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Spectrum sensing (SS) is substantial demand for enabling cognitive radio (CR) systems. In recent years, digital beamforming (DBF) is exploited for performance enhancement of multiple antenna elements or uniform linear array (ULA) based spectrum sensing techniques. Generally, a ULA suffers from its high side lobe level (SLL) and has a relatively low realized array gain. Subsequently, the ULA-based CR receiver is compelled to provide low detection performance. Furthermore, the efforts to increase the realized array gain are still not sufficient to dramatically increase the detection performance, because the problem of high SLL remains a challenging issue. In this paper, a highly efficient approach is introduced for performance enhancement of ULA-based SS techniques using side lobe level reduction (SLLR) beamforming that is applied to the receiving ULA. Worthy, a large SLLR combined with maintaining the same half power beamwidth (HPBW) as the original array, results in a synthesized array with a significantly improved gain. Thence, both the received signal-to-noise ratio (SNR) and signal-to-interference plus noise ratio (SINR) of the combined received signal are significantly enhanced at the CR receiver, which would considerably increase the detection capability of the proposed system. The proposed approach is investigated by integrating the SLLR-based antenna array with the generalized likelihood ratio test/direction of arrival (GLRT/DOA)-based SS technique that is denoted as GD technique. In contrast to the ULA, the synthesized array's excitation coefficients are non-uniform, necessitating special signal processing to incorporate it in the received signal model and derive closed-form expressions for the probability of detection, probability of false alarm, and decision threshold, all of which are already reported in this work. Several simulation scenarios are performed to validate the supremacy of the proposed SS approach compared to the traditional single antenna element based techniques and other state of the art beamforming based techniques. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) technology has found extensive application in the wireless communication domain, offering significant enhancements in communication performance by manipulating the reflection of electromagnetic waves. This research article delves into the domain of UAV-assisted wideband cognitive radio networks augmented by IRS and underpinned by sensing-based spectrum sharing techniques. We introduce UAVs as alternative solutions for both primary and secondary base stations to optimize the management of spectral resources. Our primary objective centers around the joint optimization of both the trajectories of the primary and secondary UAVs, power allocation at the secondary UAVs, reflection coefficients of intelligent reflecting surfaces, sensing time, and subcarrier allocation, all aimed at maximizing the achievable rate of secondary users. Given that the problem at hand is non-convex, we employ deep reinforcement learning algorithms for resolution. To address the challenge of mixed-action spaces, we implement a novel Dueling DQN-Twin Delayed Deep Deterministic policy gradient (DDQN-TD3) algorithm. The simulation outcomes illustrate that the methodology introduced in this paper substantially amplifies the performance of CR system when contrasted with benchmark methods. This is evident in the improved spectrum efficiency, elevated data transmission rates, and the minimization of interference with the primary network. [ABSTRACT FROM AUTHOR]

The cognitive ability of Cognitive Radio (CR) can adjust appropriate transmission parameters according to different channel conditions and adapt to dynamic communication environments. Channel quality evaluation is a key technology of adaptive communication systems, and its real-time evaluation results provide a direct basis for adaptive communication transmission scheme switching. So it is necessary to study the estimation algorithms of signal-to-noise ratio (SNR). Several SNR estimation algorithms based on additive white Gaussian noise channel (AWGN) are studied in this paper, analyze and compare through theoretical derivation and computer simulation. The simulation results of the computer MATLAB software show that the algorithm introduced in the article has a high estimation accuracy in the entire range of the SNR simulation interval of 0 to 15dB, and can accurately reflect the current channel conditions to facilitate the CR system adjusts the communication strategy in time, provides a certain reference basis for CR channel estimation decision-making. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the bit error rate (BER) analysis of the scheduled transmit antenna selection (TAS) with maximal ratio combining (MRC) for the cognitive radio multiple-input multiple-output (CR-MIMO) systems. As a first step, this paper derives approximate probability density functions (PDFs) of the effective signal to noise ratio (SNR) of the proposed scheduled TAS with MRC in CR-MIMO systems. Next, the tight closed-form expressions of the BER for the M -ary QAM and M -ary PSK modulations are presented, respectively. Using asymptotic analysis, we approximate the BER for the scheduled system, and quantify the diversity order and SNR gain, respectively. Analytical results show that the diversity order of the scheduled system is the product of spatial diversity (SD) and multiuser diversity (MUD). Thus, BER performance of the proposed system is improved as the number of users increases by MUD regardless of modulation types. Also, the SNR gain is getting worse as the number of received antennas at the primary system increases regardless of modulation types. [ABSTRACT FROM AUTHOR]

Non-Orthogonal multiple access (NOMA) and Cognitive radio (Cr) are seen as one of the most promising techniques, which improves the utilization of the spectrum in 5G. The expanding number of wireless applications like new gadgets, IOT brought about developing a block in the ISM groups. The FCC requested to permit unlicensed clients to work in the void area without obstruction to an authorized guest. Cr gives an answer for an extra range prerequisite issue for productive spectrum usage. The foremost condition for permitting CRs to utilize spectrum is not causing obstruction to licensed users. Spectrum sensing permit secondary users (Su) to separately recognize the idle portions of the spectrum, and thus evade obstruction to licensed users. In existing spectrum sensing techniques, SU can only utilize the unused spectrum when PU is not present. Therefore, spectrum exploitation of the conventional system is very low. In recent times NOMA has been projected to utilize the spectrum in an efficient manner. The proposed work permits the SU to utilize a spectrum of PU, both at its absence. Spectrum sensing in NOMA is not explored so far. Hence, in this paper, NOMA based matched filter detection is designed for QAM-64 and QAM-256. Matlab simulation is applied to study the operation of the proposed detection technique in NOMA in respect of several parameters like bit error rate (BER) Vs signal to noise ratio (SNR), the probability of detection (Pd), and probability of false alarm (Pfa). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: Cognitive radio-based Long Term Evolution (LTE) networks benefit from the powerful features of cognitive Radios (CR), such as learning and reconfigurability, enabling them to perform, if required, channel switching to a channel with higher quality. This process of searching and sensing other channels is a restoration (recovery) process where the objective is to find the best channel in the shortest time. We propose in this paper a history-aware greedy restoration scheme triggered not only when the quality of the current operating channel of the user goes below a threshold, but at regular intervals. Based on the state of the current channel, our scheme computes the optimal number of channels to be sensed in this restoration period and this number is dynamically updated after each channel sensing result. Intrinsic features of learning and history-awareness of CRs are used to create the list of channels to be sensed based on the channels’ background and historical information. The sensing order improves the restoration mechanism by providing a shorter restoration time or a restored channel with a higher quality. We show that the proposed combined scheme provides improvements for the CR-based LTE network’s throughput, compared to other restoration schemes which work based on only greediness or history-awareness. [Copyright &y& Elsevier]

Abstract: In this paper, the performance of cognitive radio (CR) code division multiple access (CDMA) networks is analyzed in the presence of receive beamforming at the base stations (BSs). More precisely, we analyze, through simulations, the performance achievable by a CR user, with and without spectrum sensing, in a three-cell scenario. Uplink communications are considered. Three different schemes for spectrum sensing with beamforming are presented, together with a scheme without spectrum sensing. CR users belong to a cognitive radio network (CRN) which is coexisting with a primary radio network (PRN). Both the CRN and the PRN are CDMA based. The CRN is assumed to utilize beamforming for its CR users. Soft hand-off (HO) and power control are considered in both the CRN and the PRN. The impact of beamforming on the system performance is analyzed, considering various metrics. In particular, we evaluate the performance of the proposed systems in terms of outage probability, blocking probability, and average data rate of CR users. The results obtained clearly indicate that significant performance improvements can be obtained by CR users with the help of beamforming. The impact of several system parameters on the performance of the three considered spectrum sensing schemes with beamforming is analyzed. Our results, in terms of probability of outage, show that the relative improvement brought by the use of beamforming is higher in the absence of spectrum sensing (reduction of 80%) than in the presence of spectrum sensing (reduction of 42%). [Copyright &y& Elsevier]

In this paper, we address power and subcarrier allocation for cooperative cognitive radio (CR) networks in the presence of spectrum sensing errors. First, we derive the mutual interference of primary and secondary networks affecting each other by taking into account spectrum sensing errors. Then, taking into account the interference constraint imposed by the cognitive network to the primary user and the power budget constraint of cognitive network, we maximize the achievable data rates of secondary users. Besides, in a multi secondary user scenario, we propose a suboptimal but low complexity power and subcarrier allocation algorithm to solve the formulated optimization problem. Our numerical results indicate that the proposed power loading scheme increases the cognitive achievable data rates compared to classical power loading algorithms that do not consider spectrum sensing errors. [ABSTRACT FROM AUTHOR]

Either licensed or un-licensed spectrum resource is becoming increasingly demanding by many multimedia applications which are traffic intensive and quality sensitive. Cognitive Radio (CR) network offers a promising solution to meet this demand by fully utilizing available spectrum resource. In this paper, we studied a scalable video transmission scheme using LDPC code over Cognitive Radio networks, which allows Primary User (PU) and Secondary Users (SU) to fully utilize current unused resources and is to meet Quality-driven video transmission requirements. Specifically, the object-based scalable video coding and low-density parity-check (LDPC) coding are designed jointly for resisting channel errors and reducing the delay. We evaluate the performance of the proposed multimedia system and demonstrate the efficiency of the proposed scheme. [ABSTRACT FROM AUTHOR]

A mathematical model for computing the out-of-band (OOB) leakage radiation in NC- OFDM systems and the interference to the adjacent primary users (PUs) is presented in this paper. Based on this mathematical model, the optimal parameters could be obtained by solving the nonlinear programming (NLP) problem subject to secondary users (SUs)' spectrum overhead and PUs' occupancy rate r. The results showed the close relationship between the number of guard subcarriers and the parameters of input back off (IBO) as well as PU occupancy r and interference temperature tolerance. The simulation results have shown that, under the conditions of given PU occupancy r and PU interference temperature limit, the solution of proposed programming problem can be used to design the optimal system parameters for secondary users with the smallest number of guard subcarriers and the demand of system to the parameters of IBO. [ABSTRACT FROM AUTHOR]

Radio frequency (RF) subsampling can be used by radio receivers to directly down-convert and digitize RF signals. A goal of a cognitive radio/software defined ratio (CR/SDR) receiver design is to place the analog-to-digital converter (ADC) as near the antenna as possible. Based on this, a band-pass sampling (BPS) frontend for CR/SDR is proposed and verified. We present a receiver architecture based second-order BPS and signal processing techniques for a digital RF frontend. This paper is focused on the benefits of the second-order BPS architecture in spectrum sensing over a wide frequency band range and in multiband receiving without modification of the RF hardware. Methods to manipulate the spectra are described, and reconstruction filter designs are provided. On the basis of this concept, second-order BPS frontends for CR/SDR systems are designed and verified using a hardware platform. [ABSTRACT FROM AUTHOR]

The evolving 5G and beyond 5G (B5G) wireless technologies are envisioned to provide ubiquitous connectivity and great heterogeneity in communication infrastructure by connecting diverse devices and providing multifarious services. Recently, the Internet of Things (IoT) and unmanned aerial vehicles (UAVs) are realized as an essential component of the upcoming 5G/B5G networks, enabling enhanced communication capacity, high reliability, low latency, and massive connectivity. However, one limiting factor in the expansion of 5G/B5G technology is the finite radio spectrum, which necessitates managing the anticipated spectrum crunch for future wireless networks. One potential solution is to develop intelligent cognitive methods to dynamically optimize the use of spectrum in 5G/B5G networks to solve the imminent problem of spectrum congestion and improve radio efficiency. This paper addresses the opportunistic spectrum access (OSA) problem in the 5G/B5G cognitive radio (CR) network of IoTs and UAVs through the novel deep learning-based detector, dubbed as Deep-CRNet. The proposed detector employs residual connections with cascaded multi-kernel convolutions to identify the primary user (PU) spectrum usage by extracting the inherent multi-scale signal and noise features in the sensed transmission patterns. Thereby, Deep-CRNet intelligently learns and locates the spectrum holes so that secondary users (SUs) and PUs can dynamically share network spectrum resources. The efficacy of Deep-CRNet is validated through simulation results, where it achieved 99.74% accuracy with 99.65% precision and 99.83% recall in accurately classifying the PU status. In addition, the average correct detection probability of Deep-CRNet in the low signal-to-noise ratio (− 20 dB to − 15 dB) range is 38.21% higher than the second best-performing detector. • A DL-based framework is presented for OSA in CR-enabled 5G/B5G networks (Deep-CRNet). • Deep-CRNet OSA model deploys UAVs as aerial BS to serve remote IoT users (PUs & SUs). • It captures intrinsic multiscale signal-noise features in PU transmission patterns. • Using residual learning, it dynamically exploits the unfilled spectrum holes for OSA. • Deep-CRNet outperforms other benchmark detectors in correct detection probability. [ABSTRACT FROM AUTHOR]