Enforce truth-telling in wireless relay networks for secure communication
ABSTRACT To ensure security in data transmission is one of the most important issues for wireless relay networks. In this paper, we consider a cooperative network, consisting of one source node, one destination node, one eavesdropper node, and a number of relay nodes. Specifically, the source selects several relay nodes which can help forward the signal to the corresponding destination to achieve the best security performance. However, the relay nodes may have the incentive not to report their true private channel information in order to get more chance to be selected and gain more payoff from the source. We employ a self-enforcing truth-telling mechanism into the network to solve this cheating problem. By adding a transfer payoff to the total payoff of each selected relay node, we prove that each relay node would get its maximum expected payoff only when it tells the truth to the source. And then, an optimal secrecy capacity of the network can be achieved. Simulation results verify the efficiency of the proposed mechanism.
SourceAvailable from: export.arxiv.org[Show abstract] [Hide abstract]
ABSTRACT: To ensure security in data transmission is one of the most important issues for wireless relay networks, and physical layer security is an attractive alternative solution to address this issue. In this paper, we consider a cooperative network, consisting of one source node, one destination node, one eavesdropper node, and a number of relay nodes. Specifically, the source may select several relays to help forward the signal to the corresponding destination to achieve the best security performance. However, the relays may have the incentive not to report their true private channel information in order to get more chances to be selected and gain more payoff from the source. We propose a Vickey-Clark-Grove (VCG) based mechanism and an Arrow-d'Aspremont-Gerard-Varet (AGV) based mechanism into the investigated relay network to solve this cheating problem. In these two different mechanisms, we design different "transfer payment" functions to the payoff of each selected relay and prove that each relay gets its maximum (expected) payoff when it truthfully reveals its private channel information to the source. And then, an optimal secrecy rate of the network can be achieved. After discussing and comparing the VCG and AGV mechanisms, we prove that the AGV mechanism can achieve all of the basic qualifications (incentive compatibility, individual rationality and budget balance) for our system. Moreover, we discuss the optimal quantity of relays that the source node should select. Simulation results verify efficiency and fairness of the VCG and AGV mechanisms, and consolidate these conclusions.IEEE Transactions on Wireless Communications 07/2013; 12(9). DOI:10.1109/TWC.2013.080113.120260 · 2.76 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: Multicasting is emerging as an enabling technology for multimedia transmissions over wireless networks to support several groups of users with flexible quality of service (QoS) requirements. Although multicast has huge potential to push the limits of next generation communication systems; it is however one of the most challenging issues currently being addressed. In this survey, we explain multicast group formation and various forms of group rate determination approaches. We also provide a systematic review of recent channel-aware multicast scheduling and resource allocation (MSRA) techniques proposed for downlink multicast services in OFDMA based systems. We study these enabling algorithms, evaluate their core characteristics, limitations and classify them using multidimensional matrix. We cohesively review the algorithms in terms of their throughput maximization, fairness considerations, performance complexities, multi-antenna support, optimality and simplifying assumptions. We discuss existing standards employing multicasting and further highlight some potential research opportunities in multicast systems.IEEE Communications Surveys & Tutorials 02/2013; 15(1):240-254. DOI:10.1109/SURV.2012.013012.00074 · 6.49 Impact Factor