MIMO-pipe modeling and scheduling for efficient interference management in multihop MIMO networks

Multiple-input-multiple-output (MIMO) technology, which is a recent breakthrough in wireless communications, has been shown to significantly improve channel capacity in single-user systems. However, obtaining a rigorous understanding of the possible MIMO gains in multihop networks is still an open topic. One grand challenge is that multihop wireless networks are interference limited and that the interference introduces coupling across various layers of the protocol stack, including the physical (PHY), medium access control (MAC), network, and transport layers. The fundamental differences between multihop networks and point-to-point settings dictate that leveraging the MIMO gains in multihop networks requires a domain change from high-SNR regimes to interference-limited regimes. In this paper, we develop a cross-layer optimization framework for effective interference management toward understanding fundamental tradeoffs among possible MIMO gains in multihop networks. We first take a bottom-up approach to develop a MIMO-pipe model based on PHY interference and extract a set of {(R_i, SINR_i)}, where each pair (R_i, SINR_i) corresponds to a meaningful stream multiplexing configuration for individual MIMO links [with R_i being the rate and SINR_i being the signal-to-interference-plus-noise ratio (SINR) requirement]. Using this link abstraction model, we study MIMO-pipe scheduling for throughput maximization. Based on continuous relaxation via randomization, we study the structural property of the optimal scheduling policy. Our findings reveal that, in an optimal strategy, it suffices for each MIMO link to use one stream configuration only (although each individual MIMO link can have multiple stream configurations). In light of this structural property, we then formulate MIMO-pipe scheduling as a combinatorial optimization problem, and by using a multidimensional 01 knapsack approach, we devise centralized approximation algorithms for both the dense network model and the extended network model, respectively. Next, we also develop a contention-based distributed algorithm, in which links update their contention probability based on local information only, and characterize the convergence and the performance of the distributed algorithm.