Abstract-We evaluate the information-theoretic achievable rates of Quantize-Map-and-Forward (QMF) relaying schemes over Gaussian N -relay diamond networks. Focusing on vector Gaussian quantization at the relays, our goal is to understand how close to the cutset upper bound these schemes can achieve in the context of diamond networks, and how much benefit is obtained by optimizing the quantizer distortions at the relays. First, with noise-level quantization, we point out that the worst-case gap from the cutset upper bound is (N + log 2 N ) bits/s/Hz. A better universal quantization level found without using channel state information (CSI) leads to a sharpened gap of log 2 N + log 2 (1 + N ) + N log 2 (1 + 1/N ) bits/s/Hz. On the other hand, it turns out that finding the optimal distortion levels depending on the channel gains is a non-trivial problem in the general N -relay setup. We manage to solve the two-relay problem and the symmetric N -relay problem analytically, and show the improvement via numerical evaluations both in static as well as slow-fading channels.
We present the design and experimental evaluation of a wireless system that exploits relaying in the context of WiFi. We opt for WiFi given its popularity and wide spread use for a number of applications, such as smart homes. Our testbed consists of three nodes, a source, a relay and a destination, that operate using the physical layer procedures of IEEE802.11. We deploy three main competing strategies that have been proposed for relaying, Decode-andForward (DF), Amplify-and-Forward (AF) and QuantizeMap-Forward (QMF). QMF is the most recently introduced of the three, and although it was shown in theory to approximately achieve the capacity of arbitrary wireless networks, its performance in practice had not been evaluated. We present in this work experimental results-to the best of our knowledge, the first ones-that compare QMF, AF and DF in a realistic indoor setting. We find that QMF is a competitive scheme to the other two, offering in some cases up to 12% throughput benefits and up to 60% improvement in frame error-rates over the next best scheme.
Abstract-Quantize-Map-and-Forward (QMF) relaying has been shown to achieve the optimal diversity-multiplexing tradeoff (DMT) [1] for arbitrary slow fading full-duplex networks [2] as well as for the single-relay half-duplex network [3]. A key reason for this is that quantizing at the noise level suffices to achieve the cut-set bound approximately to within an additive gap, without any requirement of instantaneous channel state information (CSI). However, DMT only captures the high SNR performance and potentially, limited CSI at the relay can improve performance at moderate SNRs. In this work we propose an optimization framework for QMF relaying over slow fading channels. Focusing on vector Gaussian quantizers, we optimize the outage probability for the full-duplex and halfduplex single relay by finding the best quantization level and relay schedule according to the available CSI at the relays. For the N -relay diamond network, we derive an universal quantizer that sharpens the additive approximation gap of QMF from the conventional Θ(N ) bits/s/Hz [2] [4] to Θ(log(N )) bits/s/Hz using only network topology information. Analytical solutions to channel-aware optimal quantizers for two-relay and symmetric N -relay diamond networks are also derived. In addition, we prove that suitable hybridizations of our optimized QMF schemes with Decode-Forward (DF) or Dynamic DF protocols provide significant finite SNR gains over the individual schemes.
Abstract-We present a structured Quantize-Map-andForward (QMF) scheme for cooperative communication over wireless networks, that employs LDPC ensembles for the node operations and message-passing algorithms for decoding. We demonstrate through extensive simulation results over the fullduplex parallel relay network, that our scheme, with no transmit channel state information, offers a robust performance over fading channels and achieves the full diversity order of our network at moderate SNRs.
Abstract-We consider a source that would like to communicate with a destination over a layered Gaussian relay network. We present a computationally efficient method that enables to select a near-optimal (in terms of throughput) subnetwork of a given size connecting the source with the destination. Our method starts by formulating an integer optimization problem that maximizes the rates that the Quantize-Map-and-Forward relaying protocol can achieve over a selected subnetwork; we then relax the integer constraints to obtain a non-linear optimization over reals. For diamond networks, we prove that this optimization over reals is concave, while for general layered networks we give empirical demonstrations of near-concavity, paving the way for efficient algorithms to solve the relaxed problem. We then round the relaxed solution to select a specific subnetwork. Simulations using off-the-shelf non-linear optimization algorithms demonstrate excellent performance with respect to the true integer optimum for both diamond networks as well as multi-layered networks. Even with these non-customized algorithms, significant time savings are observed vis-à-vis exhaustive integer optimization 1 .
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