Seyed Mohammad Sajad Sadough

Improved iterative detection and achieved throughputs of OFDM systems under imperfect channel estimation

Assuming imperfect channel estimation, we propose an improved detector for orthogonal frequency-division multiplexing (OFDM) systems over a frequency-selective fading channel. By adopting a Bayesian approach involving the statistics of the channel estimation errors, we formulate an improved maximum-likelihood (ML) detection metric taking into account the characteristics of the channel estimates. As a first step, we propose a modified iterative detector based on a maximum a posteriori receiver formulation which reduces the impact of channel uncertainty on the decoder performance, by an appropriate use of this metric. The results are compared to those obtained by using a detector based on a mismatched ML metric, which uses the channel estimate as if it was the perfect channel. In a second step, we calculate the information rates achieved by both the improved and mismatched ML detectors, in terms of achievable outage rates. These outage rates are compared to those provided by a theoretical (not practical) decoder defined as the best decoder in the presence of channel estimation errors. Numerical results over both uncorrelated Rayleigh fading channels and realistic ultra wideband channels show that the improved detector outperforms the classically-used mismatched approach in terms of bit error rate and achievable outage rates, without any increase in the receiver complexity.

Optimal beamforming in cognitive two-way relay networks

We consider a cognitive two-way relay network which consists of two transceivers (primary users) and multiple cognitive terminals. The two transceivers transmit their data toward the cognitive terminals which sense the spectrum constantly. If the primary users are in operation, the cognitive terminals act as relay nodes by multiplying the received signal by a beamforming coefficients and then broadcasting the so-obtained signal to the transceivers. When the primary users are not in operation, the cognitive terminals communicate with a base station (BS) to increase the overall throughputs. Our aim is to optimally calculate the beamforming weight coefficients so as to minimize the total transmit power of the two transceivers and the cognitive radios subject to three constraints on the received signal-to-noise-ratios at the two transceivers and at the cognitive BS. Simulation results show that the sum-rate of the cognitive two-way relay network is increased while the total transmit power is minimized.

An improved blind spectrum sensing technique for cognitive radio systems

Cognitive radio has been proposed as a key solution for the problem of inefficient usage of spectrum bands. Spectrum sensing is one of the most important issues in each cognitive radio system. Classically-used spectrum sensing techniques require that the cognitive transmitter is not in operation while detecting the presence/absence of the primary signal during the sensing period. In this paper, we propose to use the Kurtosis metric, for blind source separation algorithms with the aim of improving the accuracy of conventional spectrum sensing techniques based on blind source separation. We also introduce a new spectrum sensing framework that combines blind source separation with conventional spectrum sensing techniques. In this way, spectrum sensing can continue to work even when the cognitive transmitter is in operation. Simulation results provided in terms of receiver operating characteristic (ROC) curves indicate that the proposed method improves the sensing performance achieved with conventional spectrum sensing techniques.

Wavelet-Based Semiblind Channel Estimation for Ultrawideband OFDM Systems

Ultrawideband (UWB) communications involve very sparse channels, because the bandwidth increase results in a better time resolution. This property is used in this paper to propose an efficient algorithm that jointly estimates the channel and the transmitted symbols. More precisely, this paper introduces an expectation-maximization (EM) algorithm within a wavelet-domain Bayesian framework for semiblind channel estimation of multiband orthogonal frequency division multiplexing based UWB communications. A prior distribution is chosen for the wavelet coefficients of the unknown channel impulse response to model a sparseness property of the wavelet representation. This prior yields, in maximum a posteriori estimation, a thresholding rule within the EM algorithm. We particularly focus on reducing the number of estimated parameters by iteratively discarding ldquoinsignificantrdquo wavelet coefficients from the estimation process. Simulation results using UWB channels that were issued from both models and measurements show that, under sparseness conditions, the proposed algorithm outperforms pilot-based channel estimation in terms of the mean square error (MSE) and bit error rate (BER). Moreover, the estimation accuracy is improved, whereas the computational complexity is reduced compared with traditional semiblind methods.

Spectrum Leasing for OFDM-Based Cognitive Radio Networks

In this paper, we propose an Orthogonal Frequency Division Multiplexing (OFDM)-based cooperative communication scheme joint with spectrum leasing. In the proposed scheme, the Primary User (PU) leases some parts of its available resources (in both time and frequency) as a revenue of cooperation to the Secondary Users (SUs) who act as relays for the primary transmission. The optimization of parameters that control the amount of leased resources from the PU to the SU is investigated. An iterative framework is proposed for the PU to find its appropriate relays. Moreover, a dynamic noncooperative game is considered at the relays to compete among themselves for controlling their powers and the Nash Equilibrium (NE) is suggested as the solution of this competition. Analytical results show that the PU achieves a higher data rate by this cooperation process compared with the case where there is no cooperation.

Improved Iterative MIMO Signal Detection Accounting for Channel-Estimation Errors

For the case of imperfect channel estimation, we propose an improved detector for multiple-input-multiple-output (MIMO) systems using an iterative receiver. We consider two iterative detectors based on maximum a posteriori and soft parallel-interference cancellation and propose, for each case, modifications to the MIMO detector by taking into account the channel-estimation errors. Considering training-based channel estimation, we present some numerical results for the case of Rayleigh block fading, which demonstrate an interesting performance improvement, compared with the classically used mismatched detectors. This improvement is obtained without a considerable increase in receiver complexity. It is more advantageous when few pilots are used for channel estimation. Thus, the proposed improved detectors are of particular interest under relatively fast-fading conditions, where it is important to reduce the number of pilots to avoid a significant loss in data rate.

Improved spectrum sensing and achieved throughputs in cognitive radio networks

State of the art spectrum sensing methods evaluate sensing performance only based on correct detection of the presence of the primary users signal. As a first step, we propose a more realistic framework that also takes the correct detection of the absence of primary users signal into account for spectrum sensing performance evaluation. More precisely, by using the above framework, we select energy detection spectrum sensing parameters so as to minimize a cost function leading to an optimal operating point over the widely-used receiver operating characteristic curve. In a second step, by using the parameters obtained in the first step, we calculate the information rates achieved by both the cognitive and the primary users. These rates are compared to those provided by an ideal (interference-free) spectrum sensing. Numerical results show that the proposed improved spectrum sensing outperforms conventional spectrum sensing techniques in terms of total achievable throughputs, without any increase in the cognitive radio complexity.

On the Outage Capacity of a Practical Decoder Accounting for Channel Estimation Inaccuracies

The optimal decoder achieving the outage capacity under imperfect channel estimation is investigated. First, by searching into the family of nearest neighbor decoders, which can be easily implemented on most practical coded modulation systems, we derive a decoding metric that minimizes the average of the transmission error probability over all channel estimation errors. Next, we specialize our general expression to obtain the corresponding decoding metric for fading MIMO channels. According to the notion of Estimation-induced outage (EIO) capacity introduced in our previous work and assuming a block Rayleigh-fading channel, we characterize the maximal achievable information rates using Gaussian codebooks associated to the proposed decoder. These achievable rates are compared to the rates achieved by the classical mismatched maximum likelihood (ML) decoder and the ultimate limits given by the EIO capacity. Numerical results show that the derived metric provides significant gains, in terms of achievable EIO rates and bit error rate (BER), in a bit interleaved coded modulation (BICM) framework, without introducing any additional decoding complexity. However, the achievable rates of such metric are still far from the EIO capacity.

Multiband Cooperative Spectrum Sensing for Cognitive Radio in the Presence of Malicious Users

We propose a cooperative multiband spectrum sensing scheme for a cognitive radio network operating in the presence of malicious users. More precisely, we investigate the optimality of cooperative spectrum sensing with the aim of optimizing the detection performance and then maximize the aggregate achievable throughput given a bound on the aggregate interference imposed on the primary network. Comparative simulation results validate the superiority of our proposed method in terms of detection accuracy compared to conventional defense techniques used to combat the presence of malicious attackers.

Cooperative spectrum sensing in cognitive radio networks under primary user emulation attacks

Cognitive radio as a solution of spectrum scarcity, proposes a dynamic spectrum access which imposes some threats to the network. One of these common threats is primary user emulation attack, where some malicious users try to mimic the primary signal and deceive secondary users to prevent them from accessing vacant frequency bands. In this work, we consider the true definition of a primary user emulation attacker (PUEA) in which fake signals are sent when the primary user signal is not present in the radio environment. We formulate and derive cooperative spectrum sensing (CSS) rules for a cognitive network in the presence of a PUEA and propose a new spectrum sensing based on energy detection with fusion centers employing OR/AND logic rules. Simulation results show that our proposed method can mitigate the destructive effect of PUEA in spectrum sensing, compared to conventional energy detection spectrum sensing.