In this paper, we study the relay selection problem for a finite buffer-aided decode-and-forward cooperative wireless network. A relay selection policy that fully exploits the flexibility offered by the buffering ability of the relay nodes in order to maximize the achieved diversity gain is investigated. This new scheme incorporates the instantaneous strength of the wireless links as well as the status of the finite relay buffers and adapts the relay selection decision on the strongest available link by dynamically switching between relay reception and transmission. In order to analyse the new relay selection policy in terms of outage probability and diversity gain, a theoretical framework that models the evolution of the relay buffers as a Markov chain (MC) is introduced. The construction of the state transition matrix and the related steady state of the MC are studied and their impact on the derivation of the outage probability is investigated. We show that the proposed relay selection scheme significantly outperforms conventional relay selection policies for all cases and ensures a diversity gain equal to two times the number of relays for large buffer sizes.

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Abstract-There is emerging interest in more detailed models for wireless shadowing, which may include nonconstant shadowing variance, non-lognormal shadowing, and, most importantly, correlation between paths; we focus on this last aspect. This paper offers a structured synthesis of the existing literature on autocorrelation and cross-correlation in wireless shadowing and attempts to fill existing gaps in the analysis of correlation models. We make a survey of these models and argue, as has previously been observed, that certain models are not mathematically feasible, which may lead to problems in simulations or analysis. We then state some theorems that test whether the models are positive semidefinite, which is the central necessary condition for feasibility, and evaluate the existing models accordingly. Additionally, we evaluate the models according to their physical plausibility, which leads us to choose one model among many as arguably the best one in existence so far. This paper should be useful as a guide on how to implement shadowing correlation in one's work, how to choose an appropriate correlation model, and how to modify existing models or create new models so that they fulfill mathematical feasibility.

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