Multiple-input Single-output Downlink Research Articles

Achieving Maximum Weighted Sum-Rate in Multicell Downlink MISO Systems

This paper considers the maximization of the weighted sum-rate (WSR) in multicell downlink multiple-input single-output (MISO) systems. The problem is non-convex and thus cannot be solved directly via conventional convex optimization methods. To achieve global optimality, we propose a novel monotonic optimization approach where a sensible search scheme first checks the feasibility of a chosen point and then utilizes a sequential partition method at each iteration to reduce the total number of feasibility evaluations. Besides, another relocating procedure also accelerates the convergence. Simulation results verify the improved convergence performance of the proposed approach compared with the previously proposed outer polyblock approximation algorithm and the branch-reduce-and-bound algorithm.

Weighted max-min fair beamforming, power control, and scheduling for a MISO downlink

In this paper, we consider the joint beamforming, power control and scheduling problem in a multiple-input single- output (MISO) downlink for maximizing the minimum weighted rate among all users. We solve this problem by formulating it as a convex optimization problem whose function evaluations require maximization of a difference of convex functions (D.C. Programming). We also propose a simpler heuristic that finds the best policy under restricted set of power vectors. We then compare the performance of these policies against the dirty paper coding (DPC) policy to quantify the performance gap between an optimal point-to-point coding approach and an optimal joint encoding/decoding approach.

Margin adaptive optimization in multi-user MISO-OFDM systems under rate constraint

In this paper, we focus on the total transmission power minimization problem for downlink beamforming multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems while ensuring each user's QoS requirement. Although the linear integer programming (LIP) solution we formulate provides the performance upper bound of the margin adaptive (MA) optimization problem, it is hard to be implemented in practice due to its high computational complexity. By regarding each user's equivalent channel gain as approximate independent values and using iterative descent method, we present a heuristic MA resource allocation algorithm. Simulation results show that the proposed algorithm efficiently converges to the local optimum, which is very close to the performance of the optimal LIP solution. Compared with existing space division multiple access (SDMA) OFDM systems with or without adaptive resource allocation, the proposed algorithm achieves significant performance improvement by exploiting the frequency diversity and multi-user diversity in down-link multiple-input single-output (MISO) OFDM systems.