Showing papers on "Relay published in 2021"
TL;DR: The performance of cooperative simultaneous wireless information and power transfer (SWIPT) nonorthogonal multiple access (NOMA) for massive IoT systems is studied and hardware impairment parameter has a deleterious effect on system performance while the channel estimation parameter is always beneficial to the OP.
Abstract: Massive connectivity and limited energy are main challenges for the beyond 5G (B5G)-enabled massive Internet of Things (IoT) to maintain diversified Qualify of Service (QoS) of the huge number of IoT device users. Motivated by these challenges, this article studies the performance of cooperative simultaneous wireless information and power transfer (SWIPT) nonorthogonal multiple access (NOMA) for massive IoT systems. Under the practical assumption, residual hardware impairments (RHIs) and channel estimation errors (CEEs) are taken into account. The communication between the base station (BS) and two NOMA IoT device users is realized through a direct link and the assistance of multiple relays with finite energy storage capability that can harvest energy from the BS. Aiming at improving the system performance, an optimal relay is selected among $K$ relays by using the partial relay selection (PRS) protocol to forward the received signal to the two NOMA IoT device users, namely, the far user (FU) and near user (NU). To evaluate the system performance, exact analytical expressions for the outage probability (OP) are derived in closed form. In order to get a better understanding of the overall system performance, we further undertake diversity order analyses by deriving asymptotic expressions for the OP in the high signal-to-noise ratio (SNR) regime. In addition, we also investigate the energy efficiency (EE) of the considered system, which is a crucial performance metric in massive IoT systems so that the impact of key system parameters on the performance can be quantified. Finally, the optimal power allocation scheme to maximize the sum rate of the considered system in the high SNR regime is also designed. Numerical results have shown that: 1) hardware impairment parameter has a deleterious effect on system performance while the channel estimation parameter is always beneficial to the OP; 2) the expected performance improvements obtained by the user of PRS protocol are enhanced by increasing the number of relays; and 3) the proposed power allocation scheme can optimize the sum-rate performance of the considered system.
107 citations
TL;DR: In this paper, the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs) is posed as a joint optimization problem of transmit beamforming and RIS control, whose goal is to maximize the EE under minimum rate constraints of the users.
Abstract: This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs. This problem is posed as a joint optimization problem of transmit beamforming and RIS control, whose goal is to maximize the energy efficiency under minimum rate constraints of the users. To solve this problem, two iterative algorithms are proposed for the singleuser case and multi-user case. For the single-user case, the phase optimization problem is solved by using a successive convex approximation method, which admits a closed-form solution at each step. Moreover, the optimal RIS on-off status is obtained by using the dual method. For the multi-user case, a low-complexity greedy searching method is proposed to solve the RIS on-off optimization problem. Simulation results show that the proposed scheme achieves up to 33% and 68% gains in terms of the energy efficiency in both single-user and multi-user cases compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively.
65 citations
TL;DR: This paper proposes an online model-free Constrained Deep Reinforcement Learning (CDRL) algorithm based on Lagrangian primal-dual policy optimization to solve the CMDP, and learns a cooperative policy in which the altitude of UAVs and channel access probability of IoT devices are dynamically controlled to attain the maximal long-term network capacity.
Abstract: In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve the massive channel access of a wireless IoT network where solar-powered Unmanned Aerial Vehicles (UAVs) relay data from IoT devices to remote servers. Specifically, IoT devices contend for accessing the shared wireless channel using an adaptive $p$ -persistent slotted Aloha protocol; and the solar-powered UAVs adopt Successive Interference Cancellation (SIC) to decode multiple received data from IoT devices to improve access efficiency. To enable an energy-sustainable capacity-optimal network, we study the joint problem of dynamic multi-UAV altitude control and multi-cell wireless channel access management of IoT devices as a stochastic control problem with multiple energy constraints. We first formulate this problem as a Constrained Markov Decision Process (CMDP), and propose an online model-free Constrained Deep Reinforcement Learning (CDRL) algorithm based on Lagrangian primal-dual policy optimization to solve the CMDP. Extensive simulations demonstrate that our proposed algorithm learns a cooperative policy in which the altitude of UAVs and channel access probability of IoT devices are dynamically controlled to attain the maximal long-term network capacity while ensuring energy sustainability of UAVs, outperforming baseline schemes. The proposed CDRL agent can be trained on a small network, yet the learned policy can efficiently manage networks with a massive number of IoT devices and varying initial states, which can amortize the cost of training the CDRL agent.
60 citations
TL;DR: In this article, the authors proposed a novel hybrid communication network that utilizes both a full-duplex (FD) Decode-and-Forward (DF) relay and an Intelligent Reflecting Surface (IRS) to support data transmission over wireless channels.
Abstract: In this work, we propose a novel hybrid communication network that utilizes both a Full-Duplex (FD) Decode-and-Forward (DF) relay and an Intelligent Reflecting Surface (IRS) to support data transmission over wireless channels. We design the reflecting coefficients at the IRS to maximize the minimum achievable rate of the two hops for the proposed hybrid network. To that end, we utilize a change-of-variables with Semi-Definite Relaxation (SDR) approach to overcome the non-concave objective function and the non-convex optimization constraints. Our results demonstrate that the proposed hybrid IRS with FD relay scheme is able to achieve a significant performance gain over both the hybrid IRS with Half-Duplex (HD) relay as well as the IRS-only scheme, given that the self-interference at the relay is sufficiently suppressed.
59 citations
TL;DR: In this paper, the authors derived exact end-to-end SNR expressions for RIS-aided and amplify-and-forward (AF) relay systems, and proposed a novel and simple way to obtain the optimal phase shifts at the RIS elements.
Abstract: Reconfigurable Intelligent Surface (RIS) can create favorable multipath to establish strong links that are useful in millimeter wave (mmWave) communications. While previous works assumed Rayleigh or Rician fading, we use the fluctuating two-ray (FTR) distribution to model the small-scale fading in mmWave frequency. First, we obtain the statistical characterizations of the product of independent FTR random variables (RVs) and the sum of product of FTR RVs. For the RIS-aided and amplify-and-forward (AF) relay systems, we derive exact end-to-end signal-to-noise ratio (SNR) expressions. To maximize the end-to-end SNR, we propose a novel and simple way to obtain the optimal phase shifts at the RIS elements. The optimal power allocation scheme for the AF relay system is also proposed. Furthermore, we evaluate important performance metrics including the outage probability and the average bit-error probability. To validate the accuracy of our analytical results, Monte-Carlo simulations are subsequently conducted to provide interesting insights. It is found that the RIS-aided system can attain the same performance as the AF relay system with low transmit power. More interestingly, as the channel conditions improve, the RIS-aided system can outperform the AF relay system using a smaller number of reflecting elements.
56 citations
TL;DR: Theoretical analysis indicates that the NOMA-DF-relay protocol outperforms the conventional orthogonal multiple access (OMA) based DF- Relay protocol in terms of data rate and the proposed algorithms can further significantly improve the network performance in comparison with the other schemes.
Abstract: In this paper, we investigate the application of the non-orthogonal multiple access (NOMA) technique into the unmanned aerial vehicle (UAV) aided relay networks. Specifically, we first incorporate the NOMA protocol with the decode-and-forward (DF) relay protocol to enhance the performance of the cell edge users in a macrocell network. Theoretical analysis indicates that the NOMA-DF-relay protocol outperforms the conventional orthogonal multiple access (OMA) based DF-relay protocol in terms of data rate. To fully exploit the advantages of the proposed protocol, we formulate a joint UAV height optimization, channel allocation, and power allocation problem with the objective to maximize the total data rate of the cell edge users under the coverage of the UAV. For solving the formulated problem effectively, we first analyze its property and employ the golden section method to propose a general framework to obtain the optimal height of the UAV. Then, we design a low-complexity iterative algorithm to solve the joint channel-and-power allocation problem based on the matching theory and the Lagrangian dual decomposition technique. Finally, simulation results demonstrate that the NOMA-DF-relay protocol is superior to the OMA-DF-relay protocol even when the system parameters are not optimized, and the proposed algorithms can further significantly improve the network performance in comparison with the other schemes.
54 citations
TL;DR: A Parallel joint Optimized Relay Selection (PORS) protocol is proposed to reduce collision, delay as well as energy consumption for wake-up radio enabled WSNs and a relay node selection approach is proposed by comprehensively considering factors such as the number of data packets, waiting time, and remaining energy.
53 citations
TL;DR: In this article, the authors investigated the massive access for a satellite-aerial-terrestrial network (SATN), where a high-altitude platform (HAP) is deployed as a relay to assist the uplink transmission from terrestrial user equipment (UE) to satellite.
Abstract: This paper investigates the massive access for a satellite-aerial-terrestrial network (SATN), where a high-altitude platform (HAP) is deployed as a relay to assist the uplink transmission from terrestrial user equipment (UE) to satellite. Unlike previous works, we adopt radio frequency (RF) and free space optical for the aerial-terrestrial and satellite-aerial links, respectively. Specifically, by assuming that imperfect angular information (IAI) of each UE is known at the HAP, we develop a space division multiple access (SDMA) scheme to maximize the ergodic sum rate (ESR). To this end, we first exploit the IAI to calculate the analytical expression of channel correlation matrix. Then, by considering the limitation of array freedom, we propose a subspace-based UE grouping and scheduling scheme to cluster all UEs into groups. Next, we present a computationally effective beamforming (BF) scheme for each UE at HAP to efficiently implement SDMA in the RF link. Furthermore, a closed-form expression for the ESR of the SATN is derived to validate the proposed BF and SDMA schemes. Finally, simulation results corroborate the derived theoretical formulas and reveal the impacts of array size, angular estimation error, the number of UEs and scheduling threshold on the system performance.
53 citations
TL;DR: The effect of hardware impairments (HIs) on the secrecy performance of NOMA-based integrated satellite multiple-terrestrial relay networks (ISMTRNs) is studied and Monte Carlo simulations for the secrecy energy efficiency (SEE) are obtained.
Abstract: Integrated satellite terrestrial networks and non-orthogonal multiple access (NOMA) have been confirmed to be promising and effective approaches to achieve substantial performance gains for future wireless paradigms. This paper studies the effect of hardware impairments (HIs) on the secrecy performance of NOMA-based integrated satellite multiple-terrestrial relay networks (ISMTRNs). Particularly, we consider two important wiretapping cases: Case I, colluding case: all eavesdroppers cooperatively overhear the information; and Case II, non-colluding case: one eavesdropper with the best wiretapping quality is chosen to overhear the main channel information. Specially, the closed-form expressions for the secrecy outage probability (SOP) of the considered NOMA-based ISMTRNs in the presence of the above two eavesdropping cases and partial relay selection scheme are obtained. To obtain further insights in high signal-to-noise ratios (SNRs) regime, the asymptotic analysis of SOP with two considered cases are also derived, which give efficient means to evaluate the benefit of NOMA scheme and the impacts of HIs on the SOP. Moreover, we obtain Monte Carlo (MC) simulations for the secrecy energy efficiency (SEE).
52 citations
TL;DR: This paper proposes an iterative algorithm to effectively obtain the locally optimal solution to the throughput optimization problems and presents the resulting trajectories over the atmospheric condition, the buffer size, and the delay requirement.
Abstract: In this paper, we investigate a mobile relaying system assisted by an unmanned aerial vehicle (UAV) with a finite size of the buffer. Under the buffer size limit and delay constraints at the UAV relay, we consider a dual-hop mixed free-space optical/radio frequency (FSO/RF) relaying system (i.e., the source-to-relay and relay-to-destination links employ FSO and RF links, respectively). Taking an imbalance in the transmission rate between RF and FSO links into consideration, we address the trajectory design of the UAV relay node to obtain the maximum data throughput at the ground user terminal. Specifically, we classify two relaying transmission schemes according to the delay requirements, i.e., i) delay-limited transmission and ii) delay-tolerant transmission. Accordingly, we propose an iterative algorithm to effectively obtain the locally optimal solution to our throughput optimization problems and further present the complexity analysis of this algorithm. Through this algorithm, we present the resulting trajectories over the atmospheric condition, the buffer size, and the delay requirement. In addition, we show the optimum buffer size and the throughput-delay tradeoff for a given system. The numerical results validate that the proposed buffer-aided and delay-considered mobile relaying scheme obtains 223.33% throughput gain compared to the conventional static relaying scheme.
52 citations
TL;DR: An effective iterative optimization algorithm is proposed to resolve the proposed optimization problem through Lagrangian dual function and results validate that the performance of the algorithm can improve energy efficiency of the system effectively.
Abstract: Smart agriculture is able to optimize the information resources of agriculture, which can improve the quality and productivity of agricultural products. Wireless sensor networks (WSNs) provide smart agriculture with effective solutions for collecting, transmitting, and processing of information. However, the large number of sensor networks consume too much energy that violates the principle of green communication. Simultaneous wireless information and power transfer (SWIPT) technology utilizes radio-frequency signals to transmit information and provide energy to WSNs, which can extend the lifetime of WSNs effectively. In this article, an architecture design of smart agriculture is first proposed by exploiting the SWIPT. Then, an energy efficiency optimization scheme is studied to achieve green communication, in which the subcarriers’ pairing and power allocation are jointly optimized. The process of communication is divided into two phases. Specifically, in the first phase, source sensor sends information to relay sensor and destination sensor. Relay sensor utilizes a part of the subcarriers to receive the information, and utilizes the remaining subcarriers to collect energy. Destination sensor uses all the subcarriers to receive the information. In the second phase, relay sensor utilizes the energy collected in the first phase to forward the information to destination sensor. An effective iterative optimization algorithm is proposed to resolve the proposed optimization problem through Lagrangian dual function. Simulation results validate that the performance of the algorithm can improve energy efficiency of the system effectively.
TL;DR: This paper studies the throughput optimization of IWSNs with energy harvesting from the interference Radio Frequency (RF) signal considering the reliability constraint of the industrial information transmission.
Abstract: In industrial wireless sensor networks (IWSNs), a lot of energy is wasted in the form of electromagnetic radiations. It can be effectively utilized with energy harvesting (EH), which absorbs part of the energy in the transmission signal but reduces the throughput and reliability of IWSNs. In this article, we study the throughput optimization of IWSNs with EH from the interference radio-frequency (RF) signal considering the reliability constraint of the industrial information transmission. Under the premise of limited energy supply of EH relays, the throughput maximization of IWSNs is formulated as a nonconvex optimization problem. In order to transform the nonconvex problem to a convex optimization problem, the successive convex approximation (SCA) approach is adopted. Furthermore, a power allocation algorithm is designed to maximize the total transmission rate of the network. Simulation results demonstrate that the proposed algorithm can maximize the throughput under the primise of SINR reliability.
TL;DR: Using two drones, where one distributes the entangled photons and the other serves as relay node, the authors in this article achieved entanglement distribution with Clauser-Horne-Shimony-Holt S parameter of 2.59±0.11 at 1 km distance.
Abstract: Entanglement distribution has been accomplished using a flying drone, and this mobile platform can be generalized for multiple mobile nodes with optical relay among them. Here we develop the first optical relay to reshape the wave front of photons for their low diffraction loss in free-space transmission. Using two drones, where one distributes the entangled photons and the other serves as relay node, we achieve entanglement distribution with Clauser-Horne-Shimony-Holt S parameter of 2.59±0.11 at 1 km distance. Key components for entangled source, tracking, and relay are developed with high performance and are lightweight, constructing a scalable airborne system for multinode connectio and toward mobile quantum networks.
TL;DR: In this article, a multi-agent deep reinforcement learning-based buffer-aided relay selection scheme was proposed for an intelligent reflecting surface (IRS)-assisted secure cooperative network in the presence of an eavesdropper.
Abstract: This paper proposes a multi-agent deep reinforcement learning-based buffer-aided relay selection scheme for an intelligent reflecting surface (IRS)-assisted secure cooperative network in the presence of an eavesdropper. We consider a practical phase model where both phase shift and reflection amplitude are discrete variables to vary the reflection coefficients of the IRS. Furthermore, we introduce the buffer-aided relay to enhance the secrecy performance, but the use of the buffer leads to the cost of delay. Thus, we aim to maximize either the average secrecy rate with a delay constraint or the throughput with both delay and secrecy constraints, by jointly optimizing the buffer-aided relay selection and the IRS reflection coefficients. To obtain the solution of these two optimization problems, we divide each of the problems into two sub-tasks and then develop a distributed multi-agent reinforcement learning scheme for the two cooperative sub-tasks, each relay node represents an agent in the distributed learning. We apply the distributed reinforcement learning scheme to optimize the IRS reflection coefficients, and then utilize an agent on the source to learn the optimal relay selection based on the optimal IRS reflection coefficients in each iteration. Simulation results show that the proposed learning-based scheme uses an iterative approach to learn from the environment for approximating an optimal solution via the exploration of multiple agents, which outperforms the benchmark schemes.
TL;DR: The proposed deep Q-network-based underwater relay selection strategy improves the communication efficiency compared with the Q-learning-based strategy, and the number of iterations needed for convergence can be effectively reduced.
Abstract: Internet of Underwater Things (IoUT) consists of numerous sensor nodes distributed in an underwater area for sensing, collecting, processing information, and sending related messages to the data processing center. However, the characteristics of the underwater environment will bring strict limitations on communication coverage and power scarcity to IoUT networks. Applying cooperative communications to IoUT networks can expand the communication range and alleviate power shortages. In this article, we investigate the cooperative communication problem in a power-limited cooperative IoUT system and propose a reinforcement learning-based underwater relay selection strategy. Specifically, we first determine the optimal transmit powers of the source node and the selected underwater relay to maximize the end-to-end signal-to-noise ratio of the system. Then, we formulate the underwater cooperative relaying process as a Markov process and apply reinforcement learning to obtain an effective underwater relay selection strategy. The simulation results show that the performance of the proposed scheme outperforms that of the equal transmit power settings under the same conditions. In addition, the proposed deep Q-network-based underwater relay selection strategy improves the communication efficiency compared with the Q-learning-based strategy, and the number of iterations needed for convergence can be effectively reduced.
TL;DR: This article proposes an adaptive distance relay setting to protect distribution line connecting the PV plant, using prefault voltage and current data at the relaying point, and calculates positive- and negative-sequence PV source impedances for boundary setting of distance relay at the PV side.
Abstract: Distribution lines are generally protected by overcurrent relays. With the integration of an inverter-interfaced solar photovoltaic (PV) plant having a current-limiting feature, the fault current seen by the relay on the PV side of that feeder becomes comparable to the load current. The conventional overcurrent relaying principle is not suitable for distribution line protection. Distance relay may be a viable option for protection of distribution lines connecting the solar PV plant. However, positive- and negative-sequence source impedances of the PV plant depend on an inverter controller, which results in limited performance of fixed setting distance relay. This article proposes an adaptive distance relay setting to protect distribution line connecting the PV plant, using prefault voltage and current data at the relaying point. The method calculates positive- and negative-sequence PV source impedances for boundary setting of distance relay at the PV side. The proposed trip boundary is modified adaptively with the change in prefault conditions of the PV plant. Performance of the proposed method is tested for different control strategies and operating capacities of the PV plant and variation in the grid source impedance on a 34-bus distribution system. Real-time application of the proposed method is validated in hardware-in-the-loop using OPAL-RT simulators with IEC 61850 as the communication protocol and found to be accurate.
TL;DR: A power control algorithm in energy harvesting-based cognitive mobile relay networks where an UAV is equipped with a decode-and-forward (DF) relay to cooperate the communication of secondary user (SU) is proposed.
Abstract: In order to extend the communication coverage and improve system performance, the applications of unmanned aerial vehicles (UAVs) in wireless communications have attracted a lot of attention in the industry. In this paper, we propose a power control algorithm in energy harvesting (EH)-based cognitive mobile relay networks where an UAV is equipped with a decode-and-forward (DF) relay to cooperate the communication of secondary user (SU). Assuming that the only power source for SU transmitter with EH is a battery with infinite capacity, we solve a throughput maximization problem to optimize the transmit powers of SU and the mobile relay, subject to the causality constraint of energy usage at SU transmitter, the maximum transmit power constraint of the mobile relay, and the interference temperature (IT) constraint to protect the communication of primary user (PU). When formulating this throughput maximization problem, we adopt an offline scheme with deterministic settings. For simplicity, the original multi-variable optimization problem is transformed into a single variable optimization problem via the optimal throughput principle of the DF relaying communication system. Furthermore, we solve this new optimization problem via the Lagrange dual method, and we derive the closed-form expressions of the optimal solutions. The simulation results illustrate the optimized system performance that the optimal throughput of the secondary system can be achieved by the proposed dynamic power control algorithm.
TL;DR: A spectrum-efficient scheme for NOMA-based CDRT over Nakagami- $m$ fading channels is proposed and the closed-form expressions for the average SE, the user fairness index and the energy efficiency (EE) as well as the asymptotic average SE are derived.
Abstract: Although the use of coordinated direct and relay transmission (CDRT) in non-orthogonal multiple access (NOMA) can extend the coverage, its duplicated transmission reduces the spectrum efficiency (SE) of NOMA. To improve the SE, we propose a spectrum-efficient scheme for NOMA-based CDRT over Nakagami- $m$ fading channels. In this scheme, the base station (BS) connects with a cell-center user (CCU) directly while communicating with a cell-edge user (CEU) via a relay and the CCU. Then, the relay and the CCU use network coding to process and retransmit the signals sent by the BS first and the CEU later. Finally, the BS and the relay simultaneously broadcast downlink signals. We derive the closed-form expressions for the average SE, the user fairness index and the energy efficiency (EE) as well as the asymptotic average SE using both perfect and imperfect successive interference cancellation (SIC). Simulations verify the correctness of our theoretical analysis and the superiority of the proposed scheme in SE and EE.
TL;DR: In this paper, the authors proposed a robust protection scheme in which the relay coordination settings are optimized based on the network layout to obtain the minimum operating time while satisfying protection coordination constraints.
Abstract: Technology advancement for renewable energy resources and its integration to the distribution network (DN) has garnered substantial interest in the last few decades. Integrating such resources has proven to reduce power losses and improve the reliability of DN. However, the growing number of these resources in DN has imposed additional operational and control issues in voltage regulation, system stability, and protection coordination. Incorporation of various types of distributed generators (DG) into DN causes significant changes in the system. These including new fault current sources, new fault levels, a blinding effect in the protection scheme, reduction in the reach of relays, and decrement in the detection of low-level fault currents for existing relays. Such changes will jeopardize the effectiveness of the entire protection scheme in the DN. This research aims to propose a robust protection scheme in which the relay coordination settings are optimized based on the network layout. The potential impacts of DGs on the DN are mitigated by utilizing a user-defined overcurrent-based relay characteristic to obtain the minimum operating time while satisfying protection coordination constraints. A hybrid optimization algorithm based on Metaheuristic and Linear Programming that has the capability to attain the optimal solution and reduces computational time is proposed in this work. The performance of the proposed technique is tested on radial DN integrated with microgrid (MG). The results obtained show the proposed technique has successfully reduced the relay operating time while meeting the protection coordination requirements for dynamic operating modes of a network.
TL;DR: In this paper, the outage and throughput performance of NOMA based underlay cognitive radio (i.e., CR-NOMA) network under partial relay selection (PRS) scheme was investigated.
Abstract: This paper investigates outage and throughput performance of non-orthogonal multiple access (NOMA) based underlay cognitive radio (i.e., CR-NOMA) network under partial relay selection (PRS) scheme. Here NOMA is used in the secondary network, where K half duplex, decode-and-forward (DF) relays are available for assisting the secondary base station (SBS) to deliver information to the secondary users (SUs). We derive analytical expressions for the outage probabilities experienced by the SUs under imperfect successive interference cancellation (i-SIC) conditions. The interference threshold constraint of the primary receiver and the maximum transmit power constraint of the secondary nodes are considered for the analysis. To provide more insights on the outage performance, we obtain expressions for the asymptotic outage probabilities of the SUs in the high transmit power region. Next, we derive analytical expressions for the optimal power allocation (OPA) factors at the SBS and at the selected relay that independently maximize the delay limited throughput of the secondary network in CR-NOMA system. We also determine the jointly optimal power allocation factors that maximize the throughput. We use numerical and simulation investigations to establish that the outage probabilities of the SUs reduce significantly while the throughput improves under the considered PRS scheme, compared to random relay selection and fixed relaying strategies. Further, it is established that the proposed OPA factors can significantly reduce the outage probabilities experienced by the SUs and improve the system throughput compared to equal/random power allocation schemes.
TL;DR: A new network architecture of hybrid trusted/untrusted relay based QKD deployment over optical backbone networks is described, where the node structures of the trusted relay and untrusted relay are elaborated and an integer linear programming model and a heuristic algorithm are designed to optimize the deployment cost.
Abstract: Quantum key distribution (QKD) has demonstrated a great potential to provide future-proofed security, especially for 5G and beyond communications. As the critical infrastructure for 5G and beyond communications, optical networks can offer a cost-effective solution to QKD deployment utilizing the existing fiber resources. In particular, measurement-device-independent QKD shows its ability to extend the secure distance with the aid of an untrusted relay. Compared to the trusted relay, the untrusted relay has obviously better security, since it does not rely on any assumption on measurement and even allows to be accessed by an eavesdropper. However, it cannot extend QKD to an arbitrary distance like the trusted relay, such that it is expected to be combined with the trusted relay for large-scale QKD deployment. In this work, we study the hybrid trusted/untrusted relay based QKD deployment over optical backbone networks and focus on cost optimization during the deployment phase. A new network architecture of hybrid trusted/untrusted relay based QKD over optical backbone networks is described, where the node structures of the trusted relay and untrusted relay are elaborated. The corresponding network, cost, and security models are formulated. To optimize the deployment cost, an integer linear programming model and a heuristic algorithm are designed. Numerical simulations verify that the cost-optimized design can significantly outperform the benchmark algorithm in terms of deployment cost and security level. Up to 25% cost saving can be achieved by deploying QKD with the hybrid trusted/untrusted relay scheme while keeping much higher security level relative to the conventional point-to-point QKD protocols that are only with the trusted relays.
TL;DR: This letter proposes a deep reinforcement learning (DRL) based relay selection scheme for cooperative networks with the intelligent reflecting surface (IRS) and introduces DRL to learn from the environment to obtain the solution and reduce the computational complexity.
Abstract: This letter proposes a deep reinforcement learning (DRL) based relay selection scheme for cooperative networks with the intelligent reflecting surface (IRS). We consider a practical phase-dependent amplitude model in which the IRS reflection amplitudes vary with the discrete phase-shifts. Furthermore, we apply the relay selection to reduce the signal loss over distance in IRS-assisted networks. To solve the complicated problem of joint relay selection and IRS reflection coefficient optimization, we introduce DRL to learn from the environment to obtain the solution and reduce the computational complexity. Simulation results show that the throughput is significantly improved with the proposed DRL-based algorithm compared to random relay selection and random reflection coefficients methods.
TL;DR: It is demonstrated that RIS-assisted systems can effectively improve the performance of mixed dual-hop RF-UWOC systems.
Abstract: In this article, we investigate the performance of a reconfigurable intelligent surface (RIS)-assisted dual-hop mixed radio-frequency underwater wireless optical communication (RF-UWOC) system. An RIS is an emerging and low-cost technology that aims to enhance the strength of the received signal, thus improving the system performance. In the considered system setup, a ground source does not have a reliable direct link to a given marine buoy and communicates with it through an RIS installed on a building. In particular, the fixed buoy acts as a relay that sends the signal to an underwater destination. In this context, analytical expressions for the outage probability (OP), average bit error rate (ABER), and average channel capacity (ACC) are derived assuming fixed-gain amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols at the marine buoy. Moreover, asymptotic analyses of the OP and ABER are carried out in order to gain further insights from the analytical frameworks. In particular, the system diversity order is derived and it is shown to depend on the RF link parameters and on the detection schemes of the UWOC link. Finally, it is demonstrated that RIS-assisted systems can effectively improve the performance of mixed dual-hop RF-UWOC systems.
TL;DR: In this paper, an opportunistic relay selection (ORS) scheme was proposed to maximize the end-to-end signalto-noise ratio in cognitive IoT systems with multiple primary receivers (PRs).
Abstract: In this paper, we study short-packet communications in wireless-powered cognitive Internet-of-Things (IoT) networks with multiple primary receivers (PRs). The considered system can be applied for small factory automations, where a source and multiple relays harvest energy from a multi-antenna dedicated power beacon (PB) to send short packets to a robot destination for controlling purposes under cognitive radio constraint imposed by PRs. We propose an opportunistic relay selection (ORS) scheme to maximize the end-to-end signal-to-noise ratio in cognitive IoT systems. Closed-form expressions for the average block error rate (BLER) of the proposed system are obtained, based on which the performance floor analysis, goodput, and energy efficiency (EE) are also carried out. Relying on analytical results, we develop a deep learning framework for the BLER prediction with high accuracy and short execution time. Simulation results show the BLER, goodput, and EE improvements of the ORS scheme over conventional relay selection schemes. Moreover, the developed deep learning-based evaluation model achieves the equivalent performance as the ORS scheme in terms of BLER, goodput, and EE, while remarkably reducing the execution time in cognitive IoT systems.
TL;DR: In this article, a UAV-assisted Internet of Things (IoT) network is proposed to collect data from time-constrained IoT devices and then transfer it to a ground gateway (GW).
Abstract: Unmanned aerial vehicle (UAV) communication has emerged as a prominent technology for emergency communications (e.g., natural disaster) in the Internet of Things (IoT) networks to enhance the ability of disaster prediction, damage assessment, and rescue operations promptly. A UAV can be deployed as a flying base station (BS) to collect data from time-constrained IoT devices and then transfer it to a ground gateway (GW). In general, the latency constraint at IoT devices and UAV’s limited storage capacity highly hinder practical applications of UAV-assisted IoT networks. In this paper, full-duplex (FD) radio is adopted at the UAV to overcome these challenges. In addition, half-duplex (HD) scheme for UAV-based relaying is also considered to provide a comparative study between two modes (viz., FD and HD). Herein, a device is considered to be successfully served iff its data is collected by the UAV and conveyed to GW timely during flight time. In this context, we aim to maximize the number of served IoT devices by jointly optimizing bandwidth, power allocation, and the UAV trajectory while satisfying each device’s requirement and the UAV’s limited storage capacity. The formulated optimization problem is troublesome to solve due to its non-convexity and combinatorial nature. Towards appealing applications, we first relax binary variables into continuous ones and transform the original problem into a more computationally tractable form. By leveraging inner approximation framework, we derive newly approximated functions for non-convex parts and then develop a simple yet efficient iterative algorithm for its solutions. Next, we attempt to maximize the total throughput subject to the number of served IoT devices. Finally, numerical results show that the proposed algorithms significantly outperform benchmark approaches in terms of the number of served IoT devices and system throughput.
TL;DR: This paper provides a comprehensive performance evaluation of UAV relay networks (UAVRNs) employing non-orthogonal multiple access (NOMA) technique and characterize the outage and ergodic capacity performance of NOMA-enabled UAV RNs.
Abstract: The development of unmanned aerial vehicles (UAVs) is one of the driving forces for revolutionizing wireless communications in the modern era. Although there are many unique features of UAV networks, their widespread use is still hampered by the short battery life. As a result, most of the research efforts in this domain mainly have focused on the efficient use of energy through trajectory planning and cooperative communication. While there have been a few proposals to use energy harvesting techniques for UAV networks, a complete understanding of the performance limits of such networks is still missing in the literature and needs further investigations. Within this context, our paper provides a comprehensive performance evaluation of UAV relay networks (UAVRNs) employing non-orthogonal multiple access (NOMA) technique. Specifically, a cooperative communication system, where a UAV serves as a mobile relay, is fully considered. The UAV relay is considered to be wirelessly-powered and harvests energy from the radio signals received from a nearby base station. For the sake of comparison, both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols are considered. Subsequently, the closed-form expressions of outage probabilities and ergodic capacities are provided for each UAV relaying protocol. Extensive simulations are performed to verify the accuracy of the derived closed-form expressions. The results provided in this work characterize the outage and ergodic capacity performance of NOMA-enabled UAVRNs.
TL;DR: This work fully considered the real characteristics of IoTs systems (i.e., data aggregation and link heterogeneity), and proposed a realistic cascading model based on the layered architecture of the IoTs, which is driven by the overload events of relay nodes, base stations and communication links.
TL;DR: A single-cell multi-user orthogonal frequency division multiple access (OFDMA) network with one unmanned aerial vehicle (UAV), which works as an amplify-and-forward relay to improve the quality-of-service (QoS) of the user equipments (UEs) in the cell edge is considered.
Abstract: In this paper, we consider a single-cell multi-user orthogonal frequency division multiple access (OFDMA) network with one unmanned aerial vehicle (UAV), which works as an amplify-and-forward relay to improve the quality-of-service (QoS) of the user equipments (UEs) in the cell edge. Aiming to improve the throughput while guaranteeing the user fairness, we jointly optimize the communication mode, subchannel allocation, power allocation, and UAV trajectory, which is an NP-hard problem. To design the UAV trajectory and resource allocation efficiently, we first decompose the problem into three subproblems, i.e., mode selection and subchannel allocation, trajectory optimization, and power allocation, and then solve these subproblems iteratively. Simulation results show that the proposed algorithm outperforms the random algorithm and the cellular scheme.
TL;DR: This article proposes a scheduling protocol that exploits time-division multiple access (TDMA) to serve different source–destination pairs in turns when flying along an optimized trajectory and formulate a joint optimization problem of the TDMA-based user scheduling, the dynamic UAV trajectory, and the UAV transmit power to maximize the system throughput.
Abstract: Based on the advantages of small size, lightweight, as well as flexible deployment and recycling, unmanned aerial vehicle (UAV) has been more and more widely used in military and civilian. As flying relays, UAVs can quickly set up relay communication links for different missions, to enhance the receiving signal power, increase the system capacity, and expand the communication coverage. In this article, we investigate full-duplex (FD) UAV relaying for multiple source–destination pairs. To fully exploit the flying flexibility of the UAV in serving multiple source–destination pairs, we propose a scheduling protocol that exploits time-division multiple access (TDMA) to serve different source–destination pairs in turns when flying along an optimized trajectory. Then, we further formulate a joint optimization problem of the TDMA-based user scheduling, the dynamic UAV trajectory, and the UAV transmit power to maximize the system throughput. The formulated problem is nonconvex that makes it difficult to solve directly, hence we propose an iterative algorithm to obtain an approximate optimal solution based on block coordinate descent and successive convex optimization techniques. Simulation results demonstrate that our proposed FD-based UAV relaying network achieves significant throughput gains compared with the half-duplex (HD) baseline, and the TDMA-based protocol outperforms the OFDMA-based ones with fixed UAV position/trajectory when the UAV helps relay information for multiple source–destination pairs.
19 Feb 2021
TL;DR: In this article, a comparison between decode-and-forward (DF) relays and reconfigurable intelligent surfaces (RISs) in the case where only one relay or RIS is selected based on the maximization of the signal-to-noise-ratio (SNR) was made.
Abstract: This paper aims to make a comparison between decode-and-forward (DF) relays and reconfigurable intelligent surfaces (RISs) in the case where only one relay or RIS is selected based on the maximization of the signal-to-noise-ratio (SNR). Our study accounts for the spatial distribution of RISs and relays, which is assumed to follow a Poisson point process (PPP). It considers two different path loss models corresponding to RIS/relays randomly located in the near-field and the far-field of the transmitter. Based on the Gamma distribution moment matching method and tools from stochastic geometry, we derive approximations for the outage probability (OP) as well as the energy efficiency (EE) of the RISs-assisted system in the near-field and the far-field scenarios separately. Under the same conditions as RIS, the expressions for OP and EE of the half-duplex and the full-duplex DF relays-assisted systems are also derived. Simulation results are presented to corroborate the proposed analysis and compare between the three technologies. Our results show that RIS is the best choice in the near-field case, regardless of the OP or EE criterion. Compared to half-duplex relays and full-duplex relays, the RIS based system is the most energy-efficient solution to assist communication. Moreover, RIS allows for an improvement in both OP and EE when equipped with more reflecting elements or more densely deployed.