The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access. We consider decentralized contention-based medium access for base stations (BSs) operating on unlicensed shared spectrum, where each BS autonomously decides whether or not to transmit on a given resource. The contention decision attempts to maximize not its own downlink throughput, but rather a network-wide objective. We formulate this problem as a decentralized partially observable Markov decision process with a novel reward structure that provides long term proportional fairness in terms of throughput. We then introduce a two-stage Markov decision process in each time slot that uses information from spectrum sensing and reception quality to make a medium access decision. Finally, we incorporate these features into a distributed reinforcement learning framework for contention-based spectrum access. Our formulation provides decentralized inference, online adaptability and also caters to partial observability of the environment through recurrent Q-learning. Empirically, we find its maximization of the proportional fairness metric to be competitive with a genie-aided adaptive energy detection threshold, while being robust to channel fading and small contention windows.

Precise positioning in 5G that can enable a wide variety of new use cases. We investigate the problem of accurate line-of-sight (LOS) delay estimation of an observed wireless channel using deep neural networks (NN). These delay estimates are the primary building block for deriving accurate position estimates. Our work proposes a custom super-resolution NN that exploits the properties of the wireless channel to guide the NN design. We compare against traditional algorithms used for LOS detection and show that the proposed NN shows excellent performance in the presence of weak LOS signals and dense multipath; scenarios that are challenging for traditional signal processing algorithms.

This paper provides new insights on the convergence of a locally connected network of pulse coupled oscillator (PCOs) (i.e., a bioinspired model for communication networks) to synchronous and desynchronous states, and their implication in terms of the decentralized synchronization and scheduling in communication networks. Bioinspired techniques have been advocated by many as fault-tolerant and scalable alternatives to produce self-organization in communication networks. The PCO dynamics, in particular, have been the source of inspiration for many network synchronization and scheduling protocols. However, their convergence properties, especially in locally connected networks, have not been fully understood, prohibiting the migration into mainstream standards. This paper provides further results on the convergence of PCOs in locally connected networks and the achievable convergence accuracy under propagation delays. For synchronization, almost sure convergence is proved for three nodes and accuracy results are obtained for general locally connected networks, whereas for scheduling (or desynchronization), results are derived for locally connected networks with mild conditions on the overlapping set of maximal cliques. These issues have not been fully addressed before in the literature.

The pulse-coupled synchronization and scheduling (PulseSS) protocol is proposed in this paper for simultaneous synchronization and scheduling of communication activities in clustered wireless sensor networks (WSNs), by emulating the emergent behavior of pulse-coupled oscillator (PCO) networks in mathematical biology. Different from existing works that address synchronization and scheduling (i.e., desynchronization) separately, PulseSS provides a coordination signaling mechanism that achieves decentralized network synchronization and time division multiple access scheduling simultaneously at different time scales for clustered WSNs. Here, we assume that the nodes are connected only locally via their respective cluster heads. Moreover, PulseSS addresses the issue of propagation delays, that may plague the accuracy of PCO synchronization in practice, by providing ways to estimate and precompensate for these values locally at the sensors (i.e., PCOs). At the same time the protocol retains the adaptivity and light-weight nature of PCO protocols both in terms of signaling and computations. Simulations of both physical and medium access control layers show a synchronization accuracy of factions of microseconds above 15 dB of signal to interference and noise ratio for a five cluster network. A hardware implementation of PulseSS using TinyOS is also provided to corroborate the real world applicability of our protocol.