Hongjian Sun

Wideband spectrum sensing for cognitive radio networks: a survey

Cognitive radio has emerged as one of the most promising candidate solutions to improve spectrum utilization in next generation cellular networks. A crucial requirement for future cognitive radio networks is wideband spectrum sensing: secondary users reliably detect spectral opportunities across a wide frequency range. In this article, various wideband spectrum sensing algorithms are presented, together with a discussion of the pros and cons of each algorithm and the challenging issues. Special attention is paid to the use of sub-Nyquist techniques, including compressive sensing and multichannel sub- Nyquist sampling techniques.

Adaptive Power Allocation Schemes for Spectrum Sharing in Interference-Alignment-Based Cognitive Radio Networks

Interference alignment (IA) is a promising technique for interference management and can be applied to spectrum sharing in cognitive radio (CR) networks. However, the sum rate may fall short of the theoretical maximum, particularly at low signal-to-noise ratio (SNR), and the quality of service (QoS) of the primary user (PU) may not be guaranteed. In addition, power allocation (PA) in IA-based CR networks is largely ignored, which can further improve its performance. Thus, in this paper, PA in IA-based CR networks is studied. To guarantee the QoS requirement of the PU, its minimal transmitted power is derived. Then, we propose three PA algorithms to maximize the throughput of secondary users (SUs), the energy efficiency (EE) of the network, and the requirements of SUs, respectively, while guaranteeing the QoS of the PU. To reduce the complexity, the closed-form solutions of these algorithms are further studied in detail. The outage probability of the PU according to its rate threshold is also derived to analyze the performance of these algorithms. Moreover, we propose a transmission-mode adaptation scheme to further improve the PUs performance when its QoS requirement cannot be guaranteed at low SNR, and it can be easily combined with the proposed PA algorithms. Simulation results are presented to show the effectiveness of the proposed adaptive PA algorithms for IA-based CR networks.

Wideband Spectrum Sensing With Sub-Nyquist Sampling in Cognitive Radios

Multi-rate asynchronous sub-Nyquist sampling (MASS) is proposed for wideband spectrum sensing. Corresponding spectral recovery conditions are derived and the probability of successful recovery is given. Compared to previous approaches, MASS offers lower sampling rate, and is an attractive approach for cognitive radio networks.

Computationally Tractable Model of Energy Detection Performance over Slow Fading Channels

Energy detection (ED) has been widely used for detecting unknown deterministic signals in many wireless communication applications, e.g., cognitive radio, and ultra-wideband (UWB). However, the performance analysis of ED over slow fading channels is cumbersome, because it is difficult to derive closed-form expressions for the average probability of detection involving the generalised Marcum Q-function and the log-normal distribution. In this letter, we derive an approximation of the average probability of detection over a slow fading channel by replacing the log-normal distribution with a Wald distribution. In addition, we analyze the detection performance of the ED using a square-law combining scheme over multiple independent and identically distributed slow fading channels.

A Novel Interference Alignment Scheme Based on Sequential Antenna Switching in Wireless Networks

Interference alignment (IA) is a promising technique that can effectively eliminate the interference in wireless networks. However, in traditional IA schemes, the signal to interference plus noise ratio (SINR) may significantly degrade, and the quality of service (QoS) may be unacceptable. In this paper, a novel IA scheme based on antenna switching (AS-IA) is proposed to improve the SINR of the received signal while guaranteeing the QoS in IA wireless networks. In the proposed scheme, some of the antennas are replaced by reconfigurable ones that can switch among preset modes, and the best channel coefficients are selected. Furthermore, to reduce the computational complexity, a sequential antenna switching IA (SAS-IA) scheme is proposed with only one antenna switching in each time slot, and the communication proceeds during the process of searching for the optimal solution. To further improve the performance of the SAS-IA scheme under imperfect channel state information (CSI), a filtering SAS-IA scheme is proposed through averaging the estimated CSI during the iterations of the distributed IA algorithm. Simulation results are presented to show the effectiveness and efficiency of the proposed schemes in improving the QoS of IA wireless networks.

Adaptive Compressive Spectrum Sensing for Wideband Cognitive Radios

This letter presents an adaptive spectrum sensing algorithm that detects wideband spectrum using sub-Nyquist sampling rates. By taking advantage of compressed sensing (CS), the proposed algorithm reconstructs the wideband spectrum from compressed samples. Furthermore, an \ell_2 norm validation approach is proposed that enables cognitive radios (CRs) to automatically terminate the signal acquisition once the current spectral recovery is satisfactory, leading to enhanced CR throughput. Numerical results show that the proposed algorithm can not only shorten the spectrum sensing interval, but also improve the throughput of wideband CRs.

An energy-efficient cooperative spectrum sensing scheme for cognitive radio networks

Rapidly rising energy costs and increasingly rigid environmental standards have led to an emerging trend of addressing “energy efficiency” aspect of wireless communication technologies. Cognitive radio can play an important role in improving energy efficiency in wireless networks. In this paper, we propose an energy-efficient and time-saving one-bit cooperative spectrum sensing scheme, which has two stages. If the signal-to-noise ratio (SNR) is high or no primary user exists, only one stage of coarse spectrum sensing is needed, by which the sensing time and energy are saved. Otherwise, the second stage of fine spectrum sensing will be performed to increase the spectrum sensing accuracy. Furthermore, only one-bit decision is sent by each secondary user to minimize the overhead. Plenty of simulation is performed, and the results show that the sensing time and energy consumption are both reduced significantly in the proposed scheme.

Energy Imbalance Management Using a Robust Pricing Scheme

This paper focuses on the problem of energy imbalance management in a microgrid. The problem is investigated from the power market perspective. Unlike the traditional power grid, a microgrid can obtain extra energy from a renewable energy source (RES) such as a solar panel or a wind turbine. However, the stochastic input from the RES brings difficulty in balancing the energy supply and demand. In this study, a novel pricing scheme is proposed that provides robustness against such intermittent power input. The proposed scheme considers possible uncertainty in the marginal benefit and the marginal cost of the power market. It uses all available information on the power supply, power demand, and imbalanced energy. The parameters of the scheme are evaluated using an performance index. It is shown that the parameters can be obtained by solving a linear matrix inequality problem, which is efficiently solvable due to its convexity. Simulation examples are given to show the favorable performance of the proposed scheme in comparison with existing area control error pricing schemes.linear matrix inequality problem,area control error pricing schemes.

Adaptive Energy-Efficient Power Allocation in Green Interference-Alignment-Based Wireless Networks

Interference alignment (IA) is a promising technique for interference management in wireless networks. However, the sum rate may fall short of the theoretical maximum, particularly at low signal-to-noise ratio (SNR) levels since IA mainly concentrates on mitigating the interference, instead of improving the quality of desired signal. Moreover, most of the previous works focused on improving spectrum efficiency, but the energy efficiency (EE) aspect is largely ignored. In this paper, an adaptive energy-efficient IA algorithm is proposed through power allocation (PA) and transmission-mode adaptation for green IA-based wireless networks. The PA problem for IA is first analyzed; then, we propose a PA scheme that optimizes the EE of IA-based wireless networks. When the SNR is low, the transmitted power of some users may become zero. Thus, the users with low transmitted power are turned into the sleep mode in our scheme to save energy. The transmitted power and transmission mode of the remaining active users are adapted again to further improve the EE of the network. To guarantee the interests of all the users, fairness among users is also considered in the proposed scheme. Simulation results are presented to show the effectiveness of the proposed algorithm in improving the EE of IA-based wireless networks.

Cooperative Wideband Spectrum Sensing Over Fading Channels

In cognitive radio (CR) systems, it is crucial for secondary users to reliably detect spectral opportunities across a wide frequency range. This paper studies a novel multirate sub-Nyquist spectrum sensing (MS3) system capable of performing wideband spectrum sensing in a cooperative CR network over fading channels. The aliasing effects of sub-Nyquist sampling are modeled. To mitigate such effects, different sub-Nyquist sampling rates are applied such that the numbers of samples at different CRs are consecutive prime numbers. Moreover, the performance of MS3 over fading channels (Rayleigh fading and lognormal fading) is analyzed in the form of bounds on the probabilities of detection and false alarm. The key finding is that the wideband spectrum can be sensed using sub-Nyquist sampling rates in MS3 over fading channels, without the need for spectral recovery. In addition, the aliasing effects can be mitigated by the use of different sub-Nyquist sampling rates in a multirate sub-Nyquist sampling system.