Paolo Del Fiorentino

Distributed Dynamic Resource Allocation for Cooperative Cognitive Radio Networks With Multi-Antenna Relay Selection

A cognitive radio scenario is considered where the signals transmitted by a secondary user (SU) are relayed by multi-antenna relays using an amplify-and-forward cooperation protocol. In this paper, the optimal power allocation and beamforming scheme is derived for the SU transmitters (SU-TXs), which minimizes the exact outage probability of the SU network with relay selection, under both a transmit power constraint and a constraint on the interference power generated at every primary user receiver. After deriving the optimal structure of the beamforming matrix, several distributed resource allocation algorithms are obtained for different levels of channel state information (CSI) at the SU-TXs: perfect CSI and imperfect CSI are considered, along with the case where only channel distribution information is available. The numerical results show that the multi-antenna relays can significantly improve the performance of the SU network, which would otherwise be severely limited by the harsh interference constraints. Furthermore, we also investigate how the number of relays, the number of antennas, and the level of CSI impact the performance of the SU network. Finally, we point out that the proposed algorithms outperform several algorithms presented in the literature.

A robust resource allocation algorithm for packet BIC-UFMC 5G wireless communications

In this paper, we present a novel resource allocation (RA) algorithm for packet Universal Filtered Multi-Carrier (UFMC) BIC-based communications, the latter being a novel modulation format possibly envisaged to be applied in 5G wireless systems. Assuming the perfect knowledge of the channel and capitalizing on the specific UFMC signal waveform, the proposed RA strategy optimizes the coding rate and bit loading within the overall bandwidth along with the per-subband power distribution. In the presence of a carrier offset and over fading selective channels, the results we obtained are twofold: i) the UFMC format reveals to be more robust than the conventional OFDM scheme; ii) the performance of the UFMC system itself is further boosted by the optimal choice of radio resources evaluated by the proposed RA algorithm.

Resource allocation for multicarrier cooperative cognitive radio networks with imperfect channel state information

In this paper we consider a decode-and-forward cooperative network, which is active in the same bandwidth as a primary user network. The cooperative or secondary user network applies a dynamic resource allocation algorithm to maximize its own performance while limiting the interference caused at the primary user receivers. The main contribution of this paper is the proposition of a new accurate approximation for the outage probability, which takes into account that the channel state information available at the transmitter is imperfect and outdated. The accuracy of the approximation and the performance of the resource allocation scheme are validated through numerical simulations.

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.

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.