An optimal link layer model for multi-hop MIMO networks

Shi, Yi; Liu, Jia; Jiang, Canming; Gao, Cunhao; Hou, Y. Thomas

doi:10.1109/infcom.2011.5934994

Cited by 26 publications

(53 citation statements)

References 21 publications

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“…Further, we employ DoF to represent MIMO resources at a node. A detailed discussion of DoF allocation for spatial multiplexing and interference cancellation is given in [19]. Simply put, when there is no interference, we need to allocate DoFs at both transmitter Tx( ) and receiver Rx( ) to achieve data streams on link .…”

Section: B Mathematical Modelingmentioning

confidence: 99%

On the Asymptotic Capacity of Multi-Hop MIMO Ad Hoc Networks

Jiang

Shi

Hou

et al. 2011

IEEE Trans. Wireless Commun.

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Abstract-Multi-input multi-output (MIMO) is a key technology to increase the capacity of wireless networks. Although there has been extensive work on MIMO at the physical and link layers, there is limited work on MIMO at the network layer (i.e., multi-hop MIMO network), particularly results on capacity scaling laws. In this paper, we investigate capacity scaling laws for MIMO ad hoc networks. Our goal is to find the achievable throughput of each node as the number of nodes in the network increases. We employ a MIMO network model that captures spatial multiplexing and interference cancellation. We show that for a MIMO network with randomly located nodes, each equipped with antennas and a rate of on each data stream, the achievable throughput of each node is Θ( √ ln ).

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Section: B Mathematical Modelingmentioning

confidence: 99%

On the Asymptotic Capacity of Multi-Hop MIMO Ad Hoc Networks

Jiang

Shi

Hou

et al. 2011

IEEE Trans. Wireless Commun.

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“…Now we have all the constraints needed in an optimization framework for a multi-hop network, which include scheduling constraints (14), (26), joint PHY-Link constraints (21), (22), (23), (24), (25), and flow routing constraints (8), (9), (11). We summarize them in Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Both were proposed to resolve packet collisions, and they require that some bits in one of the collision packets be known in advance. SIC also differs from smart antenna-based interference cancellation schemes, such as Zero-Forcing Beam Forming (ZFBF) [2], [23], [31] in MIMO 1 and directional antennas [14], [19], [24]. Very recently, there is a growing interest to exploit SIC at the physical layer to improve performance of upper layers in a wireless network [3], [7], [9], [15], [16], [30].…”

Section: Related Workmentioning

confidence: 99%

Squeezing the most out of interference: An optimization framework for joint interference exploitation and avoidance

Jiang

Shi

Hou

et al. 2012

2012 Proceedings IEEE INFOCOM

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Abstract-There is a growing interest on exploiting interference (rather than avoiding it) to increase network throughput. In particular, the so-called successive interference cancellation (SIC) scheme appears very promising, due to its ability to enable concurrent receptions from multiple transmitters and interference rejection. Although SIC has been extensively studied as a physical layer technology, its research and advances in the context of multi-hop wireless network remain limited. In this paper, we try to answer the following fundamental questions. (i) What are the limitations of SIC? How to overcome such limitations? (ii) How to optimize the interaction between SIC and interference avoidance? How to incorporate multiple layers (physical, link, and network) in an optimization framework? We find that SIC alone is not adequate to handle interference in a multi-hop wireless network, and advocate the use of joint SIC and interference avoidance. To optimize the joint scheme, we propose a cross-layer optimization framework that incorporates variables at physical, link, and network layers. We use numerical results to affirm the validity of our optimization framework and give insights on how SIC and interference avoidance can complement each other in an optimal manner.

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“…both r [k] and t [l] include this interference in the computation of their weights. With this modification, we can use (8) and (9) to compute the MMSE combining weights at r [k] in the context of bilateral interference cancellation.…”

Section: B Minimum Mean-squared-error Combining For Unilateral and Bmentioning

confidence: 99%

“…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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An optimal link layer model for multi-hop MIMO networks

Cited by 26 publications

References 21 publications

On the Asymptotic Capacity of Multi-Hop MIMO Ad Hoc Networks

On the Asymptotic Capacity of Multi-Hop MIMO Ad Hoc Networks

Squeezing the most out of interference: An optimization framework for joint interference exploitation and avoidance

The performance loss of unilateral interference cancellation

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