Joint power optimization of sources and relay in cooperative networks, while multiple source-destination (SD) pairs are utilizing a relay and also have direct transmission links, is addressed in this letter. In the assumed network each SD pair requires a minimum quality of service (QoS) in terms of its received SINR. We have shown that the resulted power control equations are quadratic due to coexistence of direct and relay links. The feasibility analysis of the resulted non-convex optimization problems is complicated. The necessary condition for the optimization problem feasibility is studied and two upper bounds for the achievable SINR of the users are derived. Computer simulations are used to study the achievable SINRs and the results are compared with the obtained upper bounds.

Spectrum management policies are responsible for poor utilisation of the radio spectrum. By carrying out dynamic spectrum management (DSM), cognitive radio (CR) can increase the radio spectrum in wireless systems efficiently. CR technology accounts for the improvement in the spectrum utilisation significantly. One issue of DSM in CR is the assignment of frequency channels among its users. Herein, a general model and four utility functions for optimal channel assignment in open spectrum systems such as CR networks have been defined. First, a new utility function with a better fairness than the other functions is proposed. Then, two new different channel assignment methods, based on the artificial bee colony (ABC) and bee swarm optimisation (BSO) algorithms, are proposed, whereas other certain evolutionary algorithms and colour sensitive graph colouring (CSGC) are used to compare the performances. In order to decrease the search space, based on the channel availability and interference constraints a mapping process between the channel assignment matrix and the position of the bees has been proposed. Our simulation results, compared to the optimal solutions, show that our algorithms drastically improve network performance by reducing interference.

The authors consider a half‐duplex asynchronous code division multiple access cooperative network with N source–destination (SD) pairs and a number of relay nodes where the nodes of all pairs have to exchange data in two hops via assistance of one of the available relays. In this study, they minimise the total transmit power and derive the closed‐form solution for choosing the best relay, the best relay gain and the transmit powers of all sources where some predefined signal‐to‐interference plus noise‐ratios (SINRs) are guaranteed. Interestingly, the feasibility condition of the problem depends only on the required SINRs, the number of SD pairs and the maximum cross‐correlation of users’ codes. They suggest two control procedures for admitting or dropping of users to the network to satisfy the feasibility condition. For a reciprocal environment, the best relay and its gain are proved to remain unchanged for reversing the communication directions. In addition, the authors’ power control algorithm can be directly applied to the case of two‐hop two‐way relaying. Computer simulations are used to demonstrate the system performance.

The authors consider a linear relaying communication system using multiple antennas at a source, relay and destination. They maximise the end‐to‐end signal‐to‐noise‐ratio (SNR) to find the relay transformation (RT) and the source transmit covariance (STC) matrices. For any given STC matrix, to maximise the SNR, they prove that the relay has to assign all its power in the direction of the dominant eigenmodes of the source–relay and the relay–destination channels. They also find the joint optimal rank‐one matrices for the STC and RT. They prove that this solution also maximises the source–destination mutual information among all rank‐one matrices. Furthermore, they find the optimal power budgets allocated to the source and relay that maximise the SNR under a constraint on the total transmit power of the system. This is a practical solution as only three positive quantities need to be communicated among the nodes to calculate these optimal power budgets. Interestingly, the authors computer simulations for multiple users reveal that the sum‐rate significantly increases if users selfishly maximise their own SNRs using the proposed method, instead of maximising their own capacity. Thus, they conclude that the SNR‐maximisation is spectrally more efficient as it consumes only the best subspaces and leaves the other subspaces free.

A relay communication system where its nodes are equipped with multiple antennas is considered. The relay applies a transformation matrix on its received signal and forwards it to the destination. The authors show that, given a limited relay transmit power, the transformation matrix which maximises the signal‐to‐noise power ratio (SNR) is almost surely a rank‐one matrix. Such a singular transformation matrix prevents accurate channel estimation. As a remedy, they impose a constraint on the condition number (CN) of the relay transformation matrix. They prove that the optimal relay matrix under this new constraint has up to two distinct singular values. Thus, the relay transmits some power in all directions, and this small energy allows better channel estimation. The author's simulation results show that imposing the CN constraint on the relay matrix results in some small loss in the SNR which is quantified in this study; however, the optimal relay matrix enables the receivers to track the channel state information.

The authors consider a relay communication system, where all nodes are equipped with multi‐input multi‐output antennas, and there is a direct path/channel between the source and the destination. Assuming a linear non‐regenerative relaying, the relay matrix is designed by maximising the signal‐to‐noise ratio of the system. The authors derive the optimum relaying matrix for different power constraints in the relay, including the fixed relay power constraint and constraint on the maximum transmitting power from the relay. Under the constraint of fixed total transmit power, they derive the optimum power budgets of the relay and source that only depend on four positive quantities. They prove that there always exists a rank‐one relaying matrix, which transmits signal in one‐dimensional subspace and leaves the other subspaces clean. In addition, when the quality of the source–destination (SD) channel is poor, this matrix is the best rank‐one relaying transform that maximises mutual information between the source and the destination. Finally, they conclude that the relaying is beneficial only if the link quality for the source–relay is twice better than that of the SD, where the link quality is proportional to the ratio of the channel power gain to the received noise variance.

Cognitive radio is a technology with tremendous potential for more efficient utilisation of the radio spectrum. Based on the available network side information to the secondary user, three transmission models are proposed in the literature: the ‘underlay’ method, the ‘overlay’ method and the ‘hybrid’ method. The authors study here the capacity and power allocation for the hybrid method. In particular, the authors investigate the ergodic capacity of the secondary user under joint average transmission power and average interference to the primary user receiver constraints when no channel state information is available to the secondary user except its probability density function. An algorithm based on the barrier method is proposed to achieve this. Finally, simulation results are presented and analysed to validate the authors’ theoretical results.

In this paper, we propose a speech-model based method using the linear predictive (LP) residual of the speech signal and the maximum-likelihood (ML) estimator proposed in “Blind estimation of reverberation time,” (R. Ratnam , J. Acoust. Soc. Amer., 2004) to blindly estimate the reverberation time (RT60). The input speech is passed through a low order linear predictive coding (LPC) filter to obtain the LP residual signal. It is proven that the unbiased autocorrelation function of this LP residual has the required properties to be used as an input to the ML estimator. It is shown that this method can successfully estimate the reverberation time with less data than existing blind methods. Experiments show that the proposed method can produce better estimates of RT60, even in highly reverberant rooms. This is because the entire input speech data is used in the estimation process. The proposed method is not sensitive to the type of input data (voiced, unvoiced), number of gaps, or window length. In addition, evaluation using white Gaussian noise and recorded babble noise shows that it can estimate RT60in the presence of (moderate) background noise.

In this letter, we consider a power allocation problem for digital subscriber line (DSL) systems. The goal of this problem is to minimize the total transmit power under some constraints on minimum data-rate and maximum transmit power for each modem where we take into account various sources of interference. We convert our problem into an auxiliary geometric programming (GP) which gives the optimum solution for transmit powers in a neighborhood of a given feasible point. Then, we use an iterative scheme for obtaining the solution to our original problem by exploiting this auxiliary GP problem. Numerical examples show that the proposed method outperforms the existing schemes.

The time varying channel between transmitter and receiver in wireless communications necessitates the receiver to be informed about the channel between the transmitter and the receiver periodically in the detection procedure. This paper introduces a geometrical model for channel matrix in a multiple-input multiple-output (MIMO) communication system, where the channel is supposed to be fast fading.Then an auto regressive (AR) model is proposed for the channel matrix variations at each time instant, based on which an LMS-like procedure for tracking the channel is suggested to increase the time period required for channel training in order to increase the data transmission rate. The efficiency of the method for channel matrix estimation is studied through simulation.The results show satisfactory match between the true and the estimated channel.

Capacity of wireless communication systems can be increased enormously using arrays of antenna at transmitter and receiver. However, in these so called multi-input multi-output (MIMO) communication systems, the performance of the signal detection is tightly related to the channel knowledge at the receiver side. Practically, the channel information at the receiver is subject to error because of the outdated data, quantization error, and imperfect channel estimation/measurement. It is known that in the case of imperfect channel knowledge, performance of symbol detection in MIMO communications may severely degrades. The optimum detection method in the sense of symbol error rate in MIMO systems is the maximum likelihood (ME) detection. In this paper, we proposed and studied a couple of robust detection methods that improve the performance of ME detector in MIMO systems in the presence of channel matrix error at the receiver

Channel estimation in multiple-input multiple-output (MIMO) wireless communication systems plays a key role in the performance of space-time decoders that depends on the accuracy of the channel knowledge. In this paper, we study the performance of MIMO channel estimation using training sequences. The least squares (LS), minimum-mean-square error (MMSE), and a new scaled LS (SLS) approaches to the channel estimation are studied and the optimal choice of training signals is investigated for each of these techniques.

A new robust power adjustment method is proposed for transmit beamforming in cellular communication systems that use antenna arrays at base stations (BSs). Our method provides an improved robustness against imperfect knowledge of the wireless channel by means of maintaining the required quality of service (QoS) for the worst-case channel uncertainty.