In quasi-static wireless networks characterized by infrequent changes in the transmission schedules of user equipment (UE), malicious jammers can easily deteriorate network performance. Accordingly, a key challenge in these networks is managing channel access amidst jammers and under dynamic channel conditions. In this context, we propose a robust learning-based mechanism for channel access in multi-cell quasi-static networks under jamming. The network comprises multiple legitimate UEs, including predefined UEs (pUEs) with stochastic predefined schedules and an intelligent UE (iUE) with an undefined transmission schedule, all transmitting over a shared, time-varying uplink channel. Jammers transmit unwanted packets to disturb the pUEs’ and the iUE’s communication. The iUE’s learning process is based on the deep reinforcement learning (DRL) framework, utilizing a residual network (ResNet)-based deep Q-Network (DQN). To coexist in the network and maximize the network’s sum cross-layer achievable rate (SCLAR), the iUE must learn the unknown network dynamics while concurrently adapting to dynamic channel conditions. Our simulation results reveal that, with properly defined state space, action space, and rewards in DRL, the iUE can effectively coexist in the network, maximizing channel utilization and the network’s SCLAR by judiciously selecting transmission time slots and thus avoiding collisions and jamming.

This paper investigates a deep reinforcement learning (DRL)-based approach for managing channel access in wireless networks. Specifically, we consider a scenario in which an intelligent user device (iUD) shares a time-varying uplink wireless channel with several fixed transmission schedule user devices (fUDs) and an unknown-schedule malicious jammer. The iUD aims to harmoniously coexist with the fUDs, avoid the jammer, and adaptively learn an optimal channel access strategy in the face of dynamic channel conditions, to maximize the network's sum cross-layer achievable rate (SCLAR). Through extensive simulations, we demonstrate that when we appropriately define the state space, action space, and rewards within the DRL frame-work, the iUD can effectively coexist with other UDs and optimize the network's SCLAR. We show that the proposed algorithm outperforms the tabular Q-learning and a fully connected deep neural network approach.

Traditional human-designed medium access control (MAC) protocols cannot tackle the heterogeneous requirements of the future 6G wireless networks. Reinforcement learning (RL) algorithms have been proposed, in which base stations (BSs) and user equipment's (UEs) act as agents to automatically learn the MAC protocols to satisfy the stringent quality of service (QoS) requirements of 6G networks. However, existing RL techniques result in a generalization issue where agents fail to identify and explore useful information in a sparse wireless environment. To tackle this challenge, we propose a cooperative multi-agent exploration (CMAE) framework in which the network state space is projected into a low-dimensional space instead of learning a policy in a high-dimensional space. Consequently, the agents start exploring from low-dimensional state space to high-dimensional space to learn the abstracted information from the wireless environment. In the proposed framework, the nodes and BSs collaborate to explore the under-explored wireless network states to jointly learn the channel access and signalling policy. Simulation results show that the proposed CMAE framework outperforms traditional baseline schemes in terms of good put and collision rate and has better generalization capabilities.

Tactical wireless sensor networks (T-WSNs) are used in critical data-gathering military operations, such as battlefield surveillance, combat monitoring, and intrusion detection. These networks have unique challenges, such as jamming attacks, which are not normally encountered in traditional WSNs. Jamming attacks on the networks’ links disrupt data communication and make packet routing in T-WSNs a difficult task. Consequently, T-WSN routing aims to find the most reliable routes, while meeting the stringent delay and energy requirements. To this end, we propose a distributed multi-agent deep reinforcement learning (MADRL)-based routing solution for multi-sink tactical mobile sensor networks to overcome link layer jamming attacks. Our proposed routing scheme captures the hop count to the nearest sink, the one-hop delay, the next hop’s packet loss rate (PLR), and the energy cost of packet forwarding in the action reward estimation. Furthermore, the proposed scheme outperforms benchmark algorithms in terms of the packet delivery ratio (PDR), packet delivery time, and energy efficiency.

Due to recent technological advancements in the area of unmanned aerial systems, equipping an unmanned aerial vehicle (UAV) with a base station (BS) has been proposed to augment terrestrial base stations and to enhance the performance of 5G and beyond-5G networks. In this paper, we investigate the 3D placement problem for multiple UAV-BSs that maximizes the number of covered users with the same as well as with different quality-of-service (QoS) requirements. First, we present a mathematical formulation of the multiple UAV-BS placement problem, and show that it is a non-convex optimization problem. Then, we propose two heuristic algorithms, and we show that for users with the same as well as with different QoS requirements, the proposed algorithms outperform the Linear Approximation (LA) state-of the-art algorithm in terms of the average number of covered users and execution time, and achieve near optimal performance. Finally, a tradeoff between the two heuristic algorithms in terms of the average number of covered users and their execution time is presented.

Wireless Sensor Networks (WSNs) are crucial in supporting continuous environmental monitoring, where sensor nodes are deployed and must remain operational to collect and transfer data from the environment to a base-station. However, sensor nodes have limited energy in their primary power storage unit, and this energy may be quickly drained if the sensor node remains operational over long periods of time. Therefore, the idea of harvesting ambient energy from the immediate surroundings of the deployed sensors, to recharge the batteries and to directly power the sensor nodes, has recently been proposed. The deployment of energy harvesting in environmental field systems eliminates the dependency of sensor nodes on battery power, drastically reducing the maintenance costs required to replace batteries. In this article, we review the state-of-the-art in energy-harvesting WSNs for environmental monitoring applications, including Animal Tracking, Air Quality Monitoring, Water Quality Monitoring, and Disaster Monitoring to improve the ecosystem and human life. In addition to presenting the technologies for harvesting energy from ambient sources and the protocols that can take advantage of the harvested energy, we present challenges that must be addressed to further advance energy-harvesting-based WSNs, along with some future work directions to address these challenges.

Cognitive radio is a promising technology that aims to enhance the utilization of the radio spectrum. This is achieved by providing opportunistic access to the unlicensed users or the secondary users. In this paper, quality of service (QoS) in cognitive radio networks is investigated for a heterogeneous network model. A modified cognitive radio spectrum sharing algorithm based on the Hungarian algorithm is proposed and compared to first come first served (FCFS) scheduling technique. The modified algorithm proposed a cost function based on the required and the available signal to noise ratio (SNR) of the application and channel, respectively. Simulation results reveal the robustness of the proposed algorithm under nonfading, Rayleigh and Nakagami fading channels in terms of achieved SNR, data rate and spectrum utilization compared to FCFS algorithm.