Multiple-input Multiple-output Cognitive Radio Network Research Articles

Non‐orthogonal multiple access‐aided MIMO cognitive radio networks with selective reporting

AbstractIn this article, we investigate the performances of conventional cooperative sensing (CCS)‐ and superior selective reporting (SSR)‐based cooperative sensing schemes in a multiple‐input, multiple‐output (MIMO)‐aided non‐orthogonal multiple access (NOMA)‐driven cognitive radio (CR) networks. First, we derive expressions for the probabilities of false‐alarm and signal detection for an optimal detector in the considered MIMO‐NOMA CR setup. Next, we study a constrained optimization problem that maximizes the sum rates of CCS and SSR schemes, with a constraint on the power allocation at SUs. This problem is shown to be a constrained convex optimization problem. Later, we analytically characterize and compare the sum rate performances of CCS and SSR schemes in MIMO‐NOMA CR and MIMO‐based orthogonal multiple access (MIMO‐OMA) CR networks. Extensive computer simulation results are presented which corroborate our analysis, which also quantifies the gain in performance of the proposed MIMO‐NOMA CR network employing SSR scheme when compared to conventional MIMO‐OMA CR systems. Furthermore, we present an experimental study based on RTL‐SDR setup, which supports our theory and also paves way towards a practical implementation of the proposed framework.

Outage probability of MIMO cognitive radio networks with energy harvesting and adaptive transmit power

This paper derives the outage probability of cognitive radio networks (CRNs) with energy harvesting (EH). The primary nodes have a single antenna, whereas the secondary nodes have multiple antennas. A secondary source S harvests energy from radiofrequency (RF) signal received from primary transmitter PT using nr, S antennas. S also adapts its power so that the interference at primary receiver PR is less than threshold I. The main contribution of the paper is to derive the throughput of multiple input multiple output (MIMO) CRN with RF energy harvesting and adaptive transmit power. We also optimize the secondary throughput by choosing the optimal harvesting duration.

Intelligent and secure transceiver design and implementation for future wireless communication

In this work, we have proposed an intelligent and secure transceiver design and implemented over RF testbed for future wireless communication. To secure the communication, first of all, physical layer security (PLS) has been proposed for underlay multiple-input multiple-output cognitive radio network (MIMO-CRN) by using a bi-directional zero-forcing beamforming. Secondly, (PLS) has been proposed by using transmit beamforming for non-orthogonal multiple access (NOMA) over multiple cells and in the presence of primary user and secondary users. The proposed model has been extended for MIMO-NOMA over double cell and multiple scenarios. To improve further security an adaptive transceiver has been designed where parameter can be varied as per the system requirement and meantime, an intelligent received is developed in which parameter can be estimated through a blind process. The modulation format is also classified for single carrier, orthogonal frequency division multiplexing (OFDM) and MIMO through a statistical method. Finally, sequence design has been proposed to improve peak average power ratio for OFDM and multicarrier code division multiplexing (MC-CDMA). These sequences can also remove multiuse user interference for CDMA system over asynchronous system by improving the correlation properties within a code.

Performance evaluation of cooperative and non‐cooperative MIMO cognitive radio networks

In a multiple-input multiple-output (MIMO) cognitive radio network, the cognitive users (CUs) coexist with the primary users (PUs) in the same spectrum for boosting the spectrum efficiency. Based on the informed limit of the interference power from the PUs, the CUs' compete to minimise the total interference power generated. In this study, noncooperative and cooperative game theoretic approaches are derived for network interference management (NIM). In the first scenario, the NIM problem computes the sum of minimum individual CU's interference power produced on the PU to converge to equilibrium by using the best response iteration method. The second one, NIM estimates Nash product to converge to Nash bargaining solution by using the dual decomposition method. Numerical results prove the effectiveness of the cooperative scenario that minimises the network interference power. Two new metrics are proposed to evaluate the performance in these scenarios. The interference cooperative loss metric estimates the overall network interference power loss from CUs cooperation rather than non-cooperation in the game theoretic solution. Capacity cooperative gain metric estimates the overall network capacity gain from CUs cooperation rather than non-cooperation in the game theoretic solution for a fixed acceptable interference level.

Toward Overcoming a Hidden Terminal Problem Arising in MIMO Cognitive Radio Networks: A Tensor-Based Spectrum Sensing Algorithm

As the main enablers of cognitive radio (CR), numerous spectrum sensing techniques have been proposed to date. Despite the numerous techniques, the existing spectrum sensing techniques tend to fail in rendering an efficient spectrum sensing whenever a hidden terminal problem arises. Meanwhile, this problem can happen at any time in any severely fading primary-to-secondary channels resulting in very low primary signal-to-noise ratios (SNRs) and hence ineffective detection of the primary user. Toward overcoming this problem, by introducing a tensor-based hypothesis testing framework, this paper proposes an efficient tensor-based detector (TBD) for a multiple-input multiple-output (MIMO) CR networks over multi-path fading channels. For the proposed spectrum sensing technique, insightful asymptotic performance analyses are provided and Monte-Carlo simulations that assess its performance have been conducted. These simulations corroborate that TBD outperforms the generalized likelihood ratio test (GLRT) detector and the maximum-minimum eigenvalue (MME) detector, especially in the very low SNR regime which is a manifestation of the hidden terminal problem. Moreover, the simulations validate the derived asymptotic performance characterizations.

Maximizing Secrecy Capacity of Underlay MIMO-CRN Through Bi-Directional Zero-Forcing Beamforming

This paper presents the optimal physical-layer security scheme of both primary user (PU) and secondary user (SU) in the underlay multiple-input multiple-output cognitive-radio network (MIMO-CRN) by use of a bi-directional zero-forcing beamformer. The proposed new method enables the PU to communicate along with the SUs through a relay node without sacrificing their individual secrecy capacity, i.e., without causing interference to each other even in the presence of eavesdropper(s). In the first phase, a transmitting beamformer at the PU/SU transmitters and a receiving beamformer at the relay have been adopted to separate PU and SU data. In the second phase, a bi-directional beamformer has been applied to eradicate the necessity of involving artificial noise for preventing the active eavesdropper(s). To maximize the total secrecy capacity, a generalized non-linear optimization problem has been formulated and converted to a simplified constrained optimization problem by utilizing the beamformer and the associated subspace restriction. Then, this problem is solved by the Lagrangian method. Both theoretical and numerical analyses of the total and individual ergodic secrecy rates are provided to demonstrate the effectiveness of our proposed method. The secrecy outage probability is also derived to evaluate the performance variations of our proposed scheme when the channel-estimation error occurs.

Robust Cooperative Spectrum Sensing for MIMO Cognitive Radio Networks Under CSI Uncertainty

This paper considers the problem of cooperative spectrum sensing in multiuser multiple-input multiple-output cognitive radio networks considering the presence of uncertainty in the channel state information (CSI) of the secondary user channels available at the fusion center. Several schemes are proposed that employ cooperative decision rules based on local sensor decisions transmitted to the fusion center by the cooperating nodes over an orthogonal multiple access channel. First, fusion rules are derived under perfect CSI at the fusion center for both antipodal and nonantipodal signaling. Then, a robust detector, termed the uncertainty statistics-based likelihood ratio test, which optimally combines the decisions of different secondary users, is obtained for scenarios with CSI uncertainty. A generalized likelihood ratio test based robust detector is also derived for this scenario. Closed-form expressions are obtained to characterize the probabilities of false alarm $(P_{\text{FA}})$ and detection $(P_D)$ at the fusion center. Simulation results are presented to compare the performance of the proposed schemes with that of the conventional uncertainty agnostic detectors and also to corroborate the analytical expressions developed.

Energy-efficient transceiver designs for multiuser MIMO cognitive radio networks via interference alignment

This paper studies the transceiver design for multiuser multiple-input multiple-output cognitive radio networks. Different from the conventional methods which aim at maximizing the spectral efficiency, this paper focuses on maximizing the energy efficiency (EE) of the network. First, we formulate the precoding and decoding matrix designs as optimization problems which maximize the EE of the network subject to per-user power and interference constraints. With a higher priority in accessing the spectrum, the primary users (PUs) can design their transmission strategies without awareness of the secondary user (SU) performance. Thus, we apply a full interference alignment technique to eliminate interference between the PUs. Then, the EE maximization problem for the primary network can be reformulated as a tractable concave-convex fractional program which can be solved by the Dinkelbach method. On the other hand, the uncoordinated interference from the PUs to the SUs cannot be completely eliminated due to a limited coordination between the PUs with the SUs. The secondary transceivers are designed to optimize the EE while enforcing zero-interference to the PUs. Since the EE maximization for the secondary network is an intractable fractional programming problem, we develop an iterative algorithm with provable convergence by invoking the difference of convex functions programming along with the Dinkelbach method. In addition, we also derive closed-form expressions for the solutions in each iteration to gain insights into the structures of the optimal transceivers. The simulation results demonstrate that our proposed method outperforms the conventional approaches in terms of the EE.

Energy-Efficient Power Control Algorithms in Massive MIMO Cognitive Radio Networks

To achieve the maximum network energy efficiency (EE) and guarantee the fairness of EE among cognitive users (CUs), respectively, in the massive multiple-input multiple-output cognitive radio network, we investigate two power optimization problems: network EE optimization problem (NEP) and fair EE optimization problem (FEP) under a practical power consumption model. Because of the fractional nature of EE and the interference, both NEP and FEP are non-convex and NP-hard. To tackle these issues, we propose two energy-efficient power control algorithms, in which we decompose NEP/FEP into two steps, and solve them with an alternating iterative optimization scheme. Specifically, in the first step, for an initial transmit power, the maximum network EE/fair EE is achieved by the bisection method based on fractional programming; then, with the achieved EE, in the second step, the adapted optimal transmit power can be obtained by an efficient iterative algorithm based on sequential convex programming. These two steps are performed alternately until the stop conditions are reached. Numerical results confirm the fast convergence of these proposed algorithms and demonstrate their effectiveness with high network EE and well fairness of EE among CUs. Furthermore, it is illustrated that, under a practical power consumption model, more cognitive base station antennas would cause some loss of network EE but bring some improvements on the network spectral efficiency (SE), whereas higher circuit power consumption would reduce the network EE but only slightly affect the network SE.

Energy-Efficient Joint Power Allocation in Uplink Massive MIMO Cognitive Radio Networks With Imperfect CSI

In this paper, a joint pilot and data power allocation problem with max-min fair energy efficiency (EE) guarantee in the uplink massive multiple-input multiple-output cognitive radio networks is investigated. Given the fractional objective function, channel estimation errors, and inter-user interference, the joint allocation problem is formulated as a nonconvex and NP-hard problem. To tackle this, we transform the original problem into its convex form by introducing auxiliary variables and variable substitution, and then address it with the help of the Lagrangian dual method. Since the optimization variables are interrelated and interact on each other, it is difficult to directly obtain the closed-form solution to this problem. To settle this issue, we propose an alternative iterative algorithm to achieve the optimal power policy by a gradient-based adaption method, with its corresponding optimal Lagrangian multipliers obtained by the subgradient method. Numerical results show that the proposed approach has the best minimum EE performance and decent spectral efficiency performance. Besides, compared with the other schemes, significant saving in total transmit power and good cognitive user fairness are achieved by the proposed algorithm.

Transmit Precoding in Underlay MIMO Cognitive Radio With Unavailable or Imperfect Knowledge of Primary Interference Channel

This paper addresses the problem of precoder design that maximizes the throughput of a secondary user (SU) in an underlay multiple-input multiple-output cognitive radio network, where the channel state information (CSI) from the SU to the primary user (PU) is unavailable or inaccurate. The design maintains the quality of service for the PU through an interference amount measure in terms of the interference temperature or the leakage rate. For the case of unknown CSI, we propose an iterative adaptation algorithm by exploiting the side information in the primary communication network. For the case of imperfect CSI, we model the amount of uncertainty to be within a convex set defined by the Schatten norm and apply the maximin optimization to obtain the solution. To complete this paper, we derive the conditions on the CSI uncertainty radius under which the robust design with imperfect CSI would not perform better than the one with unknown CSI, when using the interference temperature metric. The proposed techniques are supported by numerical simulations.

Resource optimisation using bandwidth‐power product for multiple‐input multiple‐output orthogonal frequency‐division multiplexing access system in cognitive radio networks

This study investigates resource allocation problems for a point-to-point multi-carrier multiple-input multiple-output cognitive radio network. Different from conventional resource optimisation problems, a joint power and bandwidth resource optimisation framework using the novel optimisation metric, namely bandwidth-power product, is developed. Besides, rate requirement of the secondary system is satisfied, and the interferences introduced to the primary users (PUs) are below threshold of tolerance. The optimal source precoding matrix is designed and two methods, namely the project-channel singular value decomposition (SVD) and direct-channel SVD methods, are applied to satisfy interference power constraints for PUs. Then the unified power and channel allocation problem is derived and found to be a mixed-integer programming problem. Hence, a sub-optimal and tractable algorithm with low complexity is proposed. The innovative idea is to determine the channel resource budget by selecting the best channels, where the criterion for evaluating the quality of channel is detailed discussed. Then the power optimisation subproblem and channel allocation subproblem can be performed independently using the Lagrange-duality theory and Gauss–Newton method, respectively. The simulation results show significant improvement in spectral efficiency by using this framework compared to classical power optimisation framework using the waterfilling scheme.

MSE-Based Transceiver Designs for Full-Duplex MIMO Cognitive Radios

We study two scenarios of full-duplex (FD) multiple-input–multiple-output cognitive radio networks: FD cognitive ad hoc networks and FD cognitive cellular networks. In FD cognitive ad hoc networks (also referred as interference channels), each pair of secondary users (SUs) operate in FD mode and communicate with each other within the service range of primary users (PUs). Each SU experiences not only self-interference but also interuser interference from all other SUs, and all SUs generate interference on PUs. We address two optimization problems: one is to minimize the sum of mean-squared errors (MSE) of all estimated symbols, and the other is to minimize the maximum per-SU MSE of estimated symbols, both of which are subject to power constraints at SUs and interference constraints projected to each PU. We show that these problems can be cast as a second-order cone programming, and joint design of transceiver matrices can be obtained through an iterative algorithm. Moreover, we show that the proposed algorithm is not only applicable to interference channels but also to FD cellular systems, in which a base station operating in FD mode simultaneously serves multiple uplink and downlink users, and it is shown to outperform HD scheme significantly.

Efficient transceiver beamforming in multiple‐input–multiple‐output cognitive radio network

Owing to the ability of improving spectrum utilisation and quality-of-service (QoS) in wireless communications, multiple-input–multiple-output (MIMO) cognitive radio (CR) network has been identified as an excellent choice for the next generation communication. To protect primary users (PUs) from excessive interference while ensuring a meaningful QoS of secondary users (SUs), one key challenge of such system is the joint design of transceiver beamforming for SUs. In this study, the authors investigate the beamforming in MIMO CR network, and two optimisation problems in terms of the minimisation of the total transmission power and the maximisation of the smallest received signal-to-interference-plus-noise ratio (SINR) are considered. The problems can be formulated as indefinite quadratic optimisation programs which are known to be non-convex NP-hard. Hence, a distributed algorithm based on semi-definite programming and minimum-mean-squared-error is proposed to achieve the optimal beamforming weight vector. The findings of this study demonstrated that the proposed algorithm outperforms the existing ones with respect to the total transmitted power and SUs SINR.

Robust Estimator-Correlator for Spectrum Sensing in MIMO CR Networks with CSI Uncertainty

This work introduces novel detection schemes which are robust with respect to the uncertainty in the estimate of the signal covariance matrix for non-coherent spectrum sensing in multiple-input multiple-output (MIMO) cognitive radio networks. We employ an eigenvalue perturbation theory based approach to model the uncertainty in the estimated signal covariance matrix. Subsequently, we derive an optimization framework for the generalized likelihood ratio test (GLRT) based robust test statistic detector (RTSD) and robust estimator-correlator detector (RECD) towards primary user detection, which incorporate the channel state information (CSI) uncertainty inherent in such scenarios. Further, employing the Karush-Kuhn-Tucker (KKT) conditions, we derive closed form expressions for the proposed robust spectrum sensing schemes. Simulation results demonstrate the superior performance of the proposed robust detectors in comparison to the uncertainty agnostic estimator-correlator (EC) detector for spectrum sensing in MIMO cognitive radio networks with CSI uncertainty.

Joint Transceiver Beamforming in MIMO Cognitive Radio Network Via Second-Order Cone Programming

This paper considers the spectrum sharing multiple- input-multiple-output (MIMO) cognitive radio network, in which multiple primary users (PUs) coexist with multiple secondary users (SUs). Joint transceiver cognitive beam former design is introduced to minimize the transmit power of the SU base station (SBS) while simultaneously targeting lower bounds on the received signal-to-interference-plus-noise ratio (SINR) for the SUs and imposing upper limits on the interference temperature to the PUs. With the perfect knowledge of all links, the optimal secondary transceiver beam former is achieved iteratively. Due to the limited cooperation between SBS and PUs, perfect information of primary links may not be available at SBS which could lead to severe interference to the PUs. Robust designs are developed against the uncertainties in the primary links by keeping the interference to the PU below a prespecifled threshold with high probability. Simulation results are presented to validate the effectiveness of the proposed algorithms that minimizes the total transmit power and simultaneously guarantees quality-of-service (QoS) of both SUs and PUs.

Joint beamforming and power control in MIMO cognitive radio networks

In this paper, the problem of joint beamforming and power control is studied in the uplink of multiple-input multiple-output (MIMO)cognitive radio networks (CRNs). The objective is maximizing the minimum signal-to-interference-plus-noise ratio (SINR) of the secondary users (SUs). The CRN consists of a cognitive radio-base station (CR-BS) and a number of SUs. The proposed scheme uses genetic algorithm (GA) to solve the optimization problem by considering both the quality of service (QoS) constraints of the primary network and the maximum transmission power constraints of the CRN.