In this paper, a novel resource allocation (RA) strategy is designed for the downlink of orthogonal frequency division multiple access networks employing practical modulation and coding under quality of service constraints and retransmission techniques. Compared with previous works, two basic concepts are combined together, namely: 1) taking the goodput (GP) as performance metric and 2) ensuring maximum fairness among users. Thus, the resulting RA maximizes the GP of the worst users, optimizing subcarrier allocation (SA), per-subcarrier power allocation (PA), and adaptation of modulation and coding (AMC) of the active users, yielding a nonlinear nonconvex mixed optimization problem (OP). The intrinsic demanding difficulty of the OP is tackled by iteratively and optimally solving the AMC, PA, and SA subproblems, devoting special care to the difficult nonlinear combinatorial SA-OP. First, the optimal (yet computationally complex) solution is found by applying the branch and bound method to the optimal SA solution found in the relaxed domain, and accordingly, it is taken as benchmark. Then, an innovative suboptimal yet efficient solution based on the metaheuristic ant colony optimization (ACO) framework is derived. The proposed RA strategy is corroborated by comprehensive simulations, showing improved performance even at the cost of affordable numerical complexity.

We propose a novel resource allocation (RA) strategy for a cognitive radio packet-oriented bit-interleaved coded orthogonal frequency division multiplexing (BIC-OFDM) system with decode-and-forward (DF) relays. The aim of the RA is maximizing the goodput (GP) of the source-relay-destination link, which is the number of information bits correctly received at the destination node per unit of time. Therefore, we derive an accurate analytic approximation for this figure of merit, which allows us to find the optimum constellation size, code rate and energy allocation per subcarrier. Further, this expression also serves as a novel relay selection criterion. Finally, we validate the proposed RA method, and compare its performance to capacitymaximizing algorithms through numerical simulations.

In this work, we consider a network composed of several single-antenna transmitter-receiver pairs in which each pair aims at selfishly minimizing the power required to achieve a given signal-to-interference-plus-noise ratio. This is obtained modeling the transmitter-receiver pairs as rational agents that engage in a non-cooperative game. Capitalizing on the well-known results on the existence and structure of the generalized Nash equilibrium (GNE) point of the underlying game, a low complexity, iterative and distributed algorithm is derived to let each terminal reach the GNE using only a limited feedback in the form of link-layer acknowledgements (ACK) or negative acknowledgements (NACK). Numerical results are used to prove that the proposed solution is able to achieve convergence in a scalable and adaptive manner under different operating conditions.

In this work, we investigate the power allocation (PA) problem aimed at minimizing the users' packet error rate (PER) over a noncooperative link, i.e., a link where the set of users, employing packet-oriented bit-interleaved coded (BIC) orthogonal frequency division multiplexing (OFDM) systems, compete for the same bandwidth. For these kind of systems, the PER is not available in closed-form, but a very efficient solution is offered by the effective SNR mapping (ESM) technique. This method allows each user to evaluate a single scalar value, the effective SNR (ESNR), accounting for all the SNIRs experienced over the active subcarriers, and to univocally map it into a PER value. Thus, in order to derive a decentralized strategy allowing each user to minimize its own PER, the problem is described as a strategic game, called min-PER game, with the set of player, utilities and strategies represented by the competitive users, the ESNRs and the set of feasible power allocations, respectively. We will show both the existence of at least one Nash Equilibrium (NE) for the min-PER game and its equivalence with a Nonlinear Variational Inequality (NVI) problem. Finally, relying on the theory of contraction mappings, we will derive a distributed algorithm to reach the NE of the game.