Riccardo Andreotti

A game theoretical approach for coded cooperation in cognitive radio networks

In this paper, the authors focus on a game theoretical approach for cooperation in cognitive radio (CR) networks. In particular, an integrated design framework which relies on the effective SNR methodology is proposed. The authors derive a distributed power allocation policy aimed at maximizing the reliability of a cooperative BIC OFDM link wherein pragmatic modulation and coding schemes are considered. More in detail, the cognitive devices adapt their power to enable efficient cooperation and coexistence between cognitive nodes and primary networks. First of all, the gain due to the cooperation protocol is analytically derived, resorting to a simple first order recursive equation that depends on the current channel conditions. Then, after an accurate formalization of the optimization problem, a distributed iterative solution based on a novel algorithm, named Successive Set Reduction, is proposed. In particular, the authors show that : i) the proposed power allocation policy takes into account the cooperative gain through a simple scalar value, named cooperative effective SNR; ii) it is effective in improving the packet error rate performance with respect to other conventional power allocation strategies, thus allowing a better coverage for the secondary network; iii) the convergence of the distributed algorithm has exponential speed and requires only local signaling between secondary users.

Goodput-Based Link Resource Adaptation for Reliable Packet Transmissions in BIC-OFDM Cognitive Radio Networks

Cognitive radio (CR) stands out as a potential cornerstone to break the spectrum gridlock through enabling the coexistence of licensed (primary) and unlicensed (secondary) users in the same bandwidth. This paper deals with a novel link resource adaptation (LRA) strategy to be applied in CR scenarios for reliable packet transmissions based on bit interleaved coded orthogonal frequency division multiplexing (BIC-OFDM). We first formulate the power allocation (PA) problem constrained by both the available power at the secondary transmitter (ST) and the interference tolerable at the primary receivers, aimed at maximizing the offered layer 3 data rate, i.e., the goodput (GP) metric. Then, we derive the optimal PA strategy resorting to the customary Lagrangian dual decomposition (LDD) technique, which, however, like many other conventional numerical methods, exhibits several drawbacks, such as slow convergence and need for parameter tuning. These restrictions are circumvented through the development of a novel iterative yet simple PA algorithm, referred to as successive set reduction (SSR) approach, whose optimality conditions are analytically demonstrated by resorting to the Quasi Variational Inequality (QVI) framework. Based on this PA algorithm, an adaptive modulation and coding (AMC) scheme at the ST is eventually derived. Simulation results over a realistic scenario corroborate the effectiveness of the proposed SSR-based AMC algorithm, highlighting the GP improvements over non-adaptive LRA techniques, besides a remarkable complexity reduction w.r.t. conventional numerical methods.

Resource allocation in OFDMA underlay cognitive radio systems based on Ant Colony Optimization

This paper1 deals with dynamic resource allocation problem for OFDMA-based cognitive radio systems. The proposed solution is specifically tailored for a secondary base stations (SBS) transmitting to secondary users (SUs) over the same bands of the licensed primary users (PUs) in underlay fashion. The downlink transmission goodput is thereby maximized while keeping the interference on the PUs within a tolerable range. The NP-hard goodput maximization problem is tackled resorting to an efficient meta-heuristic algorithm based on Ant Colony Optimization (ACO) framework.

Power-efficient distributed resource allocation under goodput QoS constraints for heterogeneous networks

This work proposes a distributed resource allocation (RA) algorithm for packet bit-interleaved coded OFDM transmissions in the uplink of heterogeneous networks (HetNets), characterized by small cells deployed over a macrocell area and sharing the same band. Every user allocates its transmission resources, i.e., bits per active subcarrier, coding rate, and power per subcarrier, to minimize the power consumption while both guaranteeing a target quality of service (QoS) and accounting for the interference inflicted by other users transmitting over the same band. The QoS consists of the number of information bits delivered in error-free packets per unit of time, or goodput (GP), estimated at the transmitter by resorting to an efficient effective SNR mapping technique. First, the RA problem is solved in the point-to-point case, thus deriving an approximate yet accurate closed-form expression for the power allocation (PA). Then, the interference-limited HetNet case is examined, where the RA problem is described as a non-cooperative game, providing a solution in terms of generalized Nash equilibrium. Thanks to the closed-form of the PA, the solution analysis is based on the best response concept. Hence, sufficient conditions for existence and uniqueness of the solution are analytically derived, along with a distributed algorithm capable of reaching the game equilibrium.

Goodput-maximizing resource allocation in cognitive Radio BIC-OFDM systems with DF relay selection

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.

Adaptive hybrid ARQ for goodput optimization in BIC-OFDM systems

In next generation wireless networks, high data rates under strict quality of service (QoS) constraints call for flexible radio interfaces capable of adapting their configuration on the fly to the time-varying operating environment. Motivated by this need, this paper first derives a simple link performance prediction model for bit interleaved coded orthogonal frequency division multiplexing (BIC-OFDM) systems using incremental redundancy (IR) hybrid automatic repeat request (HARQ) mechanisms. Then, an adaptive HARQ strategy is formulated whose aim is maximizing the goodput (GP) metric, i.e., the number of error-free information bits delivered to the user by unit of time, over the coding rate, the bit distribution and an on-off power allocation across the active subchannels. Simulation results corroborate the GP performance gains of the proposed approach compared with non-adaptive transmissions, while keeping the computational complexity at affordable levels.

Resource Allocation via Max–Min Goodput Optimization for BIC-OFDMA Systems

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.

Accurate modeling of the predicted κESM-based link performance metric for BIC-OFDM systems

Effective SNR mapping (ESM) is a powerful technique for optimizing the performance of orthogonal frequency division multiplexing (OFDM) based wireless systems. ESM transforms a vector of subcarrier SNRs into a scalar effective SNR, which represents the SNR that would yield the same error performance in an equivalent system operating over an additive white Gaussian noise (AWGN) channel. This technique significantly simplifies the development of adaptive coding and modulation (ACM) algorithms. As the distribution of the effective SNR, when only imperfect and outdated channel state information (CSI) is available, cannot be expressed in closed form, we develop an approximate statistical model which is based on the beta distribution. Our model is compared in terms of approximation accuracy against models based on the Gaussian distribution, the gamma distribution and the more complex Pearson and generalized extreme value (GEV) distributions.

Distributed power control over interference channels using ACK/NACK feedback

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.

Cross-layer link adaptation for goodput optimization in MIMO BIC-OFDM systems

This work proposes a novel cross-layer link performance prediction (LPP) model and link adaptation (LA) strategy for soft-decoded multiple-input multiple-output (MIMO) bit-interleaved coded orthogonal frequency division multiplexing (BIC-OFDM) systems employing hybrid automatic repeat request (HARQ) protocols. The derived LPP, exploiting the concept of effective signal-to-noise ratio mapping (ESM) to model system performance over frequency-selective channels, does not only account for the actual channel state information at the transmitter and the adoption of practical modulation and coding schemes (MCSs), but also for the effect of the HARQ mechanism with bit-level combining at the receiver. Such method, named aggregated ESM, or αESM for short, exhibits an accurate performance prediction combined with a closed-form solution, enabling a flexible LA strategy, that selects at every protocol round the MCS maximizing the expected goodput (EGP), i.e., the number of correctly received bits per unit of time. The analytical expression of the EGP is derived capitalizing on the αESM and resorting to the renewal theory. Simulation results carried out in realistic wireless scenarios corroborate our theoretical claims and show the performance gain obtained by the proposed αESM-based LA strategy when compared with the best LA algorithms proposed so far for the same kind of systems.