Ali Grami

Distributed Beamforming for Relay Networks Based on Second-Order Statistics of the Channel State Information

In this paper, the problem of distributed beamforming is considered for a wireless network which consists of a transmitter, a receiver, and relay nodes. For such a network, assuming that the second-order statistics of the channel coefficients are available, we study two different beamforming design approaches. As the first approach, we design the beamformer through minimization of the total transmit power subject to the receiver quality of service constraint. We show that this approach yields a closed-form solution. In the second approach, the beamforming weights are obtained through maximizing the receiver signal-to-noise ratio (SNR) subject to two different types of power constraints, namely the total transmit power constraint and individual relay power constraints. We show that the total power constraint leads to a closed-form solution while the individual relay power constraints result in a quadratic programming optimization problem. The later optimization problem does not have a closed-form solution. However, it is shown that using semidefinite relaxation, this problem can be turned into a convex feasibility semidefinite programming (SDP), and therefore, can be efficiently solved using interior point methods. Furthermore, we develop a simplified, thus suboptimal, technique which is computationally more efficient than the SDP approach. In fact, the simplified algorithm provides the beamforming weight vector in a closed form. Our numerical examples show that as the uncertainty in the channel state information is increased, satisfying the quality of service constraint becomes harder, i.e., it takes more power to satisfy these constraints. Also our simulation results show that when compared to the SDP-based method, our simplified technique suffers a 2-dB loss in SNR for low to moderate values of transmit power.

Joint Receive-Transmit Beamforming for Multi-Antenna Relaying Schemes

In this correspondence, we study the problem of joint receive and transmit beamforming for a wireless network consisting of a transmitter, a receiver, and a relay node. The relay node is equipped with multiple antennas while the transmitter and the receiver each uses only one antenna. Our communication scheme consists of two phases: first the transmitter sends the information symbols to the relay. In the second phase, the relay re-transmits a linearly transformed version of the vector of the signals received at its multiple antennas. We introduce the novel concept of general rank beamforming which can be applied to our communication scheme. In our general rank beamforming approach, the relay multiplies the vector of its received signals by a general-rank complex matrix and re-transmits each entry of the output vector on the corresponding antenna. Through maximizing the signal-to-noise ratio (SNR), we obtain a closed-form solution to the general rank beamforming problem. We also prove that for the case of statistically independent transmitter-relay (TR) and relay-receiver (RR) channels, the general rank beamforming approach results in a rank-one solution for the beamforming matrix regardless of the rank of the channel correlation matrices. Simulation results show that when applied to the case of statistically dependent TR and RR channels, our general rank beamforming technique outperforms the separable receive and transmit beamforming method by a significant margin.

Cognitive Wireless Sensor Networks: Emerging topics and recent challenges

Adding cognition to the existing Wireless Sensor Networks (WSNs), or using numerous tiny sensors, similar to the idea presented in WSNs, in a Cognitive Radio Network (CRN) bring about many benefits. In this paper, we present an overview of Cognitive Wireless Sensor Networks (CWSNs), and discuss the emerging topics and recent challenges in the area. We discuss the main advantages, and suggest possible remedies to overcome the challenges. CWSNs enable current WSNs to overcome the scarcity problem of spectrum which is shared with many other successful systems such as Wi-Fi and Bluetooth. It has been shown that the coexistence of such networks can significantly degrade a WSNs performance. In addition, cognitive technology could provide access not only to new spectrum, but also to spectrum with better propagation characteristics. Moreover, by the adaptive change of system parameters such as modulation type and constellation size, different data rates can be achieved which in turn can directly influence the power consumption and the network lifetime. Furthermore, sensor measurements obtained within the network can provide the needed diversity to cope with spectrum fading at the physical layer.

Steady-state Markov chain analysis for heterogeneous cognitive radio networks

Cognitive radio technology has been widely researched to improve the spectrum usage efficiency. Modeling of the spectrum occupancy in a cognitive framework including licensed and unlicensed users with various traffic conditions, is a prior requirement to do the system analysis. In this paper, we develop a continuous-time Markov chain model to describe the radio spectrum usage, and derive the transition rate matrix for this model. In addition, we perform steady-state analysis to analytically derive the probability state vector. The proposed model and derived expressions are compared to the existing models, and examined through numerical analysis.

Network beamforming based on second order statistics of the channel state information

The problem of distributed beamforming is considered for a network which consists of a transmitter, a receiver, and r relay nodes. Assuming that the second order statistics of the channel coefficients are available, we design a distributed beamforming technique via maximization of the receiver signal-to-noise ratio (SNR) subject to individual relay power constraints. We show that using semi-definite relaxation, this SNR maximization can be turned into a convex feasibility semi-definite programming problem, and therefore, it can be efficiently solved using interior point methods. We also obtain a performance bound for the semi-definite relaxation and show that the semi-definite relaxation approach provides a c-approximation to the (nonconvex) SNR maximization problem, where c = O((log r)-1) and r is the number of relays.

Optimal network beamforming for bi-directional relay networks

We consider a relay network which consists of two transceivers and r relay nodes. We study a half-duplex two-way relaying scheme. First, the two transceivers transmit their information symbols simultaneously and the relays receive a noisy mixture of the two transceiver signals. Then each relay adjusts the phase and the amplitude of its received signal by multiplying it with a complex beamforming coefficient and transmits the so-obtained signal. Aiming at optimally calculating the beamforming weight vector as well as the transceiver transmit powers, we minimize the total transmit power subject to two constraints on the receive signal-to-noise ratios (SNRs) at the two transceivers. We show that the optimal weight vector can be obtained through a simple iterative algorithm which enjoys a linear computational complexity per iteration.

Energy-aware secondary user selection in cognitive sensor networks

In cognitive radio, accurate spectrum sensing is essential to optimally use the available spectrum opportunities. On the other hand, energy is a scarce resource especially in cognitive sensor networks. In this study, the authors combine both these conflicting requirements and propose an energy-aware secondary user selection algorithm for cognitive sensor networks. First, an optimisation problem is solved to obtain the minimum required number of cognitive users, whereas satisfying the system requirements. Second, the most eligible cognitive users are identified through a probability-based approach. They study two extreme cases by focusing on either energy or accuracy parameters. By numerical analysis, it is shown that the accuracy benchmark is increased by as much as 39% by only considering the sensing accuracy, and the energy benchmark is reduced by as low as 76% by only considering the remaining level of energy. In addition, they conduct computer simulation and compare the networks lifetime at several sensing accuracy thresholds. It is elaborated that greater sensing accuracy thresholds lead to longer network lifetime. Finally, the effects of several fusion rules on the proposed method are studied through simulation and numerical analyses. It is discussed that the Majority rule has the best performance among the examined rules.

Pulse Shape, Excess Bandwidth, and Timing Error Sensitivity in PRS Systems

In this correspondence, the effect of pulse shape on the performance of nonminimum bandwidth partial response signaling (PRS) systems is studied, using the measures of speed tolerance, eye width, and robustness to sampling phase jitter. In general, in modified duobinary (1- D^{2}) and dicode (1 - D) systems, linear rolloff pulse shaping performs better than raised cosine rolloff, while the raised cosine rolloff pulse spectrum is better in the duobinary (1 + D) system.

Decentralized spectrum learning and access adaptive to channel availability distribution in primary network

We consider the effect of the mean availability distribution of primary channels on the performance of distributed learning and access policies, and develop a distributed learning and access policy that is effective in a wide range of primary channel conditions. We first extend the recently proposed BLA algorithm to distributed online learning of underlying primary channel availabilities, and modify the existing access policies to form BLA-ρRAND and BLA-DLF policies. By analyzing the distributed access collision mechanism offered by the ρRAND and DLF policies [1], [2], we identify how different mean channel availability distributions can impact the effectiveness of each policy. In light of this, we propose DSLA policy that adapts to different channel availability distribution conditions. Based on a closeness factor we propose, the DSLA policy automatically switches between the underlying learning policies, as well as the access policies, to determine which policy is most effective for a given primary channel condition. Simulation studies show that our proposed DSLA policy is effective in providing a good performance for a wide range of primary channel availability distributions.

A continuous-time Markov chain model and analysis for cognitive radio networks

Cognitive radio concept has been widely researched to improve the spectrum usage efficiency. Appropriate modelling of the spectrum occupancy by both licensed and unlicensed users is necessary to do clear system analysis in a cognitive framework. In this paper, a continuous-time Markov chain model is developed to better describe the radio spectrum usage. The state space vector and the transition rate matrix that completely describe the system are obtained; a steady-state analysis is performed and the stationary state probability (SSP) vector is derived. In addition, we take into account the inaccuracy of the existing spectrum sensing model (missed opportunities), and derive an improved expression for the maximum throughput of secondary users as a function of the primary user traffic parameters and the achieved opportunity ratio (AOR). The optimum sensing period that maximises AOR is also analytically obtained. The proposed model and the derived expressions were examined through numerical analysis and compared with the existing models. This model is very general and applicable to systems with N secondary users in the vicinity of the primary user.