Multi-antenna Cognitive Radio Research Articles

Improved-MUSIC Algorithm Based on Compressive Subspace Learning for Multiantenna Cognitive Radio

Improved-MUSIC Algorithm Based on Compressive Subspace Learning for Multiantenna Cognitive Radio

Joint Sensing Times Detection Thresholds and Users Association Optimization in Multi-Channel Multi-Antenna Cognitive Radio Networks

Joint Sensing Times Detection Thresholds and Users Association Optimization in Multi-Channel Multi-Antenna Cognitive Radio Networks

On Sensing Performance of Multi-Antenna Mobile Cognitive Radio Conditioned on Primary User Activity Statistics

In limited space scenarios, the antennas in a multiantenna cognitive radio (CR) system are closely spaced and often experience correlation among them. In this paper, the sensing performance of arbitrarily correlated antennas over the Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading channel for the mobile CR user is analysed. In particular, the analytical expression for the average detection probability for a mobile CR user employing the selection combining with arbitrarily correlated triple diversity branches is derived as a special case. Moreover, to characterize the performance of an energy detector under mobility, the area under the curve of a receiver operating characteristic is analysed. Furthermore, as the sensing decisions can be utilized to improve the sensing performance, a comprehensive analysis of sensing performance of the mobile secondary user conditioned on the primary user (PU) activity statistics is carried out. We assume the PU traffic to be following the Discrete-Time Markov Chain (DTMC) model, and its <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$idle$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$busy$ </tex-math></inline-formula> periods to be following generalised Pareto distribution. The analytical framework is substantiated by Monte Carlo simulations. Results indicate that antenna correlation deteriorates detection performance. Moreover, the detection performance deteriorates further due to the mobility of CR users, especially in the deep fading channel scenarios. Furthermore, findings also suggest that under mobility, the sensing performance improves if the duty cycle of the PU channel is low. This work provides a realistic sensing framework for CR enabled vehicles.

Rate-Splitting Multiple Access for Communications and Jamming in Multi-Antenna Multi-Carrier Cognitive Radio Systems

With the increasing number of wireless communication systems and the demand for bandwidth, the wireless medium has become a congested and contested environment. Operating under such an environment brings several challenges, especially for military communication systems, which need to guarantee reliable communication while avoiding interfering with other friendly or neutral systems and denying the enemy systems of service. In this work, we investigate a novel application of Rate-Splitting Multiple Access (RSMA) for joint communications and jamming with a Multi-Carrier (MC) waveform in a multi-antenna Cognitive Radio (CR) system. RSMA is a robust multiple access scheme for downlink multi-antenna wireless networks. RSMA relies on multi-antenna Rate-Splitting (RS) strategy at the transmitter and Successive Interference Cancellation (SIC) at the receivers. By employing RSMA at the secondary transmitter, our aim is to simultaneously communicate with Secondary Users (SUs) and jam Adversarial Users (AUs) to disrupt their communications while limiting the interference to Primary Users (PUs) in a setting where all users perform broadband communications by MC waveforms in their respective networks. We consider the practical setting of imperfect CSI at Transmitter (CSIT) for the SUs and PUs, and statistical CSIT for AUs. We formulate a problem to obtain optimal precoders which maximize the mutual information under interference and jamming power constraints. We propose an Alternating Optimization-Alternating Direction Method of Multipliers (AO-ADMM) based algorithm for solving the resulting non-convex problem. We perform an analysis based on Karush-Kuhn-Tucker (KKT) conditions to determine the optimal jamming and interference power thresholds that guarantee the feasibility of problem and propose a practical algorithm to calculate the interference power threshold. By simulation results, we demonstrate that RSMA achieves a higher sum-rate performance than Space Division Multiple Access (SDMA) and Non-Orthogonal Multiple Access (NOMA).

Improved Spectrum Sensing Schemes Using Prewhitening and Weights Under Spatially Correlated Noise

Conventional spectrum sensing (SS) schemes in a multiantenna cognitive radio utilize matrix-inverse based prewhitening to decorrelate the spatially correlated received signals. However, the improved SS schemes can be proposed by efficiently exploiting the spatial correlation information. In this paper, two detectors—weighted cross-correlation absolute value detector (WCCAVD) and weighted energy detector (WED), are proposed by exploiting the spatial correlation of noise. It is shown that in the presence of high spatial correlation and low signal-to-noise ratio, the proposed WCCAVD and WED outperform the conventional cross-correlation absolute value detector and energy detector (ED), respectively, by more than 1 dB. For the spatial correlation below 0.5, both the proposed detectors are shown to have comparable performance with respect to the corresponding conventional detectors. The analytical expressions for the decision threshold, the false-alarm and the detection probabilities of the proposed detectors are derived. The analytical results are validated by Monte-Carlo simulations.

Spectrum sensing in a calibrated multi-antenna cognitive radio: Exact LRT approaches

This paper addresses the spectrum sensing problem in a calibrated multi-antenna cognitive radio. To do so, a composite hypothesis testing problem is formulated according to the covariance matrix of the received signal. Then, we resort to the likelihood ratio test (LRT) principle to derive some new LRT tests. This problem has been previously solved based on two-step Generalized LRT (2S-GLRT), leading to different detection strategies in comparison to this study; thus the proposed method is referred to as an exact spectrum sensing method. We provide some simulation results to show that the detection performance of the proposed detectors is better than their counterparts, derived based on the 2S-GLRT, as well as some other state-of-the-art approaches given in the literature.

Cognitive Radio Spectrum Sensing Under Joint TX/RX I/Q Imbalance and Uncalibrated Receiver

In high-speed wireless communication systems, direct-conversion transceivers (DCTRs) are in demand due to low implementation cost of analog front ends. However, the DCTRs are more susceptible to radio frequency impairments like in-phase and quadrature (I/Q) imbalance. This has been shown to significantly limit the spectrum sensing (SS) capability of a secondary user to find spectrum holes. Another source of performance limitation in multiantenna cognitive radio (CR) systems is the uncalibrated receivers across the antennas. Unlike previous works, which considered these two impairments separately, we model the received signal as a single-input multiple-output CR system in the presence of transmit (TX) and receive (RX) I/Q imbalances and when the receiver is uncalibrated across the antennas. In such a scenario, we model the SS problem as a composite binary hypothesis testing problem, apply the likelihood ratio test principle, and develop a new robust SS approach. The performance of the proposed SS algorithm is evaluated using extensive Monte Carlo simulations to illustrate the superiority of the proposed detector in comparison with the existing SS algorithms in the literature. The obtained results indicate that the proposed SS algorithm has constant false alarm rate property with respect to any nonuniform noise variance uncertainties as well as RX I/Q imbalance parameters, thereby being a robust and blind detector.

Simple &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$F$ &lt;/tex-math&gt; &lt;/inline-formula&gt;–Test-Based Spectrum Sensing Techniques for Multi-Antenna Cognitive Radios

An F-test detector with a simple analytical false alarm threshold expression is considered an alternative to the blind detectors which exhibit complicated analytical expressions. However, the existing F-test requires the channel state information (CSI) as a prior knowledge and is known to be sensitive to CSI estimation errors. In this paper, we present and evaluate simple F-test-based spectrum sensing techniques that do not require the knowledge of CSI for multi-antenna cognitive radios. Exact and asymptotic analytical performance closed-form expressions are derived for the presented detectors. Simulations assess the performance of the presented detectors and validate the derived closed-form expressions. For an additive noise exhibiting the same variance across multiple-antenna frontends, simulations also corroborate that the presented detectors are constant false alarm rate detectors which are robust against noise uncertainty.

Simultaneous cooperative spectrum sensing and wireless power transfer in multi-antenna cognitive radio

In multi-antenna cognitive radio (CR), the sensing performance of detecting the presence of primary user (PU) in fading channel can be improved through multi-antenna cooperative spectrum sensing. However, the CR may consume more stored energy due to the cooperative spectrum sensing and thus decrease its transmission performance. In this paper, to guarantee the transmission performance, a simultaneous cooperative spectrum sensing and wireless power transfer (SCSSWPT) scheme has been proposed, which can harvest the radio frequency (RF) energy of the PU signal to supply the consumed energy of spectrum sensing. Time splitting model, power splitting and antenna splitting model are proposed to implement cooperative spectrum sensing, energy harvesting and data transmission simultaneously. Three optimization problems have been formulated to maximize spectrum efficiency of the CR in the three SCSSWPT models, respectively, subject to the constraints of detection probability and harvested energy. The simulation results have shown that there is an optimal number of sensing antennas to maximize the spectrum efficiency of the CR, and the time splitting model can achieve higher spectrum efficiency.

Simultaneous Cooperative Spectrum Sensing and Energy Harvesting in Multi-antenna Cognitive Radio

In cognitive radio (CR), the secondary user (SU) may use more battery energy to perform spectrum sensing, thus decreasing the transmission energy. In order to guarantee the transmission performance, an energy harvesting-based multi-antenna CR is proposed, which lets the SU harvest the radio frequency (RF) energy of the PU signal and the noise to supplement the energy loss. Time splitting model and antenna splitting model have been proposed to realize the simultaneous cooperative spectrum sensing and energy harvesting for multi-antenna CR, in which cooperative spectrum sensing, energy harvesting and data transmission can be performed in one SU. The joint resource allocations of these two models have been formulated as a class of optimization problems about sensing time, harvesting time, the number of sensing antennas and transmission power. The joint optimization algorithm has been proposed to obtain the optimal solutions to the optimization problems. The simulation results have indicated that the proposed models can achieve larger throughput compared with the sensing-throughput tradeoff model.

Asymptotic Approximation of the Standard Condition Number Detector for Large Multi-Antenna Cognitive Radio Systems

Standard condition number (SCN) detector is a promising detector that can work efficiently in uncertain environments. In this paper, we consider a Cognitive Radio (CR) system with large number of antennas (eg. Massive MIMO) and we provide an accurate and simple closed form approximation for the SCN distribution using the generalized extreme value (GEV) distribution. The approximation framework is based on the moment-matching method where the expressions of the moments are approximated using bi-variate Taylor expansion and results from random matrix theory. In addition, the performance probabilities and the decision threshold are considered. Since the number of antennas and/or the number of samples used in the sensing process may frequently change, this paper provides simple form decision threshold and performance probabilities offering dynamic and real-time computations. Simulation results show that the provided approximations are tightly matched to relative empirical ones.

An EM Approach for Cooperative Spectrum Sensing in Multiantenna CR Networks

In this paper, a cooperative wideband spectrum sensing scheme based on the expectation–maximization (EM) algorithm is proposed for the detection of a primary user (PU) system in multiantenna cognitive radio (CR) networks. Given noisy signal observations from $N$ secondary users (SUs) over multiple subbands at a fusion center (FC), prior works on cooperative spectrum sensing often use the set of received subband energy as decision statistics over the sensing interval. However, to achieve satisfactory performance, knowledge of the channel state information (CSI) and the noise variances at all the SUs is required by these algorithms. To overcome this limitation, our proposed method, which is referred to as joint detection and estimation (JDE), adopts the EM algorithm to jointly detect the PU signal and estimate the unknown channel frequency responses and noise variances over multiple subbands in an iterative manner. Various aspects of this proposed EM-JDE scheme are investigated, including a reliable initialization strategy to ensure convergence under practical conditions and a distributed implementation to reduce communication overhead. Under the assumption of perfect estimation for the channel frequency responses and noise variances, we further show that the proposed EM-JDE converges to the maximum-likelihood (ML) solution, which serves as an upper bound on its performance. Monte Carlo simulations over Rayleigh fading channels show that the proposed scheme significantly improves the performance of spectrum detection by exploiting the diversity of the spatially distributed SUs with multiple antennas.

Rayleigh quotient based interference alignment spectrum sharing in MIMO cognitive radio networks

An interference alignment (IA) spectrum sharing method based on Rayleigh quotient is proposed for distributed multi-user multi-antenna cognitive radio (CR) networks. The interference from cognitive users (CUs) to the primary (PR) system is constrained through the Rayleigh quotients of channel matrices to deal with the absence of PR users (PUs) in the IA process. As a result, the IA scheme can be applied in CR networks without harmful interference to PUs. Compared with existing IA based spectrum sharing methods, the proposed method is more general because of breaking the restriction that CUs can only transmit on the idle sub-channels of the PR system. Moreover, in comparison to other four spectrum sharing methods applicable in general scene, the proposed method leads to improved performance of achievable sum rate of the CR system as well as guarantees the transmission of PUs

A Comparative Performance Analysis of Interweave and Underlay Multi-Antenna Cognitive Radio Networks

In this paper, an analytical performance study for multi-antenna Cognitive Radio (CR) systems is presented. The two most popular CR approaches, namely, the interweave and underlay system designs, are considered and based on the derived analytical framework, a throughput-based comparison of these two system designs is presented. The system parameters are selected such that a quality of service (QoS) constraint on primary communication, is satisfied. Closed form expressions for the outage probability at the Primary User (PU), as well as expressions for the ergodic rate of the Secondary User (SU) are derived, for both system designs. The derived expressions are functions of key design parameters, such as the sensing time and the detection threshold in the case of interweave CR, and the maximum allowable interference power received by the PU, in the case of underlay CR. Based on the derived expressions, for interweave CR, the sensing parameters, i.e., sensing time and energy detection threshold, are optimized such as to maximize the secondary system rate. By comparing the throughput performance for both system designs, the existence of specific regimes (in terms of primary activity, number of transmit and receive antennas as well as the outage probability of the PU), where one CR approach outperforms the other, is highlighted.

Power Minimization in Multi-Band Multi-Antenna Cognitive Radio Networks

This paper aims to design an optimal set of beam-vectors for multi-band multi-antenna cognitive radio networks that jointly allocate power over both space and frequency, so that the sum power of secondary users (SUs) is minimized, subject to rate demands at the SUs, as well as the interference constraints imposed by primary users. Unlike the rate maximization problems, which are always feasible, this power minimization (PM) problem may be infeasible due to the rate constraints. Therefore, we provide a complete analysis of the PM problem by splitting the solution into two separate phases. In phase I, a novel method is developed to check the feasibility of the PM problem by considering an alternative problem, where one additional variable is introduced. This alternative problem is always feasible and one algorithm based on network duality and geometric programs is developed to solve it. In phase II, a novel algorithm is developed to solve the PM problem. This algorithm can be implemented in an online fashion. Furthermore, this algorithm is proved to converge to a Karush-Kuhn-Tucker point of the PM problem. Simulation results show that the proposed algorithms converge in only a few iterations and significantly outperform the existing single-band method in terms of both the feasibility probabilities and power savings.

Throughput analysis of multi‐antenna cognitive broadcast networks

In this study, the authors analyse the throughput of random beamforming (RBF) for a multi-antenna cognitive radio (CR) broadcast network. Using extreme value theory, the asymptotic average throughput of a single-beam RBF and a multiple-beam RBF are derived from the limiting distribution of the sample maximum of received signal-to-noise ratio (signal-to-interference-plus-noise ratio) with maximal ratio combiner at the secondary user. It is shown that the throughput of the single-beam RBF scales as logarithm of the number of users, which stands in contrast to the double logarithm of the number of users in non-CR networks. They also found that the single-beam RBF is always much preferable to the multi-beam RBF on the throughput in the CR networks, which is opposite to the previous results in the non-CR networks. Simulation results show that the author's approximated expressions match with the simulation results very well.

MMSE-SLNR Precoding for Multi-Antenna Cognitive Radio

In this paper, we propose and optimize a low-complexity precoding scheme for multiple antenna cognitive radio networks. In a cognitive network, a secondary transmitter is allowed to access the spectrum of the primary network only if the interference to the primary network remains below the predefined power limit. The proposed scheme, termed MSLNR, is a combination of the optimal minimum-mean-square-error (MMSE) receiver and the signal-to-leakage-plus-noise-ratio (SLNR) transmitter, with additional scaling to comply with the cognitive interference constraint. We also present a robust design method for the case where the secondary transmitter has only partial channel state information (CSI). The MSLNR scheme requires low implementation complexity. The transmit precoder is evaluated while taking into account the optimal receiver weight, but without any iterations. Yet, simulation results demonstrate that the performance of the proposed MSLNR scheme is close to the performance of the best known solution.

Optimal Beamforming and Power Allocation for Sensing-Based Spectrum Sharing in Cognitive Radio Networks

In this paper, the optimal beamforming and power allocation strategy for the secondary transmitter based on the sensing result on the state of the primary user in a multiantenna cognitive radio (CR) network is proposed. Unlike the existing sensing-based spectrum sharing, where the secondary transmitter employs a single antenna and adjusts only its transmit power, in the proposed scheme, the secondary transmitter employs multiple transmit antennas and adjusts the beamforming vector as well as the transmit power. An iterative algorithm that can efficiently obtain the optimal beamforming vector and power allocation is developed. Simulation results show that the proposed scheme achieves a significant rate gain over the existing sensing-based spectrum sharing schemes.

Price-Based Joint Beamforming and Spectrum Management in Multi-Antenna Cognitive Radio Networks

We consider the problem of maximizing the throughput of a multi-antenna cognitive radio (CR) network. With spatial multiplexing over each frequency band, a multi-antenna CR node controls its antenna radiation directions and allocates power for each data stream by appropriately adjusting its precoding matrix. Our objective is to design a set of precoding matrices (one per band) at each CR node so that power and spectrum are optimally allocated for the node and its interference is steered away from unintended receivers. The problem is non-convex, with the number of variables growing quadratically with the number of antenna elements. To tackle it, we translate it into a noncooperative game. We derive an optimal pricing policy for each node, which adapts to the node's neighboring conditions and drives the game to a Nash-Equilibrium (NE). The network throughput under this NE equals to that of a locally optimal solution of the non-convex centralized problem. To find the set of precoding matrices at each node (best response), we develop a low-complexity distributed algorithm by exploiting the strong duality of the convex per-user optimization problem. The number of variables in the distributed algorithm is independent of the number of antenna elements. A centralized (cooperative) algorithm is also developed. Simulations show that the network throughput under the distributed algorithm rapidly converges to that of the centralized one. Finally, we develop a MAC protocol that implements our resource allocation and beamforming scheme. Extensive simulations show that the proposed protocol dramatically improves the network throughput and reduces power consumption.

Combined multiuser signal classification and blind equalization

A multiuser automatic modulation classifier (MAMC) is an important signal processing component of a multiantenna cognitive radio (CR) receiver that helps the radio in identifying modulation format employed by multiple users in a frequency band simultaneously. In a typical wireless communication, transmitted signals are subjected to multipath fading and interference from other users. Multipath fading not only affects symbol detection performance but also affects the performance of the automatic modulation classifier. A multi input multi output (MIMO) blind equalizer is another important component of a multiantenna CR receiver that improves symbol detection performance by reducing inter symbol interference and inter user interference. In a CR scenario it is preferable to consider the performance of the AMC also while adapting the parameters of the blind equalizer. In this paper we propose MIMO blind equalizers that improves the performance of both multiuser symbol detection and cumulants based MAMC. Computer simulations are provided to illustrate this concept and the proposed algorithm.