Analysis of Outage Probability and Throughput for Half-Duplex Hybrid-ARQ Relay Channels

International audienceWe consider a half-duplex wireless relay network with hybrid-automatic retransmission request (HARQ) and Rayleigh fading channels. In this paper, we analyze the average throughput and outage probability of the multirelay delay-limited (DL) HARQ system with an opportunistic relaying scheme in decode-and-forward (DF) mode, in which the best relay is selected to transmit the source's regenerated signal. A simple and distributed relay selection strategy is considered for multirelay HARQ channels. Then, we utilize the nonorthogonal cooperative transmission between the source and selected relay for retransmission of source data toward the destination, if needed, using space-time codes. We analyze the performance of the system. We first derive the cumulative density function (cdf) and probability density function (pdf) of the selected relay HARQ channels. Then, the cdf and pdf are used to determine the exact outage probability in the lth round of HARQ. The outage probability is required to compute the throughput-delay performance of this half-dublex opportunistic relaying protocol. The packet delay constraint is represented by L, which is maximum number of HARQ rounds. Furthermore, simple closed-form upper bounds on outage probability are derived. Based on the derived upper bound expressions, it is shown that the proposed schemes achieve the full spatial diversity order of N+1, where N is the number of potential relays. In addition, simulation shows that our proposed scheme can achieve higher average throughput, compared with direct transmission and conventional tho-phase relay networks

Amplify-and-forward relay identification using joint Tx/Rx I/Q imbalance-based device fingerprinting

Abstract Relay identification is necessary in many cooperative communication applications such as detecting the presence of malicious relays for communication security, selecting the intended relays for signal forwarding, and tracing a specific relay. However, this identification task becomes extremely challenging for amplify-and-forward (AF) relaying systems since AF relays usually have no capability of adopting traditional identification methods implemented above the physical layer. This paper proposes a physical-layer AF relay identification scheme based on the exploitation of the device-specific in-phase and quadrature-phase imbalance (IQI) feature. Given that IQI estimation is mandatory in most present receivers for compensation, it is cost-effective to make use of these estimation results for fingerprinting AF relays. A generalized likelihood ratio test-based fingerprint differentiation technique is adopted to detect the minor difference between two range-limited IQI fingerprints. Using this differentiation technique, a whitelist-based identification algorithm consisting of fingerprint registration, update, and identification is proposed. Furthermore, the optimal training signals that lead to the maximal detection probability are derived for the typical quadrature amplitude modulation and phase-shift keying modulation schemes. The simulation results validate our derivations and confirm that the proposed method can accurately identify AF relays

Connectivity-based centroid localization Using distributed dense reference nodes

The accuracy of centroid localization based on the connectivity information between a target node and multiple randomly scattered reference nodes is investigated. First, the accuracy of the centroid localization is analyzed in a general fading environment and it is shown that the ambiguity area of this localization technique is inversely proportional to the density of the reference nodes. Additionally, this area is at the same order and in the best case half of the ambiguity area provided by estimating the location of a target node as the location of its nearest reference node. Furthermore, increasing the transmission power of a target node will increase the accuracy of the localization, but only up to a certain limit. Then, the findings on centroid localization are verified by computer simulations