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This paper proposes the integration of non-orthogonal multiple access (NOMA) and broadcasting with buffer-aided (BA) full-duplex (FD) relaying to improve the spectral efficiency of a single-source two-user network. Furthermore, opportunistic relay selection is employed, activating one or two FD relays to support the end-to-end communication. The network performance is improved through buffers, allowing to switch to half-duplex (HD) transmission/reception when FD operation is not feasible. A hybrid BA FD/HD NOMA relay selection algorithm is proposed, herein called $\mathop {\mathrm{BAHyNOMA}}$ , orchestrating the three aforementioned techniques, towards a simple yet efficient and robust relay selection. $\mathop {\mathrm{BAHyNOMA}}$ is evaluated in terms of outage probability, average sum-rate and average delay, and it is observed that the average sum-rate can be doubled, while maintaining low latency. [ABSTRACT FROM AUTHOR]

We consider the problems of computing the average degree and the size of a given network in a distributed fashion and under quantized communication. More specifically, we present two distributed algorithms, which rely on quantized operation (i.e., nodes process and transmit quantized messages) and are able to obtain the exact solutions in a finite number of steps. During the operation of our algorithms, each node can determine in a distributed manner whether convergence has been achieved and correspondingly terminate its operation. For terminating the operation of our algorithms, we assume a known bound for the network diameter. To the best of the authors' knowledge, these algorithms are the first to find exact solutions (i.e., with no error in the final result) under quantized communication. Note that our network size calculation algorithm is the first in the literature to calculate the exact size of a network in a finite number of steps without introducing a final error; in other algorithms, this error can be either due to quantization or asymptotic convergence. In our case, no error is introduced since the desired result is calculated in the form of a fraction involving an integer numerator and an integer denominator. We demonstrate the operation of our algorithms and their potential advantages through simulations. [ABSTRACT FROM AUTHOR]

Buffer-aided relays can improve the diversity of multi-hop networks, however, they increase packet delays. Thus, various delay-aware protocols have been developed, but without considering the transmission diversity. Moreover, most works adopt ideal assumptions, such as symmetric links, perfect channel state information (CSI), and error-free feedback channels. So, we propose a low-complexity (LoCo) link selection algorithm, herein called $\mathop {\mathrm {LoCo\!\!-\!\!Link}}$. The proposed algorithm may experience delays during CSI updates, and hence, by using outdated CSI its performance may deteriorate. To alleviate this issue, we next propose a distributed version of $\mathop {\mathrm {LoCo\!\!-\!\!Link}}$ ($\mathop {\mathrm {d\!\!-\!\!LoCo\!\!-\!\!Link}}$) dealing with outdated CSI. In both algorithms, the source performs broadcasting toward multiple relays; when the packets are transmitted by a relay to the destination, they are discarded from all other relays. This coordination relies on feedback channels. For non error-free feedback channels, we propose a scheme in which the relays listen to the transmission of the best relay and drop duplicate packets. Results show that $\mathop {\mathrm {LoCo\!\!-\!\!Link}}$ surpasses other algorithms, by decreasing the delay in asymmetric networks. Moreover, $\mathop {\mathrm {d\!\!-\!\!LoCo\!\!-\!\!Link}}$ avoids diversity losses due to outdated CSI, while the effect of non error-free feedback channels is mitigated by taking advantage of the inter-relay channels. [ABSTRACT FROM AUTHOR]

The aim of this paper is to address optimality of stochastic control strategies via dynamic programming subject to total variation distance ambiguity on the conditional distribution of the controlled process. We formulate the stochastic control problem using minimax theory, in which the control minimizes the payoff while the conditional distribution, from the total variation distance set, maximizes it. First, we investigate the maximization of a linear functional on the space of probability measures on abstract spaces, among those probability measures which are within a total variation distance from a nominal probability measure, and then we give the maximizing probability measure in closed form. Second, we utilize the solution of the maximization to solve minimax stochastic control with deterministic control strategies, under a Markovian and a non-Markovian assumption, on the conditional distributions of the controlled process. The results of this part include (1) minimax optimization subject to total variation distance ambiguity constraint; (2) new dynamic programming recursions, which involve the oscillator seminorm of the value function, in addition to the standard terms; and (3) a new infinite horizon discounted dynamic programming equation, the associated contractive property, and a new policy iteration algorithm. Finally, we provide illustrative examples for both the finite and infinite horizon cases. For the infinite horizon case, we invoke the new policy iteration algorithm to compute the optimal strategies. [ABSTRACT FROM AUTHOR]

Abstract: In this note we prove the unconditional stability of the Foschini–Miljanic algorithm. Our results show that the Foschini–Miljanic algorithm is unconditionally stable (convergent) even in the presence of bounded time-varying communication delays, and in the presence of topology changes. The implication of our results may be important for the design of Code Division Multiple Access (CDMA) based wireless networks. [Copyright &y& Elsevier]