This paper considers a general Gaussian relay network where a source transmits a message to a destination with the help of N half-duplex relays. It proves that the information theoretic cut-set upper bound to the capacity can be achieved to within 2:021(N +2) bits with noisy network coding, thereby reducing the previously known gap. Further improved gap results are presented for more structured networks like diamond networks. It is then shown that the generalized Degrees-of-Freedom of a general Gaussian half-duplex relay network is the solution of a linear program, where the coefficients of the linear inequality constraints are proved to be the solution of several linear programs, known in graph theory as the assignment problem, for which efficient numerical algorithms exist. The optimal schedule, that is, the optimal value of the 2^N possible transmit-receive configurations states for the relays, is investigated and known results for diamond networks are extended to general relay networks. It is shown, for the case of 2 relays, that only 3 out of the 4 possible states have strictly positive probability. Extensive experimental results show that, for a general N-relay network with N<9, the optimal schedule has at most N +1 states with strictly positive probability. As an extension of a conjecture presented for diamond networks, it is conjectured that this result holds for any HD relay network and any number of relays. Finally, a 2-relay network is studied to determine the channel conditions under which selecting the best relay is not optimal, and to highlight the nature of the rate gain due to multiple relays.
Relay networks were also studied in @cite_26 , where an iterative algorithm was proposed to determine the optimal fraction of time each HD relay transmits receives by using DF under deterministic switching mechanism.
@cite_26
2078414674 1993163337 1985276827 1862689498
We study the half-duplex multiple-relay channel (HD-MRC) where every node can either transmit or listen but cannot do both at the same time. We obtain a capacity upper bound based on a max-flow min-cut argument and achievable transmission rates based on the decode-forward (DF) coding strategy, for both the discrete memoryless HD-MRC and the phase-fading HD-MRC. We discover that both the upper bound and the achievable rates are functions of the transmit listen state (a description of which nodes transmit and which receive). More precisely, they are functions of the time fraction of the different states, which we term a schedule. We formulate the optimal scheduling problem to find an optimal schedule that maximizes the DF rate. The optimal scheduling problem turns out to be a maximin optimization, for which we propose an algorithmic solution. We demonstrate our approach on a four-node multiple-relay channel, obtaining closed-form solutions in certain scenarios. Furthermore, we show that for the received signal-to-noise ratio degraded phase-fading HD-MRC, the optimal scheduling problem can be simplified to a max optimization. The performance analysis of optimal decode-and-forward (DF) relay selection for the full-duplex (FD) mode in underlay cognitive radio (CR) networks is studied, with the impact of critical parameters such as the residual self-interference, the distributions of the received signal-to-noise ratio (SNR), and the outage probability, being analyzed. Different from the conventional relay selection schemes with half-duplex (HD) mode, the FD relay suffers from the impact of residual self-interference due to its simultaneous transmission and reception using the same channel. Furthermore, the exact closed-form expressions for the outage probabilities of the proposed FD relay-selection scheme over both independent Nakagami- @math and Rayleigh fading channels are derived, with the validity of the proposed analysis being proven by using simulation. Numerical results show that, in the presence of multiple candidate relays, an optimum relay-selection solution in terms of outage probability can always be achievable within the SNR range of (10 dB, 15 dB). We consider a three-node relay network comprised of a source, a relay, and a destination, and compare the throughputs of ideal full-duplex (FD) and half-duplex (HD) relaying. For FD relaying, we assume that the relay uses half of its antennas for reception and the other half for transmission. In contrast, for HD relaying, the relay uses all of its antennas for either reception or transmission and a buffer for data storage. Our results show that if the numbers of antennas at source and destination are equal to or larger than the number of antennas at the relay, HD relaying may achieve a similar, and in some cases even higher, throughput compared to ideal FD relaying. In this paper, a point-to-point orthogonal-frequency- division multiplexing (OFDM) system with a decode-and- forward (DF) relay is considered. The transmission consists of two hops. The source transmits in the first hop, and the relay transmits in the second hop. Each hop occupies one time slot. The relay is half-duplex, and capable of decoding the message on a particular subcarrier in one time slot, and re-encoding and forwarding it on a different subcarrier in the next time slot. Thus, each message is transmitted on a pair of subcarriers in two hops. It is assumed that the destination is capable of combining the signals from the source and the relay pertaining to the same message. The goal is to maximize the weighted sum rate of the system by jointly optimizing subcarrier pairing and power allocation on each subcarrier in each hop. The weighting of the rates is to take into account the fact that different subcarriers may carry signals for different services. Both total and individual power constraints for the source and the relay are investigated. For the situations where the relay does not transmit on some subcarriers because doing so does not improve the weighted sum rate, we further allow the source to transmit new messages on these idle subcarriers. To the best of our knowledge, such a joint optimization inclusive of the destination combining has not been discussed in the literature. The problem is first formulated as a mixed integer programming problem. It is then transformed to a convex optimization problem by continuous relaxation, and solved in the dual domain. Based on the optimization results, algorithms to achieve feasible solutions are also proposed. Simulation results show that the proposed algorithms almost achieve the optimal weighted sum rate and outperform the existing methods in various channel conditions.