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In this work, we derive the random coding error exponent for the uplink phase of a two-way relay system where physical layer network coding (PNC) is employed. The error exponent is derived for the practical (yet sub-optimum) XOR channel decoding setting. We show that the random coding error exponent under optimum (i.e., maximum likelihood) PNC channel decoding can be achieved even under the sub-optimal XOR channel decoding. The derived achievability bounds provide us with valuable insight and can be used as a benchmark for the performance of practical channel-coded PNC systems employing low complexity decoders when finite-length codewords are used., Comment: Submitted to IEEE International Symposium on Information Theory (ISIT), 2017

This paper investigates practical 5G strategies for power-balanced non-orthogonal multiple access (NOMA). By allowing multiple users to share the same time and frequency, NOMA can scale up the number of served users and increase spectral efficiency compared with existing orthogonal multiple access (OMA). Conventional NOMA schemes with successive interference cancellation (SIC) do not work well when users with comparable received powers transmit together. To allow power-balanced NOMA (more exactly, near power-balanced NOMA), this paper investigates a new NOMA architecture, named Network-Coded Multiple Access (NCMA). A distinguishing feature of NCMA is the joint use of physical-layer network coding (PNC) and multiuser decoding (MUD) to boost NOMA throughputs. We first show that a simple NCMA architecture in which all users use the same modulation, referred to as rate-homogeneous NCMA, can achieve substantial throughput improvement over SIC-based NOMA under near power-balanced scenarios. Then, we put forth a new NCMA architecture, referred to as rate-diverse NCMA, in which different users may adopt different modulations commensurate with their relative SNRs. A challenge for rate-diverse NCMA is the design of a channel-coded PNC system. This paper is the first attempt to design channel-coded rate-diverse PNC. Experimental results on our software-defined radio prototype show that the throughput of rate-diverse NCMA can outperform the state-of-the-art rate-homogeneous NCMA by 80%. Overall, rate-diverse NCMA is a practical solution for near power-balanced NOMA., Comment: 36 pages, full length version. arXiv admin note: substantial text overlap with arXiv:1610.00857

In asynchronous physical-layer network coding (APNC) systems, the symbols from multiple transmitters to a common receiver may be misaligned. The knowledge of the amount of symbol misalignment, hence its estimation, is important to PNC decoding. This paper addresses the problem of symbol-misalignment estimation and the problem of optimal PNC decoding given the misalignment estimate, assuming the APNC system uses the root-raised-cosine pulse to carry signals (RRC-APNC). First, we put forth an optimal symbol-misalignment estimator that makes use of double baud-rate samples. Then, we devise optimal decoders for RRC-APNC in the presence of inaccurate symbol-misalignment estimates. In particular, we present a new whitening transformation to whiten the noise of the double baud-rate samples. Finally, we investigate the decoding performance of various estimation-and-decoding schemes for RRC-APNC. Extensive simulations show that: (i) Our double baud-rate estimator yields substantially more accurate symbol-misalignment estimates than the baud-rate estimator does. The mean-square-error (MSE) gains are up to 8 dB. (ii) An overall estimation-and-decoding scheme in which both estimation and decoding are based on double baud-rate samples yields much better performance than other schemes. Compared with a scheme in which both estimation and decoding are based on baud-rate samples), the double baud-rate sampling scheme yields 4.5 dB gains on symbol error rate (SER) performance in an AWGN channel, and 2 dB gains on packet error rate (PER) performance in a Rayleigh fading channel., Comment: 39 pages, full length version

This paper investigates the use of deep reinforcement learning (DRL) in a MAC protocol for heterogeneous wireless networking referred to as Deep-reinforcement Learning Multiple Access (DLMA). The thrust of this work is partially inspired by the vision of DARPA SC2, a 3-year competition whereby competitors are to come up with a clean-slate design that "best share spectrum with any network(s), in any environment, without prior knowledge, leveraging on machine-learning technique". Specifically, this paper considers the problem of sharing time slots among a multiple of time-slotted networks that adopt different MAC protocols. One of the MAC protocols is DLMA. The other two are TDMA and ALOHA. The nodes operating DLMA do not know that the other two MAC protocols are TDMA and ALOHA. Yet, by a series of observations of the environment, its own actions, and the resulting rewards, a DLMA node can learn an optimal MAC strategy to coexist harmoniously with the TDMA and ALOHA nodes according to a specified objective (e.g., the objective could be the sum throughput of all networks, or a general alpha-fairness objective).