Maximizing throughput in MIMO networks with variable rate streams

Srinivasan, R.; Blough, Douglas M.; Cortes-Pena, Luis Miguel; Santi, Paolo

doi:10.1109/ew.2010.5483484

Cited by 10 publications

(18 citation statements)

References 18 publications

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“…It has only been fairly recently that MIMO networking research has considered network throughput optimization [3], [5], [10], [11], [13], [14], [16], [17], [18], [19]. A number of these papers present formal network optimization problem formulations [3], [5], [10], [13], [19].…”

Section: Related Workmentioning

confidence: 99%

“…For example, in the stream allocation problem defined in [18], a primary-interference-free link set is given. This version of the problem is arrived at by removing Constraint (6), which enforces primary interference restrictions.…”

Section: Linear Program Formulationmentioning

confidence: 99%

“…This version of the problem is arrived at by removing Constraint (6), which enforces primary interference restrictions. In [18] this problem was defined (informally) and heuristic algorithms for single-collision-domain networks were studied. Here, we study the exact optimal solution for multihop networks.…”

Section: Linear Program Formulationmentioning

confidence: 99%

“…The first comparison focuses on a heuristic scheduling algorithm for multihop MIMO networks, which we refer to as Algorithm StreamMaxRate-Multihop (SMR-M). Algorithm SMR-M is based on Algorithm StreamMaxRate from [18], which is a type of gradient-descent heuristic scheduling algorithm for single-collision-domain MIMO networks, which accounts for arbitrary rate functions on links. Algorithm SMR-M extends Algorithm SMR to work in multihop networks and also executes the algorithm on 10 random link orderings and selects the best solution of the 10.…”

Section: E Comparison Of Optimal and Sub-optimal Solutionsmentioning

confidence: 99%

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Optimal one-shot scheduling for MIMO networks

Blough

Resta

Santi

et al. 2011

2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Linear Program Formulationmentioning

confidence: 99%

Section: Linear Program Formulationmentioning

confidence: 99%

Section: E Comparison Of Optimal and Sub-optimal Solutionsmentioning

confidence: 99%

See 2 more Smart Citations

Optimal one-shot scheduling for MIMO networks

Blough

Resta

Santi

et al. 2011

2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks

Self Cite

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“…Many existing strategies for managing interference in a network of MIMO links adopt a unilateral approach. Examples of unilateral strategies include the SRP/SRMP-CiM [4], SPACE-MAC [5], CAS [6], OBIC [7,8], ExtendedGreedy [9], OSTM [10], and CLOM [11].…”

Section: Introductionmentioning

confidence: 99%

The performance loss of unilateral interference cancellation

Cortes-Pena

Barry

Blough

2012

2012 IEEE International Conference on Communications (ICC)

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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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