Dynamic cooperation link selection for network MIMO systems with limited backhaul capacity

2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

2014

Self Cite

Abstract-This paper considers the joint beamforming and clustering design problem in a downlink network multiple-input multiple-output (MIMO) setup, where the base-stations (BSs) are connected to a central processor with rate-limited backhaul links. We formulate the problem as that of devising a sparse beamforming vector across the BSs for each user, where the nonzero beamforming entries correspond to that user's serving BSs. Differing from the previous works, this paper explicitly formulates the per-BS backhaul constraints in the network utility maximization framework. In contrast to the traditional utility maximization problem with transmit power constraint only, the additional backhaul constraints result in a discrete 0-norm formulation, which makes the problem more challenging. Motivated by the compressive sensing literature, we propose to iteratively approximate the per-BS backhaul constraints using a reweighted 1-norm technique and reformulate the backhaul constraints as weighted per-BS power constraints. This allows us to solve the weighted sum rate maximization problem through a generalized weighted minimum mean square error (WMMSE) approach. To reduce the computational complexity of the proposed algorithm within each iteration, we propose two additional techniques, iterative link removal and iterative user pool shrinking, which dynamically decrease the potential BS cluster size and user scheduling pool. Numerical results show that the proposed algorithm can significantly improve the system throughput as compared to the naive BS clustering strategy based on the channel strength.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sparse beamforming design for network MIMO system with per-base-station backhaul constraints

2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

2014

Self Cite

“…Differing from the utility maximization problems in conventional wireless networks with only transmit power constraints, the additional backhaul constraints in C-RAN make the problem more challenging as the backhaul consumption at a particular BS is a function of not only the (continuous) user rates but also the (discrete) number of associated users. To tackle this mixed continuous and discrete optimization problem, existing literature mostly take the limited backhaul capacities into account implicitly either by fixing the BS clusters [27]- [29] or by adding the backhaul as a penalized term into the objective function [30]- [32]. Specifically, [27] considers sum rate maximization under fixed and disjoint clustering scheme while [28] and [29] maximize a more general utility function under user-centric but predetermined BS clusters.…”

Section: A Related Workmentioning

confidence: 99%

Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network

2014

IEEE Access

366

This paper considers a downlink cloud radio access network (C-RAN) in which all the base-stations (BSs) are connected to a central computing cloud via digital backhaul links with finite capacities. Each user is associated with a user-centric cluster of BSs; the central processor shares the user's data with the BSs in the cluster, which then cooperatively serve the user through joint beamforming. Under this setup, this paper investigates the user scheduling, BS clustering, and beamforming design problem from a network utility maximization perspective. Differing from previous works, this paper explicitly considers the per-BS backhaul capacity constraints. We formulate the network utility maximization problem for the downlink C-RAN under two different models depending on whether the BS clustering for each user is dynamic or static over different user scheduling time slots. In the former case, the user-centric BS cluster is dynamically optimized for each scheduled user along with the beamforming vector in each time-frequency slot, whereas in the latter case, the user-centric BS cluster is fixed for each user and we jointly optimize the user scheduling and the beamforming vector to account for the backhaul constraints. In both cases, the nonconvex per-BS backhaul constraints are approximated using the reweighted 1 -norm technique. This approximation allows us to reformulate the per-BS backhaul constraints into weighted per-BS power constraints and solve the weighted sum rate maximization problem through a generalized weighted minimum mean square error approach. This paper shows that the proposed dynamic clustering algorithm can achieve significant performance gain over existing naive clustering schemes. This paper also proposes two heuristic static clustering schemes that can already achieve a substantial portion of the gain.INDEX TERMS Cloud radio access network (C-RAN), network multiple-input multiple-output (MIMO), coordinated multi-point (CoMP), limited backhaul, user scheduling, base-station clustering, beamforming, weighted sum rate maximization, weighted minimum mean square error (WMMSE).

“…This allows [12] to use a weighted MMSE technique to find a local optimal solution to the rate maximization problem. Finally, the problem setup of the present paper is also related to [13], which proposes a heuristic way of solving the problem of clustering, beamforming design, user scheduling, and power allocation in a decoupled fashion.…”

Section: Introductionmentioning

confidence: 99%

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

2013 IEEE Global Communications Conference (GLOBECOM)

2013

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 usercentric 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 0-norm optimization problem, then uses the iterative reweighted 1 heuristic to find a solution. A key observation of this paper is that the reweighting can be done on the 2-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.