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.

The massive deployment of small cells in 5G networks represents an alternative to meet the ever increasing mobile data traffic and to provide very-high throughout by bringing the users closer to the Base Stations (BSs). This large increase in the number of network elements demands a significant increase in the energy consumption and carbon footprint followed by complex interference management. In order to address these challenges, we consider multi-level Sleep Mode (SM) where BS components with similar activation/deactivation times can be put to sleep. The deeper and higher energy efficient the SM is, the longer it will take the BS to activate, which might impose degradation in the Quality of Service (QoS). While this adds operational flexibility to the BS, it brings complex management to the operator. In this paper, we consider a heterogeneous network architecture where small cells can switch to different SM levels to save energy and reduce dropping rate. We propose a reinforcement learning algorithm for small cells that adapts their activities subject to service delay constraint. In this regard, the algorithm intelligently learns from the environment based on the co-channel interference, the cell buffer size and the expected cell throughput in order to decide the best SM policy. Numerical values show that important energy savings can be obtained with an acceptable dropping rate. Moreover, we show that while offloading users to the macro cell can significantly reduce their delay, dropping rate and the cluster energy consumption, it comes at a cost of decreasing the network energy efficiency up to 5 times compared with the case of no offload.

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.

Considering the exponential increase in mobile traffic, requiring denser cellular access networks, the use of renewable energy (RE) to power base stations (BSs) may contribute to reduction of the huge operational cost faced by mobile network operators (MNOs) due to power supply. Furthermore, the smart grid (SG) paradigm is deeply changing the energy market, envisioning an active interaction between grid and customers. Hence, MNOs can combine renewable-powered BSs with properly designed energy management strategies to improve the interaction with the SG, with the twofold objective of reducing the energy bill and providing ancillary services. We propose a stochastic model to investigate a renewable-powered mobile network dynamically adapting its energy consumption to accomplish SG requests, and receiving rewards in return, by exploiting two techniques. First, resource on demand (RoD) can dynamically switch off unneeded BSs. Second, an energy management policy takes efficient decisions about using/harvesting locally produced RE, depending on SG requests. The proposed techniques highly increase the probability of responding to ancillary service demands, up to 90%, also depending on the RE system size, with RoD raising, by up to 20%, the probability of accomplishing SG requests. Consequently, cost saving can become equal to the total cost or even greater, providing revenues to the MNO.

Accumulation of dust on the surface of solar panels distract the light from reaching solar cells. This leads to significant degradation in the performance of solar panels in terms of output power. In this paper, we present a simple and low cost cleaning system to mitigate the effect of dust on the performance of solar panels. The mechanical as well as the electrical subsystems are described, an algorithm for controlling the cleaning mechanism is proposed. Results show that the cleaning system allows the solar panel to provide regular power output.