Marius Pesavento

Power Allocation and Beamforming in Overlay Cognitive Radio Systems

We consider the overlay cognitive radio channel where the cognitive user is admitted to transmit simultaneously with the primary user provided that the instantaneous rate of primary link is not degraded. Assuming causal knowledge of the primary users message at the cognitive transmitter (CT), we analyze the transmission rates of the cognitive user in the single-input multiple-output (SIMO) and the multiple-input single-output (MISO) configurations. In particular, the CT uses a part of its transmit power to assist the primary user in delivering the primary users message and the other part of its power is used to deliver its own message. The transmit power and the beamformers are designed to maximize the rate of the cognitive user while fixing the the rate of the primary user. Our simulation results show that the proposed scheme yields improved system performance compared to the interweave scheme where any interference to the primary user is prohibited.

Joint Network Optimization and Downlink Beamforming for CoMP Transmissions Using Mixed Integer Conic Programming

Coordinated multipoint (CoMP) transmission is a promising technique to mitigate intercell interference and to increase system throughput in single-frequency reuse networks. Despite the remarkable benefits, the associated operational costs for exchanging user data and control information between multiple cooperating base stations (BSs) limit practical applications of CoMP processing. To facilitate wide usage of CoMP transmission, we consider in this paper the problem of joint network optimization and downlink beamforming (JNOB), with the objective to minimize the overall BS power consumption (including the operational costs of CoMP transmission) while guaranteeing the quality-of-service (QoS) requirements of the mobile stations (MSs). We address this problem using a mixed integer second-order cone program (MI-SOCP) framework and develop an extended MI-SOCP formulation that admits tighter continuous relaxations, which is essential for reducing the computational complexity of the branch-and-cut (BnC) method. Analytic studies of the MI-SOCP formulations are carried out. Based on the analyses, we introduce efficient customizing strategies to further speed up the BnC algorithm through generating tight lower bounds of the minimum total BS power consumptions. For practical applications, we develop polynomial-time inflation and deflation procedures to compute high-quality solutions of the JNOB problem. Numerical results show that the inflation and deflation procedures yield total BS power consumptions that are close to the lower bounds, e.g., exceeding the lower bounds by about 12.9% and 9.0%, respectively, for a network with 13 BSs and 25 MSs. Simulation results also show that minimizing the total BS power consumption results in sparse network topologies and reduced operational overhead in CoMP transmission and that some of the BSs are switched off when possible.

Joint Optimization of Source Power Allocation and Distributed Relay Beamforming in Multiuser Peer-to-Peer Relay Networks

In this paper, we consider the joint optimization of the source power allocation and relay beamforming weights in distributed multiuser peer-to-peer (MUP2P) relay networks applying the amplify-and-forward (AF) protocol. We adopt a quality-of-service (QoS) based approach, in which the total power transmitted from all sources and relays is minimized while guaranteeing the prescribed QoS requirement of each source-destination pair. The QoS is modeled as a function of the receive signal-to-interference-plus-noise ratio (SINR) at the destinations. Unlike the existing contributions, the transmitted powers of the sources and the beamforming weights of the relays are optimized jointly in this paper. Introducing an appropriate transformation of variables, the QoS based source power allocation and distributed relay beamforming (PADB) problem can be equivalently transformed into a difference of convex (DC) program, which can be efficiently solved with local optimality using the constrained concave convex procedure (CCCP). Based on this procedure, we also propose an iterative feasibility search algorithm (IFSA) to find an initial feasible point of the DC program. The analytic study of the proposed solution confirms that it converges to a local optimum of the PADB problem. Numerical results show that our solution outperforms (in terms of the total transmitted power) the alternating optimization procedure and the exact penalty based DC algorithm. In addition, the proposed IFSA outperforms the alternating optimization algorithm in finding feasible points of the DC program (i.e., the equivalence of the PADB problem).

Multidimensional rank reduction estimator for parametric MIMO channel models

A novel algebraic method for the simultaneous estimation of MIMO channel parameters from channel sounder measurements is developed. We consider a parametric multipath propagation model with discrete paths where each path is characterized by its complex path gain, its directions of arrival and departure, time delay, and Doppler shift. This problem is treated as a special case of the multidimensional harmonic retrieval problem. While the well-known ESPRIT-type algorithms exploit shift-invariance between specific partitions of the signal matrix, the rank reduction estimator (RARE) algorithm exploits their internal Vandermonde structure. A multidimensional extension of the RARE algorithm is developed, analyzed, and applied to measurement data recorded with the RUSK vector channel sounder in the 2 GHz band.

Distributed Beamforming for Multi-Group Multicasting Relay Networks

We generalize the concept of multiuser peer-to-peer (MUP2P) relay networks, where K source-destination pairs communicate through L relays, to that of a multi-group multicasting (MGM) relay network. In the MGM scenario, each source may broadcast its message to a group of multiple users rather than to a single user only. Common distributed beamformer designs for MUP2P relay networks aim to minimize the total transmitted relay power subject to receiver quality-of-service (QoS) constraints. In state-of-the-art techniques, the resulting nonconvex problem is approximated by a convex one which is efficiently solvable using interior point methods. These techniques are shown to be straightforwardly extendable to more general MGM relay networks. However, as the number of receivers increases (which may be typical for the proposed MGM networks where each multicast group may contain many users), these approximations become more and more inaccurate leading to severe performance degradation and problem infeasibility. To avoid this drawback, we propose an iterative method where in each iteration, a convex approximation of the original problem is solved and then adapted to the solution obtained in this iteration. We show that the approximate solution can be successively improved using such iterations. Simulation results show that in scenarios with large numbers of destination users, the proposed method substantially outperforms the state-of-the-art methods developed for MUP2P relay networks.

One- and two-dimensional direction-of-arrival estimation: An overview of search-free techniques

One of major challenges in applying traditional subspace-based direction finding techniques to real-time practical problems is in that they normally require an exhaustive spectral search over the angular parameter(s). Therefore, methods avoiding such a computationally demanding spectral search step are of great interest. In this paper, an overview of one- and two-dimensional search-free direction-of-arrival (DOA) estimation methods is presented. Both cases of uniform and non-uniform sensor arrays are addressed.

Joint Transceiver Beamforming in MIMO Cognitive Radio Network Via Second-Order Cone Programming

This paper considers the spectrum sharing multiple- input-multiple-output (MIMO) cognitive radio network, in which multiple primary users (PUs) coexist with multiple secondary users (SUs). Joint transceiver cognitive beam former design is introduced to minimize the transmit power of the SU base station (SBS) while simultaneously targeting lower bounds on the received signal-to-interference-plus-noise ratio (SINR) for the SUs and imposing upper limits on the interference temperature to the PUs. With the perfect knowledge of all links, the optimal secondary transceiver beam former is achieved iteratively. Due to the limited cooperation between SBS and PUs, perfect information of primary links may not be available at SBS which could lead to severe interference to the PUs. Robust designs are developed against the uncertainties in the primary links by keeping the interference to the PU below a prespecifled threshold with high probability. Simulation results are presented to validate the effectiveness of the proposed algorithms that minimizes the total transmit power and simultaneously guarantees quality-of-service (QoS) of both SUs and PUs.

Robust Downlink Beamforming for Cognitive Radio Networks

We address the problem of worst-case robust downlink beamforming for a multi-antenna secondary network (SN) in a cognitive radio framework. An important issue is the interference leaked to the primary users (PUs) resulting from the transmission between the SN base station and the secondary users (SUs). Our aim is to provide the SUs with a minimum acceptable quality-of-service (QoS), while keeping the interference to the PUs below a given threshold. Previous solutions for this scenario involve several coarse approximations. Here we avoid these approximations and obtain an exact reformulation of the worst-case problem using Lagrange duality. Finally, we use semidefinite relaxation (SDR) to convert the resulting problem to a convex form. Computer simulations show that the SDR step does not involve any approximation as the resulting solution is always rank-one.

Direction of arrival estimation in uniform circular arrays composed of directional elements

We address the problem of estimating the azimuth and elevation angles of multiple far-field signals in uniform circular arrays (UCAs) composed of identical sensor elements. Unlike previous search-free approaches, which strongly rely on the omni-directional element assumption, the element pattern is modelled as direction dependent. We have developed the UCA-RARE algorithm, a new computationally efficient eigenstructure-based estimation method operating in beamspace. The algorithm allows estimation of the azimuth parameters of the source directions decoupled from the elevation parameters without employing exact knowledge of the element pattern in a search-free procedure. The corresponding elevation angles are estimated in a subsequent step using a closed-form algorithm.

Iterative dual downlink beamforming for cognitive radio networks

We address the problem of multi-user downlink beamforming and power allocation in a cognitive radio (CR) secondary network (SN) with constraints on the total interference in the primary network (PN). We derive the Lagrange dual of the problem and show that both problems are equivalent. Two algorithms are proposed to solve the problem. The first is based on convex optimization and the second algorithm exploits the uplink-downlink duality that is enforced by the introduction of appropriate slack variables in the constrained optimization problem. This leads to a simple iterative technique that enjoys easy implementation and low computational costs. Simulation results illustrate that the proposed iterative technique converges to the global optimum in all cases.