Joint Multicast Beamforming Research Articles

MagicNet: The Maritime Giant Cellular Network

Recently, the development of marine industries has increasingly attracted attention from all over the world. A wide-area and seamless maritime communication network has become a critical supporting approach. In this article, we propose a novel architecture named the maritime giant cellular network (MagicNet) relying on seaborne floating towers deployed in a honeycomb topology. The tower-borne giant-cell base stations are capable of providing wide-area seamless coverage for maritime users and can construct multihop line-of-sight (LoS) links connecting to terrestrial networks. Then the MagicNet-aided maritime network architecture is expounded in terms of five dimensions (i.e., space, air, shore, surface, and underwater), which is compatible with existing systems including maritime satellite networks and maritime Internet of Things, and supports a range of compelling industrial applications. Moreover, we introduce a joint multicast beamforming and relay system for the sake of supporting high-speed and low-cost information services for near-shore areas, as well as a three-tier space-air-surface hybrid network in order to provide reliable wide-area communications for deep offshore areas. Finally, we discuss the open issues and future works on MagicNet.

Fast Algorithms for Joint Multicast Beamforming and Antenna Selection in Massive MIMO

Massive MIMO is currently a leading physical layer technology candidate that can dramatically enhance throughput in 5G systems, for both unicast and multicast transmission modalities. As antenna elements are becoming smaller and cheaper in the mmW range compared to radio frequency (RF) chains, it is crucial to perform antenna selection at the transmitter, such that the available RF chains are switched to an appropriate subset of antennas. This paper considers the joint problem of multicast beamforming and antenna selection for a single multicast group in massive MIMO systems. The prior state-of-art for this problem relies on semi-definite relaxation (SDR), which cannot scale up to the massive MIMO regime. A successive convex approximation (SCA) based approach is proposed to tackle max-min fair joint multicast beamforming and antenna selection. The key idea of SCA is to successively approximate the non-convex problem by a class of non-smooth, convex optimization problems. Two fast and memory efficient first-order methods are proposed to solve each SCA subproblem. Simulations demonstrate that the proposed algorithms outperform the existing state-of-art approach in terms of solution quality and run time, in both traditional and especially in massive MIMO settings.

Joint Multicast Beamforming and Relay Design for Maritime Communication Systems

With growing human maritime activities, supporting low-cost and high-speed information services for users at sea has become imperative. However, traditional means of maritime communications fail to provide high rate services due to their high cost and limited bandwidth. In this paper, considering a base station ashore and several offshore relay nodes, we propose a cooperative multicast communication scheme for maritime users relying on joint beamforming (BF) optimization and relay design. Specifically, we decompose our proposed joint optimization problem into two subproblems, which can be solved by the feasible point pursuit successive convex approximation approach. Furthermore, an alternating optimization algorithm is proposed, which imposes an exponentially increasing complexity as a function of the number of BF elements and the number of relays, when aiming for finding the globally optimal solution. In order to reduce this excessive computational complexity, a low-complexity distributed algorithm is also conceived and its closed-form solution is derived. Finally, the simulation results provided show that our proposed algorithm is beneficial in terms of increasing both the throughput as well as the energy efficiency.

Joint multicast beamforming and user scheduling in large‐scale antenna systems

This paper studies the joint multicast beamforming and user scheduling problem, with the objective of minimizing total transmitting power across multiple channels by jointly assigning each user to appropriate channel and designing multicast beamformer for each channel. The problem of interest is formulated in two different optimization problems, a mixed binary quadratically constrained quadratic program and a highly-structured nonsmooth program. Two different algorithms, based on convex relaxation and convex restriction, respectively, are proposed to solve the problem. The performance ratio between the approximate solution provided by the convex-relaxation-based algorithm and optimal solution is proved to be upper bounded by a constant independent of problem data. The convex-restriction-based algorithm is guaranteed to converge to a critical point to the nonsmooth formulation problem. Finally, extensive simulation results verify the theoretical analysis and demonstrate the advantage of the proposed co-design scheme over conventional fixed scheduling and random scheduling in terms of power consumption.

Joint Multicast Beamforming and Antenna Selection

Multicast beamforming exploits subscriber channel state information at the base station to steer the transmission power towards the subscribers, while minimizing interference to other users and systems. Such functionality has been provisioned in the long-term evolution (LTE) enhanced multimedia broadcast multicast service (EMBMS). As antennas become smaller and cheaper relative to up-conversion chains, transmit antenna selection at the base station becomes increasingly appealing in this context. This paper addresses the problem of joint multicast beamforming and antenna selection for multiple co-channel multicast groups. Whereas this problem (and even plain multicast beamforming) is NP-hard, it is shown that the mixed l1,∞-norm squared is a prudent group-sparsity inducing convex regularization, in that it naturally yields a suitable semidefinite relaxation, which is further shown to be the Lagrange bi-dual of the original NP-hard problem. Careful simulations indicate that the proposed algorithm significantly reduces the number of antennas required to meet prescribed service levels, at relatively small excess transmission power. Furthermore, its performance is close to that attained by exhaustive search, at far lower complexity. Extensions to max-min-fair, robust, and capacity-achieving designs are also considered.

Efficient Batch and Adaptive Approximation Algorithms for Joint Multicast Beamforming and Admission Control

Wireless multicasting is becoming increasingly important for efficient distribution of streaming media and location-aware services to mobile and hand-held devices, network management, and software updates over cellular (UMTS-LTE) and indoor/outdoor wireless networks (e.g., 802.11/16). Multicast beamforming was recently proposed as a means of exploiting the broadcast nature of the wireless medium to boost spectral efficiency and meet Quality of Service (QoS) requirements. Infeasibility is a key issue in this context, due to power or mutual interference limitations. We therefore consider the joint multicast beamforming and admission control problem for one or more co-channel multicast groups, with the objective of maximizing the number of subscribers served and minimizing the power required to serve them. The problem is NP-hard even for an isolated multicast group and no admission control; but drawing upon our earlier work for the multiuser SDMA downlink, we develop an efficient approximation algorithm that yields good solutions at affordable worst-case complexity. For the special case of an isolated multicast, Lozano proposed a particularly simple adaptive algorithm for implementation in UMTS-LTE. We identify strengths and drawbacks of Lozano's algorithm, and propose two simple but worthwhile improvements. All algorithms are carefully tested on publicly available indoor/outdoor measured channel data.