Spectrum Sensing (SS) plays an essential role in Cognitive Radio (CR) networks to diagnose the availability of frequency resources. In this paper, we aim to provide an in-depth survey on the most recent advances in SS for CR. We start by explaining the Half-Duplex and Full-Duplex paradigms, while focusing on the operating modes in the Full-Duplex. A thorough discussion of Full-Duplex operation modes from collision and throughput points of view is presented. Then, we discuss the use of learning techniques in enhancing the SS performance considering both local and cooperative sensing scenarios. In addition, recent SS applications for CR-based Internet of Things and Wireless Sensors Networks are presented. Furthermore, we survey the latest achievements in Spectrum Sensing as a Service, where the Internet of Things or the Wireless Sensor Networks may play an essential role in providing the CR network with the SS data. We also discuss the utilisation of CR for the 5th Generation and Beyond and its possible role in frequency allocation. With the advancement of telecommunication technologies, additional features should be ensured by SS such as the ability to explore different available channels and free space for transmission. As such, we highlight important future research axes and challenging points in SS for CR based on the current and emerging techniques in wireless communications.

In Cognitive Radio (CR) networks, Primary User (PU) and Secondary User (SU) coexist to efficiently share the spectrum. PU has the right to access its dedicated channel at any time, while SU, operating in an opportunistic mode. can access only when PU is absent. Thus, SU should continuously monitor the channel to avoid any interference with PU when transmitting. Several factors, such as fading and shadowing adversely impact the PU SNR at the SU receiver making the Spectrum Sensing (SS) process more challenging. Recently, Intelligent Reflecting Surface (IRS) has been proposed to control the propagation channel for wireless systems. Introducing IRS in CR networks impacts the SS performance because of altering the channel. In this paper, we investigate the effect of deploying IRS on the SS by considering two scenarios: in (S1) the IRS is configured to enhance the PU signal at SU, while in the second scenario (S2), the IRS is configured to assist the Primary Receiver (PR). First, we highlight several important challenges and research directions. Then, we derive the analytical average detection probability for both (S1) and (S2). Results show that deploying IRS can significantly enhance SS even when the IRS is deployed to assist the PR.

The blind recognition of communication parameters is a key research issue for commercial and military communication systems. In this paper, we investigate the problem of the blind recognition of linear space-time block codes (STBC). To characterize the space time coding, we propose to compute a time-lag correlation of the received samples. Provided the number of transmitters, the noise variance and the symbol timing are well estimated, we show that the theoretical values of the correlation norm only depend on the STBC and are affected by neither the channel nor the symbol modulation. The automatic recognition of the STBC is realized by selecting the STBC which minimizes the distance between the theoretical values and the experimental ones. Simulations show that our method performs well even for low signal to noise ratio (0 dB).

In cognitive radio wireless sensor networks (CRSN), the nodes act as secondary users. Therefore, they can access a channel whenever its primary user (PU) is absent. Thus, the nodes are assumed to be equipped with a spectrum sensing (SS) module to monitor the PU activity. In this manuscript, we focus on a clustered CRSN, where the cluster head (CH) performs SS, gathers the data, and sends it toward a central base station by adopting an ad hoc topology with in-network data aggregation (IDA) capability. In such networks, when the number of clusters increases, the consumed energy by the data transmission decreases, while the total consumed energy of SS increases, since more CHs need to perform SS before transmitting. The effect of IDA on CRSN performance is investigated in this manuscript. To select the best number of clusters, a study is derived aiming to extend the network lifespan, taking the SS requirements, the IDA effect, and the energy consumed by both SS and transmission into consideration. Furthermore, the collision rate between primary and secondary transmissions and the network latency are theoretically derived. Numerical results corroborate the efficiency of IDA to extend the network lifespan and minimize both the collision rate and the network latency.

This paper introduces a novel spatial domain-based self-interference cancellation (SIC) method named constrained minimum mean square error (C-MMSE) for massive multiple-input multiple-output (mMIMO) full-duplex (FD) communication systems. The main idea involves treating the self-interference (SI) signal emitted from an FD node as a distinct spatial stream arriving at the receiver part of that same FD node. This SI signal needs to be spatially postcoded alongside other relevant signals coming from different transmitters, ensuring it falls into the null space of the MIMO channel, which includes the FD node transmitter part as an input. To establish this approach, we first adapt the expressions of spatial combiners for conventional zero forcing (ZF) and minimum mean square error combining (MMSE) criteria. We demonstrate that MMSE alone is insufficient for efficient SI signal cancellation unless an additional constraint is added to enable proper SIC. Consequently, we design the innovative C-MMSE combiner and derive its expression. In addition to our proposal, the originality of our work stems from employing a spherical wave model (SWM) to model the SI channel. The choice is justified by the proximity of the transmit and receive antenna panels in the FD node. We examine and compare the SIC performance of the modified ZF combiner, the modified MMSE combiner, and the newly introduced C-MMSE combiner by evaluating the spectral efficiency (SE). Additionally, we highlight the robustness of the SWM-based SI channel modeling in comparison to conventional planar wave modeling (PWM), emphasizing the relevance of its usage.

This paper explores the integration of Location Information-based Routing (LIBRO) protocol into Energy Harvesting-based Cognitive Radio Wireless Sensor Networks (EH-CWSNs) to address energy consumption challenges and ensure sustainable operation. LIBRO has been recently proposed as an efficient routing algorithm for traditional WSN networks. In this paper, we extend its application to EH-CWSNs, in which the WSN nodes act as Secondary Users (SUs). By proposing LIBRO-based EH-CWSN, we aim to eliminate energy constraints on the nodes and reduce the need for frequent battery replacements in EH-CWSN, thus enabling deployment in challenging environments. Additionally, LIBRO’s unique feature of minimizing nodes’ cooperation during routing offers advantages for protecting Primary Users (PUs) against interference caused by secondary transmissions. The paper’s primary goal is to adapt LIBRO to the CWSN functionalities, especially the spectrum sensing operation. Then, EH-CWSN network parameters are optimized to ensure lower energy consumption compared to the harvested energy from EH units, establishing a sustainable network while respecting PU collision limits. Through a multi-objective optimization approach, we seek to strike a balance between collision probability with PUs, energy consumption, packet delivery latency, and network throughput, creating a self-powered network that meets various constraints. Numerical findings demonstrate the effectiveness of LIBRO in enhancing energy and spectral efficiencies in EH-CWSNs compared to state-of-the-art routing protocols.