Molecular communication (MC), which utilizes molecules to transmit data via diffusion channels, is a prominent system in nanonetworks. In particular, Data-collection (DC) scenarios are a challenging area of research that remains open for further investigation. In this letter, we focus on optimizing the energy efficiency (EE) of a molecular DC nanonetwork comprising a mobile nanorobot (NR) and energy-constrained nanosensors (NSs), taking into account the constraints on the molecular concentration, data rate, and available molecular resources. The defined optimization problem is a nonlinear fractional program that is difficult to solve. To determine the optimal solution, we use Dinkelbach's approach and Lagrangian analysis. The simulation results demonstrate the promising performance of the proposed framework.

Unmanned aerial vehicles (UAVs) are expected to play an important role in future wireless networks, serving as communication relays, computing servers, and flying infrastructure for ground users when ground-based infrastructure is congested or inaccessible. However, typical UAVs are powered by on-board batteries, which results in limited battery lifetime and poses a major restriction for UAV applications in communications. To overcome this, we propose a consistent and cost-aware energy procurement framework for a UAV powered concurrently by laser beams, emitted from locally deployed laser beam directors, and local renewable energy (RE) sources. The UAV intends to lower its overall energy cost for a certain operation cycle by optimizing the quantities of energy obtained from its battery as well as laser beams at each time period. Given the optimization results, we also propose a cost-aware UAV placement strategy with the ultimate goal of ensuring quality communication-energy links for the UAV, ground devices (GDs) and users. In addition, we assess the amount of additional procured RE that can be transferred via wireless power transfer to charge a set of distributed GDs. The simulations provide interesting insights into the efficiency of the proposed framework.

Forests have an important role in environmental preservation and maintenance. The primary threat is forest fires, which have disastrous repercussions. As a result, it is critical to identify and extinguish a fire before it spreads and destroys resources. To that end, we propose a forest fire detection and fighting mechanism using wireless sensor and actor networks (WSANs). Temperature sensors are utilized to detect fires, and actors (robots) are employed to extinguish them. Sensors and robots are distributed at random throughout the forest, forming clusters. Clustering, sleep/active scheduling for the sensors, and energy harvesting (EH)/moving modes for the robots, are used to extend and maximize the sensors/robots lifetime in the WSAN. In such a network, robots should move to the fire site as quickly as possible. To do this, we further propose a robot routing mechanism that focuses on determining the shortest path for each firefighting robot. In particular, each firefighting robot equipped with on-board processing uses a fuzzy$Q$-learning (FQL)-based trajectory mechanism to learn the shortest path to the fire zone in the least amount of time. Simulations are conducted to demonstrate the benefits of employing the proposed framework for rapid and effective fire response. When compared to the traditional$Q$-learning, the total approaching rate (a measure of how quickly the firefighting robots can reach the fire) to the fire spot is greater when utilizing the proposed FQL-based strategy.

This article discusses a cellular network assisted by an energy- and spectral-efficient unmanned aerial vehicle (UAV), in which the UAV is deployed to serve mobile users in the cellular network and enable mobile data offloading from a ground base station (GBS) by taking a circular flight route. We explore a visible light communication (VLC)-enabled UAV, in which a light-emitting diode (LED) is mounted on a rotary-wing UAV to offer communications to the users. Our aim is to simultaneously optimize both energy efficiency (EE) and spectral efficiency (SE) of the VLC-enabled UAV by jointly optimizing the common throughput of all users as well as the UAV’s trajectory and flying speed. We employ a unified metric, called resource efficiency (RE), and explore the RE optimization to obtain an adaptive EE–SE tradeoff. The problem posed is seen in a complex and nonconvex shape, making it hard to solve. Motivated by the enormous achievement of deep reinforcement learning (DRL) in solving complex control problems, we propose a DRL-based approach to handle this nonconvex and complicated optimization. The findings of the simulation reveal that the developed framework achieves a substantial performance in terms of the solution convergence as well as the promising quality of the solutions.

Cognitive Radio (CR) technique has been proposed for improving spectrum efficiency by dynamic spectrum access. In Cognitive Radio Networks (CRNs), unlicensed Secondary Users (SUs) with CR can utilize licensed spectrum without interfering licensed Primary Users (PUs). For effectively avoiding interference with licensed PUs and malicious attacks from jammers, a two-stage Learning-based Optimal Channel Selection (LOCS) algorithm for unlicensed SUs in distributed heterogeneous CRNs is proposed in this paper. The LOCS algorithm enables SUs to obtain real states of the licensed channels without knowing their information. Hence, SUs using LOCS algorithm can efficiently avoid collision and attack with PUs and jammers. Besides, the LOCS algorithm considers hardware limitation of the SUs, i.e., SUs can only sense and access parts of the license spectrum during any given time. SUs can select the optimal channels for spectrum sensing and data transmission by using the LOCS algorithm. Simulation results show the efficiency of our proposed algorithm in terms of collision and attack avoidance.

Interference alignment (IA) is a technique that can suppress interference with a small number of antennas by aligning interference signals using transmit weights. These weights are designed based on the channel state information (CSI) fed back from each receiver, however, under the timevarying channel, the estimated CSI can be delayed/outdated, which will result in an imperfect IA. Therefore, IA with channel prediction has attracted much attention. The auto regressive (AR) model is known as a prediction method that predicts a future state based on only the past states. In the conventional channel prediction based IA method, the past channels are used directly for prediction. Therefore, the number of calculations for prediction can be too large. In this paper, based on the AR model, we describe a low complexity and high accuracy channel prediction method for IA. To predict the future channel, we only use the differences of channels between adjacent times instead of using the past channels directly. This will lead to a very low channel prediction error. Simulations show that the proposed method improves prediction accuracy and requires less calculation than the conventional one. Moreover, the IA with the proposed channel prediction method will achieve a higher transmission rate.

Superposed multicarrier transmission is a known method to improve frequency utilization efficiency when several wireless systems share the same spectrum. Obviously, an enhanced spectral efficiency comes at the expense of interference. To suppress the effect of interference, forward error correction (FEC) metric masking can be applied. In FEC, the corresponding log-likelihood (LLR) of the superposed band is set to zero or to other proper values determined by the other parameters such as the desired to undesired power ratio (DUR). To be able to apply the FEC metric masking, the information on the superposed band sub-carriers is required at the receiver side. Therefore, in this paper, we propose a novel method for detecting the superposed bands of multicarrier transmissions using Q-learning. We present the simulation results that show a higher rate of superposed band detection accuracy in lower DUR over the conventional method, as well as similar accuracy over other DUR.

Increased environmental awareness coupled with the rising cost of energy have sparked a keen interest in the deployment of energy-efficient communication technologies over the infrastructure of cellular networks. Base stations (BSs) are responsible for the largest portion of power consumption and energy usage in cellular networks. Thus, sleep/wake-up scheduling strategies for BSs can significantly improve energy-efficiency (EE) of cellular networks. In this paper, we propose a Fuzzy Q-Learning (FQL) based energy-efficient sleep/wake-up mechanism for BSs in a heterogeneous network (HetNet). The goal is to save energy, without compromising the offered Quality of Service (QoS), by switching off the redundant BSs according to the local traffic profile and depending on the required area coverage and cell EE. The introduction of sleep mode for BSs may lead to a large-scale coverage loss, unless a specific remedial solution is exploited at the same time. To this end, we also propose to use device-to-device (D2D) communications to extend network coverage to the service areas of the switched-off BSs. Simulation results validate that the proposed framework provides significant improvements in power consumption and the EE.

We present centralized iterative algorithms that jointly determine the optimal transmit and receive filters as well as the optimal power allocation for a K-user multiple-input multiple-output (MIMO) interference channel (IC). The optimality criterion is based on the achievable sum-rate and the average per user multiplexing gain in the MIMO IC. By allowing channel state information (CSI) exchanged between base stations (BSs) and a central unit (CU), we design a feedback topology where CU collects local CSIs from all BSs, computes all transmit and receive filters and sends them to corresponding user-BS pairs. Note that the local CSIs at BSs are obtained from the estimation of the channel states during the so- called uplink-training phase. At the CU, we propose iterative algorithms utilizing alternating optimization strategy to design the filters. In most of the studies on the MIMO IC, choice of equal transmit powers for all user-BS pairs ignores the essential need to search for the optimal power allocation policy; they do not take the full advantage of the system's total power. Thus, how to allocate power among all the user-BS pairs in the network based on the sum-rate maximization strategy and under a sum power constraint is another key to this paper.

We present centralized iterative algorithms that jointly determine the optimal transmit and receive filters as well as the optimal power allocation for a K-user multiple-input multiple-output (MIMO) interference channel (IC). The optimality criterion is based on the achievable sum-rate and the average per user multiplexing gain in the MIMO IC. By allowing channel state information (CSI) exchanged between users and a central unit (CU), we design a feedback topology where the CU collects local CSIs from all base stations (BSs), computes all transmit and receive filters and sends them to corresponding user-BS pairs. At the CU, we propose iterative algorithms utilizing alternating optimization strategy to design the filters. In most of the studies on the MIMO IC, choice of equal transmit powers for all user-BS pairs ignores the essential need to search for the optimal power allocation policy; they do not take the full advantage of the system's total power. How to allocate power among all the user-BS pairs in the network based on the sum-rate maximization strategy is the key to this paper. Thus, while the filters are designed at the CU, we propose a novel power allocation problem for sum-rate maximization under sum power constraint.

Interference alignment (IA) is a technique shown to be able to achieve a significant overall throughput in a μ — user multiple-input multiple-output (MIMO) interference channel (IC). In this paper, we try to modify the conventional IA designs to achieve enhanced sum-rate performance. We jointly design transmit and receive IA filters (precoding and suppression filters) at a central unit (CU) to reduce the channel state information (CSI) feedback/sharing overhead. At the CU, in a one-way iterative strategy (left ‘L’ to right ‘R’, see Fig. 1), the precoding filters are optimized (on side ‘L’) in the direction of the gradient of the sum-rate, and the suppression filters are chosen (on side ‘R’) to minimize the leakage interferences. Then, in a two-way iterative strategy, the proposed scheme alternates between ‘L’ and ‘R’ sides to design the IA filters through the joint sum-rate maximization and interference leakage minimization on each side. Finally, a modified version of the two-way iterative strategy is proposed to design the IA filters on the basis of signal-to-interference-plus-noise ratio (SINR) (instead of the interference minimization) and the sum-rate maximization techniques. Comparing to other conventional IA designs, the proposed designs with provable convergence show a significant sum-rate performance improvement and often outperform other widely used algorithms, as shown in the simulation examples.

In this paper, we present a cognitive radio (CR) based statistical framework for a two-tier (femto- macro) heterogeneous cellular network. In this framework, the coverage probability of an arbitrary femto user is determined. Using tools from stochastic geometry and point process theory (in this paper, the spatial Poisson point process (PPP) theory is used) we model the random locations and topology of both the femto and macro networks. A considerable improvement of system performance can be generally achieved by mitigating interference, as a result of applying the CR idea over the above model. We also study the implication of a Reinforcement Learning (RL) based power control (PC) strategy per femto user in interference-limited networks over the above model to guarantee a certain value of coverage probability for a given signal-to-interference- plus-noise-ratio (SINR) target.

In this paper, we present a cognitive radio (CR) based statistical framework for a two-tier heterogeneous cellular network (femto-macro network) to model the outage probability at any arbitrary secondary (femto) and primary (macro) user. A system model based on stochastic geometry (utilizing the spatial Poisson point process (PPP) theory) is applied to model the random locations and topology of both secondary and primary networks. A considerable performance improvement can be generally achieved by mitigating interference, in result of applying the CR idea over the above model. Novel closed form expressions are derived for the outage probability of any typical femto and macro user considering the Nakagami-m fading for each desired and interference links. We also study the effect of some important design factors which play vital roles and are usually ignored in determination of outage and interference. We conduct simulations to evaluate the performance of our proposed schemes in terms of outage probability for different values of signal-to-interference-plus-noise-ratio (SINR) target.

In this paper, we present a Cognitive Radio (CR) based statistical framework for a two-tier heterogeneous cellular network (macro-femto network) to model the outage probability at any arbitrary secondary user (femto user) and primary user (macro user). A system model based on stochastic geometry (utilizing the theory of a Poisson point process (PPP)) is introduced to model the random locations and topology of both primary and secondary networks (macro-femto networks). We provide an overview of how CR idea facilitates interference mitigation in two-tier heterogeneous networks in the presented model. We also study the effect of several important design factors which play vital roles and are usually ignored in determination of outage and interference. We conduct simulations to evaluate the performance of our proposed schemes in terms of outage probability for different values of signal-to-interference-plus-noise-ratio (SINR) target.

In a cognitive radio (CR) network, the channel sensing scheme to detect the appearance of a primary user (PU) directly affects the performances of both CR and PU. However, in practical systems, the CR is prone to sensing errors due to inefficient sensing scheme. This may lead to interfering with primary user and low system performance. In this paper, we present a learning based scheme for channel sensing in CR network. Specifically, we formulate the channel sensing problem as a partially observable Markov decision process (POMDP), where the most likely channel state is derived by a learning process called Fuzzy Q-Learning (FQL). The optimal policy is derived by solving the problem. The simulation results show the effectiveness and efficiency of our proposed scheme.