Energy harvesting non-orthogonal multiple access system with multi-antenna relay and base station

In this paper, we consider downlink non-orthogonal multiple access cooperative communication system. The base station (BS) serves two types of users, which are named relay user (RU) and far user (FU). The BS and RU are equipped with multiple transmit antennas. The RU harvests energy from the BS transmissions to perform the relaying operation for the FU. We have considered 1) amplify-forward; 2) decode-forward; and 3) quantize-map-forward relaying protocols at the RU. As the BS and RU have multiple antennas, therefore we consider 1) beamforming and 2) random antenna selection strategies at the BS and RU. Closed form expressions for the outage probability are provided for the aforementioned relay protocols and antenna strategies. Further, we show that for certain data rate range of the relay and FU the quantize-map-forward relaying protocol can perform better than the other two relaying protocols

1 Cooperative Relaying at Finite SNR – Role of Quantize-Map-and-Forward

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 the DMT-optimality of QMF is that quantizing at the noise level suffices to achieve the cut-set bound approximately to within an additive gap, without the requirement of any instantaneous channel state information (CSI). However, DMT only captures the high SNR performance and potentially, limited CSI at the relay can help improve the performance in moderate SNR regimes. 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 single relay by finding the best quantization level according to the available CSI at the relays. For the half-duplex relay channel, we find jointly optimal quantizer distortions and relay schedules using the same framework. Also, for the N-relay diamond network, we derive an universal quantizer that uses only the information of network topology. The universal quantizer sharpens the additive approximation gap of QMF from the conventional Θ(N) bits/s/Hz [2] [4] to Θ(log(N)) bits/s/Hz. 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 provides significant finite SNR gains over the individual schemes. I

Physical Layer Cooperation:Theory and Practice

Information theory has long pointed to the promise of physical layer cooperation in boosting the spectral efficiency of wireless networks. Yet, the optimum relaying strategy to achieve the network capacity has till date remained elusive. Recently however, a relaying strategy termed Quantize-Map-and-Forward (QMF) was proved to achieve the capacity of arbitrary wireless networks within a bounded additive gap. This thesis contributes to the design, analysis and implementation of QMF relaying by optimizing its performance for small relay networks, proposing low-complexity iteratively decodable codes, and carrying out over-the-air experiments using software-radio testbeds to assess real-world potential and competitiveness. The original QMF scheme has each relay performing the same operation, agnostic to the network topology and the channel state information (CSI); this facilitates the analysis for arbitrary networks, yet comes at a performance penalty for small networks and medium SNR regimes. In this thesis, we demonstrate the benefits one can gain for QMF if we optimize its performance by leveraging topological and channel state information. We show that for the N-relay diamond network, by taking into account topological information, we can exponentially reduce the QMF additive approximation gap from $\Theta(N)$ bits/s/Hz to $\Theta(\log N)$ bits/s/Hz, while for the one-relay and two-relay networks, use of topological information and CSI can help to gain as much as $6$ dB. Moreover, we explore what benefits we can realize if we jointly optimize QMF and half-duplex scheduling, as well as if we employ hybrid schemes that combine QMF and Decode-and-Forward (DF) relay operations. To take QMF from being a purely information-theoretic idea to an implementable strategy, we derive a structure employing Low-Density-Parity-Check (LDPC) ensembles for the relay node operations and message-passing algorithms for decoding. We demonstrate through extensive simulation results over the full-duplex diamond network, that our designs offer a robust performance over fading channels and achieves the full diversity order of our network at moderate SNRs. Next, we explore the potential real-world impact of QMF and present the design and experimental evaluation of a wireless system that exploits relaying in the context of WiFi. We deploy three main competing strategies that have been proposed for relaying, Amplify-and-Forward (AF), DF and QMF, on the WarpLab software radio platform. We present 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. We then present a more advanced architecture for physical layer cooperation (termed QUILT), that seamlessly adapts to the underlying network configuration to achieve competitive or better performance than the best current approaches. It combines on-demand, opportunistic use of DF or QMF followed by interleaving at the relay, with hybrid decoding at the destination that extracts information from even potentially undecodable received frames. We theoretically quantify how our design choices affect the system performance. We also deploy QUILT on WarpLab and show through over-the-air experiments up to $5$ times FER improvement over the next best cooperative protocol