Abstract-A MIMO network is a wireless network made up of individual MIMO links. The problem we consider is to maximize throughput in a multihop MIMO network with interference suppression. Our problem formulation accounts for variable rates on the MIMO links, which depend on the channel conditions of the link, and the manner in which the diversity-multiplexing trade-off is handled. We present an ILP formulation of the MIMO one-shot scheduling problem with variable rates, which is the first exact formulation of a MIMO network optimization problem that accounts for full interference suppression capabilities of MIMO links. We use CPLEX to evaluate the optimal solution based on the ILP formulation for wireless networks with up to 32 concurrently transmitting links. We also modify a heuristic algorithm from a related MIMO scheduling problem to work in our problem setting. Results show that the heuristic can scale to networks with 80 or more concurrent links, but is 10-20% from optimal in terms of throughput. We show that the heuristic scheduler is not able to fully exploit the diversitymultiplexing-interference suppression tradeoff, which is inherent in the problem. This shows that there is substantial room for developing improved scheduling algorithms for MIMO networks and provides some insight into promising directions to explore.

The problem that we consider is that of maximizing throughput in a MIMO network while accounting for variable rate streams on MIMO links. The stream rates on a link depend on the channel conditions of the link, and the manner in which the diversity-multiplexing tradeoff is handled. In this work, we use the dependence of stream rates on the channel to develop methods of link selection and stream allocation that approximately maximize the aggregate throughput. Maximizing throughput is closely tied to the problem of allocating streams based on the stream rates of the selected links. Doing this optimally is very complex even for networks with 10 or fewer links. We develop a stream allocation heuristic that approximately maximizes the throughput over a given set of links. Simulation results for single collision domain networks show that our stream allocation heuristic is within 7% of optimal in networks with up to 10 links (in a typical case where the maximum concurrency allowed is 15 links). The algorithm also cuts the difference between heuristic and optimal results in half, compared to a simple greedy algorithm. Our research has also identified the feasibility checking problem for general MIMO networks as being a computationally hard problem. However, we also identify several practical special cases, e.g. when interference suppression is done only at the receiver side, for which feasibility checking remains a polynomial-time operation.

Abstract-We tackle the problem of determining the beamforming and combining weights in a network of interfering multiple-input multiple-output (MIMO) links. We classify any strategy for computing these weights as either unilateral or bilateral. A unilateral strategy is one for which the responsibility of cancelling interference from one node to another is preassigned to lie solely with only one of the two nodes, so that the other node is free to ignore the interference. Many existing strategies for managing interference in a network of MIMO nodes adopt the unilateral approach. In contrast, a bilateral strategy is one for which the responsibility of cancelling interference from one node to another is not preassigned, but is instead shared by both sides as the weights are computed. We present numerical examples to illustrate that bilateral strategies can significantly outperform unilateral strategies, especially for large networks and high interference. In one example, a bilateral approach delivers an aggregate capacity that is 227% higher than that of the best unilateral approach. We conclude that, although unilateral strategies are useful for determining whether or not the streams allocated in a network of MIMO links can coexist, the weight computation should be done bilaterally to prevent throughput loss.

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