhaul links is taken into account. • Multi-cell multigroup multicast systems: In [237], two different optimization targets are considered for a multicell multigroup MISO system, i.e., power minimization and SINR balancing. The centralized and distributed beamforming algorithms are proposed for the considered optimization problems, based on the techniques of SDR and alternating optimization.…”

Energy-Efficient Multicell Multigroup Multicasting With Joint Beamforming and Antenna Selection

“…Beamforming design for multicasting has been studied for single-cell systems for different optimization targets, e.g., transmit power minimization [12], [13], [19], max-min fairness [13], [14], [19], and sum rate maximization [15]. Joint beamforming and antenna selection for transmit power minimization was studied in [9].…”

“…A large part of the requested data traffic from users is highly correlated, especially in crowded areas, e.g., in stadiums. To deal with such situations, multicasting has received special attention as a promising solution [9]- [19]. The idea is to transmit the same information to multiple users as a single transmission, and it has become increasingly popular in the context of cache-enabled cloud radio access networks (C-RANs) proposed for 5G systems to improve both spectral and energy efficiency [20].…”

Non-Orthogonal Unicast and Broadcast Transmission via Joint Beamforming and LDM in Cellular Networks

“…A dual decomposition-based scheme has been proposed in [25] by creating consensus over inter-cell interference terms between all the BSs. In [26], a primal decomposition-based algorithm and an alternating direction method of multipliers (ADMM)-based algorithm have been proposed for the SDR version of the original problem. In [27], instead of directly dealing with the relaxed problem, the authors proposed to apply ADMM for each of the convexified SCA problems, obtaining a doule-loop scheme.…”