A General Algorithm for Interference Alignment and Cancellation in Wireless Networks

Li, Li Erran; Alimi, Richard; Shen, Dawei; Viswanathan, Harish; Yang, Yufan

doi:10.1109/infcom.2010.5461997

Cited by 49 publications

(38 citation statements)

References 10 publications

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“…This discovery of everyone gets half of the pie has since spurred considerable interest in the wireless communication community. The underlying technique, aligning unwanted signals and contracting their dimensions perceived at a receiver, has spawned further applications [2], [7], [11]. In comparison with IA, SA does not necessarily differentiate between wanted signals and unwanted interferences.…”

Section: Previous Researchmentioning

confidence: 99%

“…The idea and benefit of PNC-SA can be illustrated in an uplink communication scenario, designed to motivate interference alignment and cancellation (IAC) [2], [7], a technique for improving throughput in MIMO networks. Such a multi-user MIMO (MU-MIMO) architecture represents a trend in cellular communication that seeks further capacity gain over a simple MIMO link.…”

Section: Introductionmentioning

confidence: 99%

“…IAC [2], [7] breaks through this limitation by combining interference alignment (IA) [9] and interference cancellation (IC) techniques. As shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Signal Alignment: Enabling Physical Layer Network Coding for MIMO Networking

Zhou

et al. 2013

IEEE Trans. Wireless Commun.

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Abstract-We apply signal alignment (SA), a wireless communication technique that enables physical layer network coding (PNC) in multi-input multi-output (MIMO) wireless networks. Through calculated precoding, SA contracts the perceived signal space at a node to match its receive capability, and hence facilitates the demodulation of linearly combined data packets. PNC coupled with SA (PNC-SA) has the potential of fully exploiting the precoding space at the senders, and can better utilize the spatial diversity of a MIMO network for higher system degrees-of-freedom (DoF). PNC-SA adopts the idea of 'demodulating a linear combination' from PNC. The design of PNC-SA is also inspired by recent advances in IA, though SA aligns signals not interferences. We study the optimal precoding and power allocation problem of PNC-SA, for SNR (singal-tonoise-ratio) maximization at the receiver. The mapping from SNR to BER is then analyzed, revealing that the DoF gain of PNC-SA does not come with a sacrifice in BER. We then design a general PNC-SA algorithm in larger systems, and demonstrate general applications of PNC-SA, and show via network level simulations that it can substantially increase the throughput of unicast and multicast sessions, by opening previously unexplored solution spaces in multi-hop MIMO routing.

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Section: Previous Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Signal Alignment: Enabling Physical Layer Network Coding for MIMO Networking

Zhou

et al. 2013

IEEE Trans. Wireless Commun.

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“…Recently, Zigzag decoding is proposed to resolve the collision by using multiple composite signals [8]. In addition, how to combine the interference alignment and cancellation techniques to improve the network throughput is studied in [14]. Among them, successive interference cancellation (SIC) is the simplest one, whose effectiveness in an ad hoc network has been already verified experimentally [12].…”

Section: Related Workmentioning

confidence: 99%

Link scheduling in wireless networks with successive interference cancellation

Zhuang

Wang

et al. 2011

Computer Networks

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Successive interference cancellation (SIC) is an effective way of multipacket reception (MPR) to combat interference at the physical layer. To understand the potential MPR advantages, we study link scheduling in an ad hoc network with SIC at the physical layer. The fact that the links detected sequentially by SIC are correlated at the receiver poses key technical challenges. A link can be interfered indirectly when the detecting and removing of the correlated signals fail. We characterize the link dependence and propose a simultaneity graph (SG) to capture the effect of SIC. Then interference number is defined to measure the interference of a link. We show that scheduling over SG is NP-hard and the maximum interference number bounds the performance of a maximal greedy scheme. An independent set based greedy scheme is explored to efficiently construct a maximal feasible schedule. Moreover, with careful selection of link ordering, we present a scheduling scheme that improves the bound. The performance is evaluated by both simulations and measurements in a testbed. The throughput gain is on average 40% and up to 120% over IEEE 802.11. The complexity of SG is comparable with that of conflict graph, especially when the network size is not large.

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“…Multiuser detection [10] enables receivers to cancel interference provided that it is received with sufficient strength. Various multiuser detection techniques have been developed, such as successive interference cancellation [1], [8], parallel interference cancellation [3] and more recently zero forcing and interference alignment [5], [7]. Rate adaptation for an IC-enabled cognitive radio system was studied in [9] to derive achievable rate regions.…”

Section: Introductionmentioning

confidence: 99%

Optimal and Collaborative Rate Selection for Interference Cancellation in Wireless Networks

2011

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Abstract-Analysis of wireless systems commonly assumes single-user detection at the receivers. Interference is typically treated as noise. On the other hand, multiuser detection has long been taking advantage of interference cancellation (IC) to increase capacity. We exploit IC by optimal rate selection. Transmission rates are collaboratively optimized to maximize the benefit of IC. A link reduces its rate, if that enables IC to significantly boost the SINR on other links. We provide a complexity analysis and an integer programming model to find the optimal IC pattern. Simulation results indicate that throughput improvement is over 30% in low SINR regimes.

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A General Algorithm for Interference Alignment and Cancellation in Wireless Networks

Cited by 49 publications

References 10 publications

Signal Alignment: Enabling Physical Layer Network Coding for MIMO Networking

Signal Alignment: Enabling Physical Layer Network Coding for MIMO Networking

Link scheduling in wireless networks with successive interference cancellation

Optimal and Collaborative Rate Selection for Interference Cancellation in Wireless Networks

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