We consider the problem of dynamic spectrum access (DSA) in cognitive wireless networks, consisting of primary users (PUs) and secondary users (SUs), where only partial observations are available at the SUs due to narrowband sensing and transmissions. The network operates in a time-slotted regime, where the traffic patterns of the PUs are modeled as finite-memory Markov chains, that are unknown to the SUs. Since observations are partial, then both channel sensing and access actions affect the throughput. Focusing on the case in which there is a single SU, our objective is to maximize the SU’s long-term throughput. To that aim, we develop a novel algorithm that learns both access and sensing policies via deep Q-learning, dubbed Double Deep Q-network for Sensing and Access (DDQSA). To the best of our knowledge, this is the first work that jointly optimizes both sensing and access policies for DSA via deep Q-learning. Next, we consider wireless networks with access policy which implements a fixed channel hopping dynamics, for which we analytically determine the optimal SU sensing and access policy and its associated throughput. Then, we demonstrate that indeed, the proposed DDQSA algorithm can achieve near-optimal performance for the considered network. Our results show that the proposed DDQSA algorithm learns a policy that implements both sensing and channel access, which significantly outperforms existing approaches, and can achieve the optimal performance in certain scenarios.

One of the major factors which limits the throughput in wireless communications networks is the accuracy of time synchronization between the nodes in the network. Synchronization methods based on pulse-coupled oscillators (PCOs) have the advantage of simple implementation and achieve high accuracy when the nodes are closely located. However, such schemes tend to have poor synchronization performance for distant nodes, as well as in the presence of clock frequency offsets between the nodes. In this paper we present a novel PCO-based Deep neural network (DDN)-Aided Synchronization Algorithm coined DASA. We design DASA as a novel low-complexity and interpretable architecture by converting classic PCO-based synchronization into a trainable discriminative model . To enable DASA to operate in dynamic settings, we propose a novel, unsupervised, distributed, fast online training scheme which is able to train DASA within a few sampling instances, locally , thereby avoiding the need for information exchange between the nodes or for a central node for coordination. DASDA is demonstrated to achieve an improvement by a factor greater than ten compared to the classic reference scheme. Lastly, we propose another novel, distributed offline training scheme for DASA, which is demonstrated to offer a tradeoff between performance and simplicity of deployment compared to the online training scheme, yet, at the same time, DASA with offline training still achieves superior performance compared to the classic reference scheme.

In many communications scenarios the channel exhibits periodic characteristics. Major examples include power line communications and interference-limited communications. Additionally, certain approximations for mobile radio channels over finite time intervals may result in periodic channel models. In this work we study pilot-aided joint estimation of the channel impulse response (CIR) and of the carrier frequency offset (CFO) for linear periodic channels in which both the CIR and the noise statistics vary periodically in time. We first consider the joint maximum likelihood estimator (JMLE) for the CIR and the CFO, and discuss the practical drawbacks associated with this estimator. When the coefficients of the delay Doppler spread function of the CIR are approximately sparse, we propose two estimation schemes with higher spectral efficiency and lower computational complexity compared to the JMLE, obtained by exploiting both the periodicity and the sparsity of the channel, without requiring a-priori knowledge of the sparsity pattern. Finally, we study the design of pilot sequences aimed at improving the estimation performance in sparse periodic channels. Simulation studies corresponding to practical scenarios of the proposed estimators demonstrate that substantial benefits can be obtained by properly accounting for the sparsity and periodicity in the design of the estimation scheme.

The interference channel with a relay (ICR) is the fundamental building block of cooperation in wireless networks where there are multiple communicating pairs interfering with each other. This paper considers ICRs in which the links are subject to i.i.d. fading, and each node has channel state information (CSI) only on its incoming links (receive CSI). Two channel models are considered: phase fading and Rayleigh fading. Strong interference conditions are derived for the case where the links from the sources to the relay are good in the sense that the achievable region for decoding both messages at the relay contains the maximal achievable region at the destinations. This leads to the characterization of the capacity region for such scenarios. This is the first time the capacity region of the ICR is characterized for a nondegraded, noncognitive scenario, with a causal relay when all links are active.

In phase retrieval problems, a signal of interest (SOI) is reconstructed based on the magnitude of a linear transformation of the SOI observed with additive noise. The linear transform is typically referred to as a measurement matrix. Many works on phase retrieval assume that the measurement matrix is a random Gaussian matrix, which, in the noiseless scenario with sufficiently many measurements, guarantees invertability of the transformation between the SOI and the observations, up to an inherent phase ambiguity. However, in many practical applications, the measurement matrix corresponds to an underlying physical setup, and is therefore deterministic, possibly with structural constraints. In this paper, we study the design of deterministic measurement matrices, based on maximizing the mutual information between the SOI and the observations. We characterize necessary conditions for the optimality of a measurement matrix, and analytically obtain the optimal matrix in the low signal-to-noise ratio regime. Practical methods for designing general measurement matrices and masked Fourier measurements are proposed. Simulation tests demonstrate the performance gain achieved by the suggested techniques compared to random Gaussian measurements for various phase recovery algorithms.

We study the transmission of correlated sources over discrete memoryless (DM) multiple-access-relay channels (MARCs), in which both the relay and the destination have access to side information arbitrarily correlated with the sources. As the optimal transmission scheme is an open problem, in this paper, we propose a new joint source-channel coding scheme based on a novel combination of the correlation preserving mapping (CPM) technique with Slepian-Wolf (SW) source coding, and obtain the corresponding sufficient conditions. The proposed coding scheme is based on the decode-and-forward strategy, and utilizes CPM for encoding information simultaneously to the relay and the destination, whereas the cooperation information from the relay is encoded via SW source coding. It is shown that there are cases in which the new scheme strictly outperforms the schemes available in the literature. This is the first instance of a source-channel code that uses CPM for encoding information to two different nodes (relay and destination). In addition to sufficient conditions, we present three different sets of single-letter necessary conditions for reliable transmission of correlated sources over DM MARCs. The newly derived conditions are shown to be at least as tight as the previously known necessary conditions.