No abstract

In this paper, we deal with the problem of opportunistic spectrum access (OSA) in infrastructure-less cognitive networks. Each secondary user (SU) Tx is allowed to select one frequency channel at each transmission trial. We assume that there is no information exchange between SUs, and they have no knowledge of channel quality, availability and other SUs actions, hence, each SU selfishly tries to select the best band to transmit. This particular problem is designed as a multiuser restless Markov multi-armed bandit (MAB) problem, in which multiple SUs collect a priori unknown reward by selecting a channel. The main contribution of the paper is to propose an online learning policy for distributed SUs, that takes into account not only the availability criterion of a band but also a quality metric linked to the interference power from the neighboring cells experienced on the sensed band. We also prove that the policy, named distributed restless QoS-UCB (RQoS-UCB), achieves at most logarithmic order regret, for a single-user in a first time and then for multiuser in a second time. Moreover, studies on the achievable throughput, average bit error rate obtained with the proposed policy are conducted and compared to well-known reinforcement learning algorithms.

Recent advances in information theory have provided achievability bounds and converses for the coding rate for the finite blocklength regime. In this paper, recent results on the non-asymptotic coding rate for fading channels with no channel state information at the transmitter are exploited to analyze the goodput in additive white Gaussian noise (AWGN) and the energy-efficiency spectral-efficiency (EE-SE) tradeoff where the fundamental relationship between the codeword length and the EE is given. Finally, the true outage probability in Ricean and Nakagami-m block fading channels is investigated and it is proved that the asymptotic outage capacity is the Laplace approximation of the average error probability in finite blocklength regime.