Spatial Resources Optimization in Distributed MIMO Networks with Limited Data Sharing

Wireless access through a large distributed network of low-complexity infrastructure nodes empowered with cooperation and coordination capabilities, is an emerging radio architecture, candidate to deal with the mobile data capacity crunch. In the 3GPP evolutionary path, this is known as the Cloud-RAN paradigm for future radio. In such a complex network, distributed MIMO resources optimization is of paramount importance, in order to achieve capacity scaling. In this paper, we investigate efficient strategies towards optimizing the pairing of access nodes with users as well as linear precoding designs for providing fair QoS experience across the whole network, when data sharing is limited due to complexity and overhead constraints. We propose a method for obtaining the exact optimal spatial resources allocation solution which can be applied in networks of limited scale, as well as an approximation algorithm with bounded polynomial complexity which can be used in larger networks. The particular algorithm outperforms existing user-oriented clustering techniques and achieves quite high quality-of-service levels with reasonable complexity.Comment: submitted to Globecom 2013 - Wireless Communications Symposiu

Joint Power and Antenna Selection Optimization for Energy-Efficient Large Distributed MIMO Networks

Large multiple-input multiple-output (MIMO) network promises high energy efficiency using a large number of antennas. To reduce the signaling overhead of obtaining the full channel state information, we propose a downlink antenna selection scheme for large distributed MIMO networks with regularized zero-forcing (RZF) precoding. We study the joint optimization of antenna selection, regularization factor, and power allocation to maximize the average weighted sum-rate. The problem is non-trivial due to its combinatorial and non-convex nature. We decompose the problem into subproblems, each of which is solved by an efficient algorithm. For very large distributed MIMO networks, we obtain a capacity scaling law and show that there is an asymptotic decoupling effect, which can be exploited to simplify algorithms and physical layer processing. Simulations show that the proposed scheme achieves significant gain over the baseline