Ensuring accurate traffic perception and road safety in complex urban environments remains a significant challenge. Advanced traffic monitoring increasingly relies on deep learning, which requires large data volumes. However, existing datasets are often limited to CCTV video footage or focus on dynamic scenarios captured by sensors mounted on ego vehicles. This narrow perspective reduces the effectiveness of comprehensive traffic monitoring, particularly for LiDAR sensors, which typically capture only the vehicle’s viewpoint and miss critical areas such as intersections and pedestrian crossings. To address these limitations, we propose a holistic strategy for rapid data collection in urban settings using simulated 3D intersections. Our approach introduces a point cloud collection framework using static LiDAR sensors to provide a global view of the entire traffic scene. By incorporating randomized traffic patterns observed from multiple angles, this method generates a diverse, comprehensive dataset for object detection and instance segmentation, showcasing its advantages for benchmarking smart mobility applications.

An unmanned aerial vehicle (UAV) integrated with the remote sensing technology of light detection and ranging (LiDAR) can provide accurate and real-time road traffic information. In this paper, we propose to equip UAVs with LiDAR sensors for Intelligent Transportation Systems (ITS) applications. The goal is to find the optimal 3D placement of multiple UAV-LiDAR (ULiDs) for a given road segmentation. We formulate an optimization problem to find the optimal placement such that the road coverage efficiency is maximized. The optimization problem is constrained by notable ULiD specifications such as field-of-view (FoV), point-cloud density, geographic information system (GIS) location, and road segment coverage priorities. We propose to use a computational intelligent algorithm based on particle swarm optimization to solve the problem. Finally, we illustrate the benefits of using our proposed algorithm over other baselines.

The proliferation of Light Detection and Ranging (LiDAR) technology in the automotive industry has quickly promoted its use in many emerging areas in smart cities and internet-of-things. Compared to other sensors, like cameras and radars, LiDAR provides up to 64 scanning channels, vertical and horizontal field of view, high precision, high detection range, and great performance under poor weather conditions. In this paper, we propose a novel aerial traffic monitoring solution based on Light Detection and Ranging (LiDAR) technology. By equipping unmanned aerial vehicles (UAVs) with a LiDAR sensor, we generate 3D point cloud data that can be used for object detection and tracking. Due to the unavailability of LiDAR data from the sky, we propose to use a 3D simulator. Then, we implement PointVoxel-RCNN (PV-RCNN) to perform road user detection (e.g., vehicles and pedestrians). Subsequently, we implement an Unscented Kalman filter, which takes a 3D detected object as input and uses its information to predict the state of the 3D box before the next LiDAR scan gets loaded. Finally, we update the measurement by using the new observation of the point cloud and correct the previous prediction's belief. The simulation results illustrate the performance gain (around 8 %) achieved by our solution compared to other 3D point cloud solutions.

Beamforming optimization can enhance the next-generation wireless networks. However, finding the optimal beamforming in real-time is hard due to the need for large beam training overhead. The problem can be more challenging in dynamic environments with small coherence channel time. In this paper, we propose an accurate beam prediction solution using light detection and ranging (LiDAR)-assisted radio frequency (RF) system. More specifically, we propose a deep-learning model based on long-sort term memory (LSTM) to predict future beam indices from a set of pre-defined beam steering codebook. In addition to solving the beamforming overhead problem, the proposed deep learning approach is a model-free approach that can be applied with no required knowledge of the channel state information. We compare the proposed model with the cross-validation results in addition to other benchmarks using the top-k accuracy metric. The numerical results show that the proposed scheme has achieved a top-1 accuracy of 84.6% compared to an 80.3% for cross-validation while the other benchmarks have achieved a 57.5% using the same data set.

This paper proposes a novel portable prototype and self-contained Air Quality (AQ) monitoring device that utilizes Light Detection and Ranging (LiDAR) technology to take its measurements. The novel device aims to improve mining safety by collecting and analyzing the AQ inside mines and displaying the real-time conditions to personnel. The intent is to create a 3D map of the environment and display potentially hazardous Atmospheric Particulate Matter (APM). To achieve this goal, we prototype a portable, compact, and easy-to-operate system that utilizes LiDAR to detect APM. Then, we propose how the collected data can be used to calculate real-time AQ conditions. Finally, we illustrate selected results to show the importance and feasibility of our novel prototype.

Reconfigurable intelligent surfaces (RISs) are expected to play a significant role in the next generation of wireless cellular technology. This paper proposes an uplink localization scheme using a single-snapshot solution for user equipment (UE) that is located in the near-field of the RIS. We propose utilizing the atomic norm minimization method to achieve super-resolution localization accuracy. We formulate an optimization problem to estimate the UE location parameters (i.e., angles and distances) by minimizing the atomic norm. Then, we propose to exploit strong duality to solve the atomic norm problem using the dual problem and semidefinite programming (SDP). The RIS is controlled and designed using estimated parameters to enhance the beamforming capabilities. Finally, we compare the localization performance of the proposed atomic norm minimization with compressed sensing (CS) in terms of the localization error. The numerical results show a superior performance of the proposed atomic norm method over the CS where a sub-cm level of accuracy can be achieved under some of the system configuration conditions using the proposed atomic norm method.

Every year, wildfires burn out countless hectares of lands, resulting in ecological, environmental, and economic damage. This paper presents an energy management system that consists of an unmanned aerial vehicle (UAV) equipped with air quality and light detection and ranging (LiDAR) sensors for monitoring forests and recognizing flames early. We develop a novel approach for autonomous patrolling system. This approach has the advantage of effectively detecting wildfire incidents, while optimizing the energy consumption of the UAV’s battery to cover large areas. When a wildfire is detected, the UAV is able to transmit real-time data, such as sensor readings and LiDAR data, to the nearby communication tower. We formulate an optimization problem that minimizes the overall UAV’s energy consumption due to patrolling. Based on the pollutant dispersion mode, we propose a novel UAV patrolling solution based on genetic algorithm with the goal of maximizing the patrolling coverage of the UAV taking into account the UAV’s battery constraints. More specifically, we optimize the UAV’s flight path using a plume dispersion model to find the concentration of common gases of wildfire. Finally, simulations are presented to show the efficiency and validity of the solution.

Visible Light Communication (VLC) is a rapidly growing technology which can supplement the current radio-frequency (RF) based wireless communication systems. VLC can play a huge part in solving the ever-increasing problem of spectrum scarcity because of the growing availability of Light Emitting Diodes (LEDs). One of the biggest advantages of VLC over other communication systems is that it can provide illumination and data communication simultaneously without needing any extra deployment. Although it is essential to provide data rate at a blazing speed to all the users nowadays, maintaining a satisfactory level in the distribution of lighting is also important. In this paper, we present a novel approach of using mirrors to enhance the illumination uniformity and throughput of an indoor multi-element VLC system architecture. In this approach, we improve the Signal-to-Interference plus Noise Ratio (SINR) of the system and overall illumination uniformity of the room by redirecting the reflected LED beams on the walls to darker spots with the use of mirrors. We formulate a joint optimization problem focusing on maximization of the SINR while maintaining a reasonable illumination uniformity across the room. We propose a two-stage solution of the optimization problem: design solution and communication solution. In the design optimization, we formulate an equivalent binary linear optimization to achieve the best illumination quality by optimizing the mirror placements and the LEDs’ transmit powers. In the communication problem, however, we aim to improve the throughput of the system using a fair utility metric based on maximizing the minimum user’s data rate. Due to non-convexity of the communication problem, we propose three different heuristic solutions and analyze their performance. We also show that about threefold increase in average illumination and fourfold increase in average throughput can be achieved when the mirror placement is applied which is a significant performance improvement.

Recently, there has been a flurry of research on the use of Reconfigurable Intelligent Surfaces (RIS) in wireless networks to create dynamic radio environments. In this paper, we investigate the use of an RIS panel to improve bi-directional communications. Assuming that the RIS will be located on the facade of a building, we propose to connect it to a solar panel that harvests energy to be used to power the RIS panel’s smart controller and reflecting elements. Therefore, we present a novel framework to optimally decide the transmit power of each user and the number of elements that will be used to reflect the signal of any two communicating pair in the system (user-user or base station-user). An optimization problem is formulated to jointly minimize a scalarized function of the energy of the communicating pair and the RIS panel and to find the optimal number of reflecting elements used by each user. Although the formulated problem is a mixed-integer nonlinear problem, the optimal solution is found by linearizing the non-linear constraints. Besides, a more efficient close to the optimal solution is found using Bender decomposition. Simulation results show that the proposed model is capable of delivering the minimum rate of each user even if line-of-sight communication is not achievable.

In this paper, we propose a novel optimization model for multiple Unmanned Aerial Vehicles (UAVs) working simultaneously as relays to help two set of ground users; namely relay users and device-to-device (D2D) users. The relay users are assumed out communication rages from each other and use the UAV link to for their data transmission. While the D2D users are assumed to be nearby users and use the UAV for managing the resources without being involved in the transmission. The goal of the paper is to operate the UAVs in an energy-efficient manner to support the different set of users by i) optimizing the available bandwidth and power allocations of the D2D links, and ii) acting as relays when needed to maintain the communication links between relay users. We formulate an optimization problem that maximizes the throughput-energy utility while respecting the resource availability including the UAVs’ energy consumption, UAV-user association, and trajectory constraints. Due to the non-convexity of the problem, we propose to solve it in three steps using Taylor series approximation. Firstly, we optimize the transmit power of the UAVs and users. Then, we optimize the bandwidth allocation for a given transmit power values. Finally, an efficient heuristic algorithm based on a recursive shrink-and-realign process is proposed to optimize the UAVs’ trajectories. The performance of the proposed method shows advantages in terms of average throughput compared to the fixed power and bandwidth solutions.

Recently, elevated LiDAR (ELiD) has been proposed as an alternative to local LiDAR sensors in autonomous vehicles (AV) because of the ability to reduce costs and computational requirements of AVs, reduce the number of overlapping sensors mapping an area, and to allow for a multiplicity of LiDAR sensing applications with the same shared LiDAR map data. Since ELiDs have been removed from the vehicle, their data must be processed externally in the cloud or on the edge, necessitating an optimized backhaul system that allocates data efficiently to compute servers. In this paper, we address this need for an optimized backhaul system by formulating a mixed-integer programming problem that minimizes the average latency of the uplink and downlink hop-by-hop transmission plus computation time for each ELiD while considering different bandwidth allocation schemes. We show that our model is capable of allocating resources for differing topologies, and we perform a sensitivity analysis that demonstrates the robustness of our problem formulation under different circumstances.

This paper proposes an energy management framework for cellular heterogeneous networks (HetNets) supported by dynamic solar powered drones. A HetNet composed of a macrocell base station (BS), micro cell BSs, and drone small cell BSs are deployed to serve the networks' subscribers. The drones can land at pre-planned locations defined by the mobile operator and at the macrocell BS site where they can charge their batteries. The objective of the framework is to jointly determine the optimal trips of the drones and the MBSs that can be safely turned off in order to minimize the total energy consumption of the network. This is done while considering the cells' capacities and the minimum receiving power guaranteeing successful communications. To do so, an integer linear programming problem is formulated and optimally solved for three cases based on the knowledge level about future renewable energy statistics of the drones. A low complex relaxed solution is also developed. Its performances are shown to be close to those of the optimal solutions. However, the gap increases as the network becomes more congested. Numerical results investigate the performance of the proposed drone-based approach and show notable improvements in terms of energy saving and network capacity.

In this paper, we propose a novel wireless scheme that integrates satellite, airborne, and terrestrial networks aiming to support ground users. More specifically, we study the enhancement of the achievable users' throughput assisted with terrestrial base stations, high-altitude platforms (HAPs), and satellite stations. The goal is to optimize the resource allocations and the HAPs' locations in order to maximize the users' throughput. In this context, we formulate and solve an optimization problem in two stages: a short-term stage and a long-term stage. In the short-term stage, we start by proposing an approximated solution and a low complexity solution to solve the associations and power allocations. In the approximated solution, we formulate and solve a binary linear optimization problem to find the best associations and then we use the Taylor expansion approximation to optimally determine the power allocations. In the latter solution, we propose a low complexity approach based on a frequency partitioning technique to solve the associations and power allocations. On the other hand, in the long-term stage, we optimize the locations of the HAPs by proposing an efficient algorithm based on a recursive shrink-and-realign process. Finally, selected numerical results underline the advantages provided by our proposed optimization scheme.

In the modern day, there is a serious spectrum crunch in the legacy radio frequency (RF) band, for which visible light communication (VLC) can be a promising option. VLC is a short-range wireless communication variant which uses the visible light spectrum. In this paper, we are using a VLC-based architecture for providing scalable communications to Internet-of-Things (IoT) devices where a multi-element hemispherical bulb is used that can transmit data streams from multiple light emitting diode (LED) boards. The essence of this architecture is that it uses a Line-of-Sight (LoS) alignment protocol that handles the handoff issue created by the movement of receivers inside a room. We start by proposing an optimization problem aiming to minimize the total consumed energy emitted by each LED taking into consideration the LEDs’ power budget, users’ perceived quality-of-service, LED-user associations, and illumination uniformity constraints. Then, because of the non-convexity of the problem, we propose to solve it in two stages: (1) We design an efficient algorithm for LED-user association for fixed LED powers, and (2) using the LED-user association, we find an approximate solution based on Taylor series to optimize the LEDs’ power. We devise a heuristic solution based on this approach. Finally, we illustrate the performance of our method via simulations.

The use of Unmanned Aerial Vehicles (UAVs) has gained interest in wireless networks for its many uses and advantages such as rapid deployment and multi-purpose functionality. This is why wide deployment of UAVs has the potential to be integrated in the upcoming 5G standard. They can be used as flying base-stations, which can be deployed in case of ground Base-Stations (GBSs) failures. Such failures can be short-term or longterm. Based on the type and duration of the failure, we propose a framework that uses drones or helikites to mitigate GBS failures. Our proposed short-term and long-term cell outage compensation framework aims to mitigate the effect of the failure of any GBS in 5G networks. Within our framework, outage compensation is done with the assistance of sky BSs (UAVs), An optimization problem is formulated to jointly minimize communication power of the UAVs and maximize the minimum rates of the Users' Equipment (UEs) affected by the failure. Also, the optimal placement of the UAVs is determined. Simulation results show that the proposed framework guarantees the minimum quality of service for each UE in addition to minimizina the UAVs' consumed energy.

This paper investigates a multi-band harvesting (EH) schemes under cognitive radio interweave framework. All secondary users are considered as EH nodes that are allowed to harvest energy from multiple bands of Radio Frequency (RF) sources. A win-win framework is proposed, where SUs can sense the spectrum to determine whether the spectrum is busy, and hence they may harvest from RF energy, or if it is idle, and hence they can use it for transmission. Only a subset of the SUs can sense in order to reduce sensing energy, and then machine learning is used to characterize areas of harvesting and spectrum usage. We formulate an optimization problem that jointly optimize number of sensing samples and sensing threshold in order to minimize the sensing time and hence maximize the amount of energy harvested. A near optimal solution is proposed using Geometric Programming (GP) to optimally solve the problem in a time-slotted period. Finally, an energy efficient approach based on multi-class Support Vector Machine (SVM) is proposed by involving only training SUs instead of all SUs.

In this paper, we investigate the problem of optimal planning and deployment of multiple relays to support energy-efficient uplink transmissions of Internet of Things (IoT) devices. A novel approach is proposed to optimize the relay locations with the objective of minimizing the total energy consumption of the network. In addition, the uplink transmit power of IoT devices and the device-relay-channel association are jointly optimized to meet the QoS requirement of IoT devices. A mixed-integer linear programming (MILP) problem is formulated to obtain the optimal solution. We also design a low-complexity genetic algorithm to provide a sub-optimal solution to the problem.

Unmanned Aerial Vehicles (UAVs) enabled communications is a novel and attractive area of research in cellular communications. It provides several degrees of freedom in time, space and it can be used for multiple purposes. This is why wide deployment of UAVs has the potential to be integrated in the upcoming 5G standard. In this paper, we present a novel cell outage compensation (COC) framework to mitigate the effect of the failure of any outdoor Base Station (BS) in 5G networks. Within our framework, the outage compensation is done with the assistance of sky BSs (UAVs) and Ground BSs (GBSs). An optimization problem is formulated to jointly minimize the energy of the Drone BSs (DBSs) and GBSs involved in the healing process which accordingly will minimize the number of DBSs and determine their optimal 2D positions. In addition, the DBSs will mainly heal the users that the GBS cannot heal due to capacity issues. Simulation results show that the proposed hybrid approach outperforms the conventional COC approach. Moreover, all users receive the minimum quality of service in addition to minimizing the UAVs' consumed energy.

In this paper, we investigate energy efficient and energy harvesting (EH) in heterogeneous networks (HetNets) where all base stations (BSs) are equipped to harvest energy from renewable energy sources, e.g., solar. We consider a hybrid power supply of green (renewable) and traditional micro-grid, such that traditional micro-grid is not exploited as long as the BSs can meet their power demands from harvested and stored green energy. Therefore, our goal is to minimize the network-wide energy consumption subject to users' certain quality of service and BSs' power consumption constraints. As a result of binary BS sleeping status and user-cell association variables, proposed is formulated as a binary linear programming (BLP) problem. Two cases based on the knowledge level about future renewable energy (RE) statistics are investigated: (i) an online knowledge case where future RE statistics are unknown, (ii) an offline knowledge case where future network's statistics are a priori perfectly estimated. A green communication algorithm based on binary particle swarm optimization is implemented to solve the problem with low complexity time.

This paper proposes an energy management framework for cellular heterogeneous networks (HetNets) supported by dynamic drone small cells. A 3-tier HetNet is considered where macrocell, on#x002F;off switching micro cells, and solar-powered drone small cells are deployed to serve the networks' subscribers. In addition to energy harvesting, the drones can power their batteries via a charging station located at the macrocell site. Pre-planned locations are identified by the mobile operator for possible drones' placement. The objective of the framework is to optimally determine the positioning of the drones in addition to the micro cells status that can be turned off in order to minimize the daily energy consumption of the network. The framework takes also into account the cells' capacity and quality of service (QoS) metric defined by the minimum received power. An integer linear programming problem is formulated to optimally determine the network status during a time blocked period. The performance of this online scheme shows important advantages in terms of energy efficiency and connectivity compared to the traditional case without drones specially when the network is congested.

This paper proposes an optimized transmission scheme for Energy Harvesting (EH)-based two-way multiple-relay systems. All relays are considered as EH nodes that harvest energy from renewable and radio frequency (RF) sources, then use it to forward the information to the destinations. The power-splitting (PS) protocol, by which the EH node splits the input RF signal into two components for information transmission and energy harvesting, is adopted in the relay side. The objective is to optimize the PS ratios and the relays' transmit power levels in order to maximize the total sum-rate utility over multiple coherent time slots. An optimization approach based on geometric programming is proposed to solve the problem. Numerical results illustrate the behavior of the EH-based two-way multiple-relay system with respect to various parameters and compare the performance of the proposed approach with that of the dual problem-based approach.

In this paper, a multiple relay selection scheme for Energy Harvesting (EH)-based two-way relaying is investigated. All the relays are considered as EH nodes that harvest energy from renewable and radio frequency sources, then use it to forward the information to the sources. The time-switching protocol (TS), in which the receiver switches between transmitted information and harvested energy, is adopted in the relay side. The goal is to find the optimal TS ratios associated with the selected relays that maximize a rate-based utility function over multiple coherent time slots. Two metrics reflecting the degrees of fairness in the optimization are investigated. A joint-optimization solution based on binary particle swarm optimization is proposed to solve the problem. Numerical results illustrate the behavior of the TWR network according to the considered utility functions, the generated amount renewable energy, in addition to other system parameters.

In this paper, we investigate the Energy Harvesting (EH)-based two-way relaying system using Amplify-and-Forward (AF) and Decode-and-Forward (DF) strategies. The relay is considered as an EH node that harvests the received Radio Frequency (RF) signal and uses this harvested energy to forward the information. Two relaying protocols based on Time Switching (TS) and Power Splitting (PS) receiver architectures are proposed to enable EH and information processing at the relay. Analytical throughput expressions are derived and optimized for both protocols. The goal is to find the optimal TS and PS ratios that maximize the total throughput Numerical results illustrate the performance of TS and PS protocols for different strategies, and show that at high signal-to-noise ratio, PS is superior to TS, and AF is superior to DF in terms of achievable sum-rate.

In this paper, the problem of radio and power resource management in underlay cognitive radio heterogeneous networks is investigated, where macro and pico Base Stations (BSs) are considered as primary BSs while femto BSs are considered as secondary BSs. The goal is to minimize the total primary power consumption and maximize the secondary utility of the network while satisfying the primary user quality of service determined by target data rate and interference constraints. Furthermore, a green communication algorithm is implemented based on a sleeping strategy. Simulations study investigates the performance of the proposed scheme and shows an important saving in terms of total power consumption.

Self-healing mechanism is one of the three functionalities for self-organizing networks, and it has three major components to be studied by the academic society: fault detection, fault diagnosis and cell outage compensation. In this paper, we study the cell outage compensation function of the self-healing mechanism. In a heterogeneous network environment with densely deployed Femto Base Stations (FBSs), we form a resource allocation problem for FBSs and Femto User Equipments (FUEs) operations using Coordinated Multi-Point (CoMP) transmission and reception with joint processing technique. Since the formulated problem is considered as NP hard problem, we propose a heuristic operation scheme to solve the problem. Simulation results show that our proposed operation scheme can improve FUE throughput by up to 30% compared to other solutions, and it can also prevent the system total rate loss from having the same speed of radio resource loss when failures happen.

In this paper, the problem of radio and power resource management in long term evolution heterogeneous networks (LTE HetNets) is investigated. The goal is to minimize the total power consumption of the network while satisfying the user quality of service determined by each target data rate. We study the model where one macrocell base station is placed in the cell center, and multiple small cell base stations and femtocell access points are distributed around it. The dual decomposition technique is adopted to jointly optimize the power and carrier allocation in the downlink direction in addition to the selection of turned off small cell base stations. Our numerical results investigate the performance of the proposed scheme versus different system parameters and show an important saving in terms of total power consumption.

In this paper, the problem of both bandwidth and power allocation for two-way multiple relay systems in overlay cognitive radio (CR) setup is investigated. In the CR overlay mode, primary users (PUs) cooperate with cognitive users (CUs) for mutual benefits. In our framework, we propose that the CUs are allowed to allocate a part of the PUs spectrum to perform their cognitive transmission. In return, acting as an amplify-and-forward two-way relays, they are used to support PUs to achieve their target data rates over the remaining bandwidth. More specifically, CUs acts as relays for the PUs and gain some spectrum as long as they respect a specific power budget and primary quality-of-service constraints. In this context, we first derive closed-form expressions for optimal transmit power allocated to PUs and CUs in order to maximize the cognitive objective. Then, we employ a strong optimization tool based on particle swarm optimization algorithm to find the optimal relay amplification gains and optimal cognitive released bandwidths as well. Our numerical results illustrate the performance of our proposed algorithm for different utility metrics and analyze the impact of some system parameters on the achieved performance.

In this paper, the problem of power allocation for a multiple-input multiple-output two-way system is investigated in underlay Cognitive Radio (CR) set-up. In the CR underlay mode, secondary users are allowed to exploit the spectrum allocated to primary users in an opportunistic manner by respecting a tolerated temperature limit. The secondary networks employ an amplify-and-forward two-way relaying technique in order to maximize the sum rate under power budget and interference constraints. In this context, we formulate an optimization problem that is solved in two steps. First, we derive a closed-form expression of the optimal power allocated to terminals. Then, we employ a strong optimization tool based on particle swarm optimization algorithm to find the power allocated to secondary relays. Simulation results demonstrate the efficiency of the proposed solution and analyze the impact of some system parameters on the achieved performance.

This paper studies the energy efficient transmission and the power allocation problem for multiple two-way relay networks equipped with multi-input multi-output antennas where each relay employs an amplify-and-forward strategy. The goal is to minimize the total power consumption without degrading the quality of service of the terminals. In our analysis, we start by deriving closed-form expressions of the optimal powers allocated to terminals. We then employ a strong optimization tool based on the particle swarm optimization technique to find the optimal power allocated at each relay antenna. Our numerical results illustrate the performance of the proposed scheme and show that it achieves a sub-optimal solution very close to the optimal one.

High-speed railway system equipped with moving relay stations placed on the middle of the ceiling of each train wagon is investigated. The users inside the train are served in two hops via the 3GPP Long Term Evolution (LTE) technology. The objective of this work is to maximize the number of served users by respecting a specific quality-of-service constraint while minimizing the total power consumption of the eNodeB and the moving relays. We propose an efficient algorithm based on the Hungarian method to find the optimal resource allocation over the LTE resource blocks in order to serve the maximum number of users with the minimum power consumption. Moreover, we derive a closed-form expression for the power allocation problem. Our simulation results illustrate the performance of the proposed scheme and compare it with various previously developed algorithms as well as with the direct transmission scenario.

In this paper, we investigate a multiple relay selection scheme for two-way relaying cognitive radio networks where primary users and secondary users operate on the same frequency band. More specifically, cooperative relays using Amplify-and- Forward (AF) protocol are optimally selected to maximize the sum rate of the secondary users without degrading the Quality of Service (QoS) of the primary users by respecting a tolerated interference threshold. A strong optimization tool based on genetic algorithm is employed to solve our formulated optimization problem where discrete relay power levels are considered. Our simulation results show that the practical heuristic approach achieves almost the same performance of the optimal multiple relay selection scheme either with discrete or continuous power distributions.