Aamir Ishaque

MSE-based stochastic transceiver optimization in downlink cognitive radio networks

In practice, the imperfect channel state information can degrade the optimization performance of a cognitive system, especially yielding the detrimental violation of the interference constraint perceived by the primary users. This paper investigates the linear transceiver design in cognitive downlink systems with the imperfect channel knowledge, aiming at minimizing the mean square error of the secondary network subject to both the transmit and interference power constraints. Two methods are proposed to solve the optimization problem. The first alternating method decomposes the non-convex primal problem into two subproblems. Each of them is converted to the standard convex optimization forms. The second method is based on the reformulation of the primal problem into a two-loop optimization problem in which a more efficient gradient projected method applies. We also prove the condition to achieve Karush-Kuhn-Tucker optimality. The effectiveness and robustness of the proposed solutions are validated by the simulation result.

Robust MSE-Based Transceiver Optimization in MISO Downlink Cognitive Radio Network

The concurrent transmission of cognitive radios (CRs) and primary users (PUs) can achieve high spectral efficiency, provided that the interference power to each PU is limited below a certain threshold. Joint optimization of the precoding and the equalization is widely used to exploit the spatial diversity to mitigate the interference. However, its performance is sensitive to the inevitable imperfect channel state information (CSI). This paper studies the robust transceiver optimization in multiple-input and single-output downlink CR network, aiming at minimizing the worst-case per-user mean square error. The channel uncertainty model is assumed to be ellipsoidal bounded for both channel matrices and short-term channel covariance matrices. Under the uncertainty models for both cases of CSI, we first derive the strict upper bound or equivalent conditions for the constraints, then reformulate the original optimization problem into the semi-definite programming problems which can be efficiently solved. Simulation results demonstrate that the proposed schemes effectively mitigates the performance degradation due to the imperfect CSI. Their robustness with respect to the interference constraint is also validated, which is regarded as the critical part in the design of CR network.

MSE-based linear transceiver optimization in MIMO cognitive radio networks with imperfect channel knowledge

This paper addresses the robust transceiver optimization in multiple-input and multiple-output cognitive radio network, where primary users (PUs) and secondary users (SUs) coexist in the same spectrum band. In the design of cognitive system, the performance degradation perceived by PU should be strictly restricted even with imperfect channel state information (CSI) at cognitive transmitter and receivers. Therefore, this work aims at minimizing the sum mean square error of secondary downlink network and strictly limiting the interference caused to PUs with imperfect channel knowledge. Two types of CSI error models are considered: the bounded model and the stochastic model. Since the original optimization problems are non-convex for the joint optimization, firstly it is decomposed into two subproblems to optimize the precoding and equalizers separately, then the iterative algorithms are proposed to solve the subproblems in an alternating way. The challenge is to design the efficiently solvable forms of these subproblems. For the bounded model, Schur complement lemma is utilized to convert the subproblems into convex optimization problems. For the stochastic model, the problem is formulated either according to the stochastic rule or derived for the analytical solutions. The effectiveness and robustness of proposed algorithms are evaluated by the numerical results.

I/Q imbalance and CFO in OFDM/OQAM systems: Interference analysis and compensation

Offset-QAM (OQAM) based OFDM has been considered as a promising technique for future wireless networks due to its higher resilience to narrow-band interference and doubly dispersive channels. In this paper, we investigate the effects of the RX imperfections, namely the I/Q imbalance (IQI) and the carrier frequency offset (CFO), on OFDM/OQAM downlink performance coupled with the imperfect knowledge of the frequency-selective channel. The influence of each impairment is characterized by the interference analysis of the demodulated signal that reveals interesting insight into the distortion origination. Then, we analytically investigate the performance loss as a function of IQI, CFO and channel distortions in a realistic receiver with channel estimation error. To cope with impaired reception, a joint maximum-likelihood estimation method with repetitive training signals is employed. Simulation results demonstrate the relative sensitivity of OFDM/OQAM receivers to the impairments and that the performance loss due to the imperfections can be recovered by the compensation technique.

Widely linear receivers for SMT systems with TX/RX frequency-selective I/Q imbalance

Staggered multitone (SMT) modulation scheme has received a considerable attention recently due to its higher spectral efficiency compared to the conventional OFDM systems and robustness to frequency offset and Doppler spread. However, channel equalization in SMT is complicated by the so called intrinsic interference as a result of non-flat channel response in each subchannel. In the presence of I/Q imbalance (IQI), this task becomes even more challenging as the image band and its intrinsic neighborhood contaminates the received signal. This paper deals with widely linear schemes for the effective channel equalization using time-domain and frequency-domain methodologies. Further iterative improvements are proposed for the later, combining enhanced equalization with per-subchannel-pair processing after intrinsic interference cancellation. The proposed techniques are simulated for both the uncoded and coded systems suffering from frequency-selective IQI and their effectiveness with multipath channels is demonstrated.

Capacity analysis of uplink multi-user SC-FDMA system with frequency-dependent I/Q imbalance

In this paper, we investigate the capacity of the multi-user (MU) channels with the single-carrier frequency-division-multiple-access (SC-FDMA) transceivers using widely linear equalizers (WLE) to counteract front-end I/Q imbalance (IQI) issues. When the erroneous channel knowledge is available at the equalizer, it is shown that the mutual information could be expressed as a function of the eigenvalues of the direct and image channel realization. Expressions for the error variance of the effective channel and the associated channel capacity are derived in closed form and discussed under special cases. Numerical analysis shows that the ergodic and outage capacity of WLE receivers tends to achieve a capacity region in the vicinity of an ideal system and considerably extends the coverage region of the cell. The impact of the channel correlation and the IQI on the outage probability is also investigated, which highlights the crucial role of the MU resource assignment in exploiting the spectral diversity and their optimality in the presence of IQI.

On the efficient mitigation of phase noise in MIMO-OFDM receivers

In this paper, an algorithm is proposed for the joint phase noise (PN) estimation and data detection in or- thogonal frequency division multiplexing signals pertaining to spatially multiplexed multiple-input multiple-output channels. Severe oscillator PN gives rise to inter-carrier interference that becomes a limiting factor in sustaining reliable communication link. Considering a received signal perturbed by PN, we derive an iterative Bayesian algorithm based on the statistical modeling of the PN process. This approach however needs computationally intensive inversion of large data-dependent matrices. Taking advantage of the approximate sparsity pattern inherent to the PN spectral composition and exploiting decoupled compensators, the proposed scheme enhances the receiver performance while retaining a modest complexity. Furthermore, we consider how these estimators perform in the case of multiple-antenna RX front-ends with a generalized oscillator signal distribution to the radio paths and numerically evaluate the performance achieved by the proposed algorithm.

On the sensitivity of SMT systems to oscillator phase noise over doubly-selective channels

In this paper, we analyze the effects of oscillator phase noise and doubly-selective fading channel on the performance of the staggered multitone (SMT) system. Unlike the more familiar discrete multitone (DMT) systems, SMT is shown to be insensitive to self-interference effects due to phase noise. The impeding channel and phase noise interference coefficients are defined and theoretical expressions for the inter-carrier and intersymbol interference are derived to establish solid analytical basis. We demonstrate the resilience of SMT waveform to phase noise distortion and channel fading effects in numerical experiments using appropriate models and the theoretical considerations are shown to fit well with simulation findings. Our assessment indicates that SMT system performs nearly as well as the classical DMT modulation for non-dispersive channels, while it outperforms DMT system by 2 dB for the studied fading channel scenario and fixed equalization complexity for both systems.

Duality-Based Robust Transceiver Design for Cognitive Downlink Systems

This paper studies robust transceiver optimization for cognitive downlink systems in the presence of imperfect channel state information (CSI). We aim at minimizing the sum mean square error (MSE) of the secondary downlink transmission subject to the interference constraint imposed on the primary users. The MSE duality is developed to describe the uplink-downlink relation, in which imperfect CSI and multiple power constraints are taken into account. Based on the duality result, we propose an efficient robust approach which can effectively avoid the violation of the interference constraint. We also discuss the performance in terms of optimality as well as complexity issue. Compared to the downlink-based approach, the proposed one features faster convergence speed and lower complexity.

Widely Linear Estimation of Oscillator Phase Noise with Rank Reduction

In this paper, we present a novel algorithm for oscillator phase noise estimation using two key properties of the phase noise process in digital baseband: reduced-dimensional characteristics when expanded with Karhunen-Loeve basis functions and statistical improperness of the phase noise coefficients paving the way for the optimality of widely-linear (WL) estimators. The proposed methods are designed to take full- advantage of the second-order statistics, rank-deficient models and has an attractive trade- off between performance and complexity. To compute rank-reduced WL Wiener filter for highly compressed estimation, two methods are derived and analyzed using either data or parameter subspace reduction. Numerical experiments are presented for practically relevant phase noise distributions and they demonstrate the applicability of the proposed methods. They show that WL estimator can achieve up to 5 dB improvement over the best strictly linear estimator, with the maximum achieved in complexity-favorable low-rank conditions.