Yuben Qu

Network coding-based multicast in multi-hop CRNs under uncertain spectrum availability

The benefits of network coding on multicast in traditional multi-hop wireless networks have already been demonstrated in previous works. However, most existing approaches cannot be directly applied to multi-hop cognitive radio networks (CRNs), given the unpredictable primary user occupancy on licensed channels. Specifically, due to the unpredictable occupancy, the channels bandwidth is uncertain and thus the capacity of the link using this channel is also uncertain, which may result in severe throughput loss. In this paper, we study the problem of network coding-based multicast in multi-hop CRNs considering the uncertain spectrum availability. To capture the uncertainty of spectrum availability, we first formulate our problem as a chance-constrained program. Given the computationally intractability of the above program, we transform the original problem into a tractable convex optimization problem, through appropriate Bernstein approximation together with relaxation on link scheduling. We further leverage Lagrangian relaxation-based optimization techniques to propose an efficient distributed algorithm for the original problem. Extensive simulation results show that, the proposed algorithm achieves higher multicast rates, compared to a state-of-the-art non-network coding algorithm in multi-hop CRNs, and a conservative robust algorithm that treats the link capacity as a constant value in the optimization.

DCNC: throughput maximization via delay controlled network coding for wireless mesh networks

Network coding NC can greatly improve the performance of wireless mesh networks WMNs in terms of throughput and reliability, and so on. However, NC generally performs a batch-based transmission scheme, the main drawback of this scheme is the inevitable increase in average packet delay, that is, a large batch size may achieve higher throughput but also induce larger average packet delay. In this work, we put our focus on the tradeoff between the average throughput and packet delay; in particular, our ultimate goal is to maximize the throughput for real-time traffic under the premise of diversified and time-varying delay requirements. To tackle this problem, we propose DCNC, a delay controlled network coding protocol, which can improve the throughput for real-time traffic by dynamically controlling the delay in WMNs. To define an appropriate control foundation, we first build up a delay prediction model to capture the relationship between the average packet delay and the encoding batch size. Then, we design a novel freedom-based feedback scheme to efficiently reflect the reception of receivers in a reliable way. Based on the predicted delay and current reception status, DCNC utilizes the continuous encoding batch size adjustment to control delay and further improve the throughput. Extensive simulations show that, when faced with the diversified and time-varying delay requirements, DCNC can constantly fulfill the delay requirements, for example, achieving over 95% efficient packet delivery ratio EPDR in all instances under good channel quality, and also obtains higher throughput than the state-of-art protocol. Copyright

Opportunistic network coding for secondary users in cognitive radio networks

In cognitive radio networks (CRNs), secondary users (SUs) may employ network coding to pursue higher throughput. However, as SUs must vacate the spectrum when it is accessed by primary users (PUs), the available transmission time of SUs is usually uncertain, i.e., SUs do not know how long the idle state can last. Meanwhile, existing network coding strategies generally adopt a block-based transmission scheme, which means that all packets in the same block can be decoded simultaneously only when enough coded packets are collected. Therefore, the gain brought by network coding may be dramatically decreased as the packet collection process may be interrupted due to the unexpected arrivals of PUs.In this paper, for the first time, we develop an efficient network coding strategy for SUs while considering the uncertain idle durations in CRNs. At its heart is that systematic network coding (SNC) is employed to opportunistically utilize the idle duration left by PUs. To handle the uncertainty of idle durations, we utilize confidential interval estimation to estimate the expected duration for stochastic idle durations, and multi-armed bandits to determine the duration sequentially for non-stochastic idle durations, respectively. Then, we propose a coding parameter selection algorithm for SNC by considering the complicated correlation among the receptions at different receivers. Simulation results show that, our proposed schemes outperform both traditional optimal block-based network coding and non-network coding schemes, and achieve competitive performance compared with the scheme with perfect idle duration information.

Spectrum-Aware Network Coded Multicast in Mobile Cognitive Radio Ad Hoc Networks

Secondary users (SUs) can flexibly access licensed channels to improve the spectrum utilization in cognitive radio networks (CRNs). In mobile cognitive radio ad hoc networks (MCRAHNs) where nodes can move, SUs are intermittently connected because of node mobility, as well as the uncertain channel availability. Providing multicast services in MCRAHNs is urgently needed with the compelling demand on service qualities and varieties, and the proliferation of cognitive technology. The benefits of network coding over multicast in traditional wireless networks or CRNs have been well demonstrated in existing works. However, the existing studies merely address the two crucial issues (i.e., uncertain channel availability and node mobility) together and, thereby, cannot be applied to network coding-based multicast in MCRAHNs. In this paper, we study the problem of network coding-based multicast in MCRAHNs considering both channel uncertainty and node mobility. We utilize discrete-time Markov chains to model the channel availability and node mobility in MCRAHNs, and we then formulate a spectrum-aware network coded multicast problem as an optimization problem, which minimizes the total transmission cost, subject to the timely successful delivery constraint and link transmission constraint. Since the formulated problem is hard to tackle according to mixed integer programing, we further design a distributed spectrum-aware cost-based (SACB) scheme based on the metric of forwarding benefit. With extensive simulations based on both synthetic and realistic traces, we show that, compared with existing schemes, SACB can achieve almost the minimum transmission cost while maintaining high multicast success probability.

A comparison of feedback schemes for network coding in wireless mesh networks

Intra-flow Network Coding (NC) has attracted extensive attention because that it can yield substantial performance gains in wireless mesh networks (WMNs). Feedback scheme is critical for almost all intra-flow NC protocols because the feedback can be used to inform the sender of the accomplishment of transmission as well as parameter adaptation, etc. The current feedback schemes in intra-flow NC technique mainly include two categories: Traditional feedback (TF) and Freedom feedback (FF). However, the performance comparison of these two types of feedback schemes has not been fully studied. In this paper, we model the two feedback schemes and compare their performance in WMNs. Through extensive simulations, we show that when the source uses small transmission intervals, FF outperforms TF in terms of both decoding delay and utilization, while FF has no advantage over TF under large transmission intervals. Furthermore, for both TF and FF, the performance degrades with the deterioration of link quality, and the trend is similar. We believe that this work can provide insight into the design of efficient intra-flow NC protocol in WMNs.

Multicast in multi-channel cognitive radio ad hoc networks: Challenges and research aspects

Abstract Cognitive radio (CR) is an emerging technology that has been around for more than fifteen years to relieve spectrum shortages. Based on the concept of CR, multiple nodes opportunistically share the licensed spectrum over multiple licensed channels and form a multi-channel cognitive radio ad hoc networks (CRAHNs). While multicast in multi-channel CRAHNs is urgently needed, it is challenging since there are some intrinsic differences between multi-channel CRAHNs and conventional multi-channel wireless ad hoc networks (WAHNs). This article summarizes the main unique characteristics of multi-channel CRAHNs in time-, frequency- and space-domain. The key research aspects of multicast in multi-channel CRAHNs include joint multicast routing and spectrum allocation, cross-layer multicast scheduling, and multicast with QoS guarantee. The article also pays a special attention on discussing how to employ network coding techniques to improve the performance of multicast in multi-channel CRAHNs.

Multicast in Multihop CRNs Under Uncertain Spectrum Availability: A Network Coding Approach

The benefits of network coding on multicast in traditional multihop wireless networks have already been extensively demonstrated in previous works. However, most existing approaches cannot be directly applied to multihop cognitive radio networks (CRNs), given the unpredictable primary user occupancy on licensed channels. Specifically, due to the unpredictable occupancy, the channel’s available bandwidth is time-varying and uncertain. Accordingly, the capacity of the link using that channel is also uncertain, which can significantly affect the network coding subgraph optimization and may result in severe throughput loss if not properly handled. In this paper, we study the problem of network coding-based multicast in multihop CRNs while considering the uncertain spectrum availability. To capture the uncertainty of spectrum availability, we first formulate our problem as a chance-constrained program. Given the computational intractability of the above-mentioned program, we then transform the original problem into a tractable convex optimization problem, through appropriate Bernstein approximation with relaxation on link scheduling. We further leverage Lagrangian relaxation-based optimization techniques to propose an efficient distributed algorithm for the original problem. Extensive simulation results show that the proposed algorithm achieves higher multicast rates, compared with a state-of-the-art non-network coding algorithm in multihop CRNs, and a conservative robust network coding algorithm that treats the link capacity as a constant value in the optimization.

A Two-Dimensional Multiarmed Bandit Approach to Secondary Users with Network Coding in Cognitive Radio Networks

We study how to utilize network coding to improve the throughput of secondary users (SUs) in cognitive radio networks (CRNs) when the channel quality is unavailable at SUs. We use a two-dimensional multiarmed bandit (MAB) approach to solve the problem of SUs with network coding under unknown channel quality in CRNs. We analytically prove the asymptotical-throughput optimality of the proposed two-dimensional MAB algorithm. Simulation results show that our proposed algorithm achieves comparable throughput performance, compared to both the theoretical upper bound and the scheme assuming known channel quality information.