Sparse beamforming for limited-backhaul network MIMO system via reweighted power minimization

Abstract

This paper considers a downlink multicell cooperation model in which the base-stations (BSs) are connected to a central processor (CP) via rate-limited backhaul links. A user-centric clustering model is adopted where each scheduled user is cooperatively served by a cluster of BSs, and the serving BSs for different users may overlap. This paper formulates an optimal joint clustering and beamforming design problem in which each user dynamically forms a sparse network-wide beamforming vector whose non-zero entries correspond to the serving BSs. Specifically, we assume a fixed signal-to-interference-and-noise ratio (SINR) constraint for each user, and investigate the optimal tradeoff between the sum transmit power and the sum backhaul capacity needed to form the cooperating clusters. Intuitively, larger cooperation size leads to lower transmit power, because interference can be mitigated through cooperation, but it also leads to higher sum backhaul, because user data needs to be made available to more BSs. Motivated by the compressive sensing literature, this paper formulates the sparse beamforming problem as an ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> -norm optimization problem, then uses the iterative reweighted ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> heuristic to find a solution. A key observation of this paper is that the reweighting can be done on the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> -norm square of the beamformers (i.e., the power) at the BSs. This gives rise to a weighted power minimization problem over the entire network, which can be solved using the uplink-downlink duality technique with low computational complexity. This paper further proposes judicious choice of the weights, and shows that the new algorithm can provide a better tradeoff between the sum power and the sum backhaul capacity in the high SINR regime than previous algorithms.