Paper Info
Title
MIMO-Pipe Modeling and Scheduling for Efficient Interference Management in Multihop MIMO Networks
Abstract
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 {(Ri, SINRi) }, where each pair (Ri, SINRi) corresponds to a meaningful stream multiplexing configuration for individual MIMO links [with Ri being the rate and SINRi 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 0-1 knapsack approach, we devise centralized approximation algorithms fo- - r 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.
Year
DOI
Venue
2010
10.1109/TVT.2010.2060376
IEEE Transactions on Vehicular Technology
Keywords
Field
DocType
mimo link abstraction,mac,optimisation,throughput maximization,radio links,distributed algorithms,cross-layer optimization,dense network model,wireless communications,scheduling,interference-limited regimes,multihop multiple-input–multiple-output (mimo) networks,mimo-pipe scheduling,multihop mimo networks,wireless scheduling,transport layers,randomization,cross-layer optimization framework,combinatorial optimization problem,combinatorial mathematics,physical interference,point-to-point settings,multidimensional 0-1 knapsack approach,mimo link,approximation algorithms,radio networks,medium access control,signal-to-interference-plus-noise ratio,optimal scheduling policy,efficient interference management,contention-based distributed algorithm,continuous relaxation,access protocols,channel capacity,contention probability,mimo gains,protocol stack,multihop networks,knapsack problems,phy interference,link abstraction model,multiple-input-multiple-output technology,mimo technology,multihop wireless networks,mimo systems,single-user systems,interference limited,interference (signal),mimo-pipe modeling,probability,wireless communication,interference,signal to noise ratio,transport layer,spread spectrum communication,multiplexing,network model,reliability,bottom up,signal to interference plus noise ratio,distributed algorithm,point to point,mimo
Wireless network,Cross-layer optimization,Multi-user MIMO,3G MIMO,Computer science,MIMO,Computer network,Electronic engineering,Signal-to-interference-plus-noise ratio,Distributed algorithm,Channel capacity
Journal
Volume
Issue
ISSN
59
8
0018-9545
Citations 
PageRank 
References 
8
0.51
16
Authors
3
Name
Order
Citations
PageRank
Weiyan Ge123216.28
Junshan Zhang22905220.99
Guoliang Xue34438279.38