Sum Throughput Research Articles

Outage performance of NOMA with Alamouti/MRC in dual-hop energy harvesting relay networks

This paper presents outage performance of Alamouti coding/maximum ratio combining (Alamouti/MRC) scheme in dual-hop non-orthogonal multiple access network taking advantage of simultaneous wireless information and power transfer (SWIPT). In the considered system, a base station simultaneously serves multiple users via energy harvesting enabled amplify-and-forward relay by exploiting Alamouti coding, and the users employ MRC technique. Thereby, not only spatial diversity is exploited but also channel state information burden can be reduced in terms of transmit diversity, in addition to increasing energy efficiency by SWIPT. Exact outage probability for any user is obtained over Nakagami-m fading channels by considering the channel estimation error (CEE), together with asymptotic expression providing more valuable insights. Besides, the sum throughput and energy efficiency are also investigated to characterize the system performance. Finally, the analytical results are verified by the Monte Carlo simulations. Results show that a variety of trade-off cases arise for optimum performance.

Deep Reinforcement Learning Heterogeneous Channels for Poisson Multiple Access

This paper proposes a medium access control (MAC) protocol based on deep reinforcement learning (DRL), i.e., multi-channel transmit deep-reinforcement learning multi-channel access (MCT-DLMA) in heterogeneous wireless networks (HetNets). The work concerns practical unsaturated channel traffic that arrives following a Poisson distribution instead of saturated traffic that arrives before.By learning the access mode from historical information, MCT-DLMA can well fill the spectrum holes in the communication of existing users. In particular, it enables the cognitive user to multi-channel transmit at a time, e.g., via the multi-carrier technology. Thus, the spectrum resource can be fully utilized, and the sum throughput of the HetNet is maximized. Simulation results show that the proposed algorithm provides a much higher throughput than the conventional schemes, i.e., the whittle index policy and the DLMA algorithms for both the saturated and unsaturated traffic, respectively. In addition, it also achieves a near-optimal result in dynamic environments with changing primary users, which proves the enhanced robustness to time-varying communications.

Sum throughput enhancement of wireless powered IoT communication network assisted by IRS with a practical reflection model

Sum throughput enhancement of wireless powered IoT communication network assisted by IRS with a practical reflection model

Analysis of Individual User Data Rate in a TDMA-RIS-NOMA Downlink System: Beyond the Limitation of Conventional NOMA

Non-orthogonal multiple access (NOMA) is playing a pivotal role in 5G technology and has the potential to be useful in future developments beyond 5G. Although the effectiveness of NOMA has largely been explored in the sum throughput maximization, the identification of individual user data rate (IDR) still remained an unexplored area. Previously, it has been shown that reconfigurable intelligent surfaces (RIS) can lead to an overall improvement in the data rate by enhancing the effective channel gain of the downlink NOMA system. When time division multiple access (TDMA) is clubbed with multiple RISs in a distributed RIS-assisted NOMA (TDMA-RIS-NOMA) downlink system, a point-to-point communication model is created between access point-to-RIS-to-user device. Due to this point-to-point communication model, optimization of the phase shifts provided by meta-atoms of each RIS is facilitated. The optimized phase shifts of meta-atoms maximize the equivalent channel gain between the access point to the user. In this scenario, the channel becomes saturated and signal-to-interference plus noise ratio (SINR) becomes a function of power coefficients only. In this study, the power coefficients are calculated to maximize the SINR of each user belonging to a NOMA cluster using a geometric progression-based power allocation method such that IDR reaches its upper bound. These observations are also verified using the recently published magic matrix-based power allocation method. There are two observations from this study: (i) the IDR is better in the case of the TDMA-RIS-NOMA downlink system than using downlink NOMA alone and (ii) irrespective of the number of meta-atoms and total cluster power, the upper bound of IDR cannot be increased beyond a certain limit for all users except the highest channel gain user. Because of the restricted upper bound for IDR, we suggest that the RIS-assisted downlink TDMA-NOMA system is more suitable for IoT applications, where minimum IDR can also suffice

Multi-Agent Reinforcement Learning-Based Decentralized Spectrum Access in Vehicular Networks With Emergent Communication

In this letter, we propose a novel decentralized spectrum access algorithm based on the multi-agent reinforcement learning (MARL) for cellular vehicle-to-everything (C-V2X) networks. The agents make decisions independently with the global objective of maximizing vehicle-to-infrastructure (V2I) users’ sum throughput while meeting vehicle-to-vehicle (V2V) users’ latency and reliability requirements. Specifically, to achieve better collaboration among agents we introduce the inter-agent communication mechanism into MARL. So, in the proposed algorithm each agent consists of an action selector module and a message generator module, i.e., the agents learn emergent communication via an additional dedicated channel to enable explicit collaboration, which helps with learning effective policies for channel access. Simulation results show the effectiveness of the proposed algorithm, in improving both V2I users’ throughput and V2V users’ packet delivery ratio.

Improve the throughput of M-to-1 free-space optical systems by employing uniquely decodable codes

This paper utilizes uniquely decodable codes (UDCs) in an M-to-1 free-space optical (FSO) system. Benefiting from UDCs’ nonorthogonal nature, the sum throughput is improved. We first prove that the uniquely decodable property still holds, even in optical fading channels. It is further discovered that the receiver can extract each source’s data from superimposed symbols with only one processing unit. According to theoretical analysis and simulation results, the throughput gain is up to the normalized UDC’s sum rate in high signal-to-noise ratio cases. An equivalent desktop experiment is also implemented to show the feasibility of the UDC-FSO structure.

UAV-Net+: Effective and Energy-Efficient UAV Network Deployment for Extending Cell Tower Coverage With Dynamic Demands

Nowadays, mobile data traffic is growing explosively, but in many realistic scenarios, users still often encounter insufficient or unstable network bandwidth. A promising solution is provided by unmanned aerial vehicle mounted base stations (UAV-BSs) to serve regions with bandwidth shortfall. It is significant to investigate how to deploy UAVs effectively and efficiently for maximizing the sum throughput and service time of a set of mobile clients scattered in a large environment. A basic idea is to use RF ray tracing simulations as a hint to narrow down the search space of UAVs for conducting actual measurements. Furthermore, we formulate two key sub-problems, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">chunk selection</i> , which selects an optimal subset of chunks in the region as the search space of UAVs, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">chunk search</i> , which plans the paths of UAVs to cover all selected chunks for conducting measurements, provide network services and charge timely under energy constraints to maximize the total service time. As they are both proved to be NP-hard, a 3D convolutional deep reinforcement learning (DRL) based chunk selection algorithm and an energy-aware DRL-based chunk search algorithm are proposed to solve them efficiently. A prototype system, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UAV-Net+</i> , is implemented to evaluate the feasibility by conducting measurements by an UAV mounted WiFi AP communicating with several clients scattered in a campus, reporting an obvious throughput gain with a small measurement overhead and time consumption. Extensive simulations demonstrate the effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3D convolutional DRL-based chunk selection algorithm</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">energy-aware DRL-based chunk search algorithm</i> . It is able to achieve 95.35% effective throughput of the skyline algorithm, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Oracle</i> , with only 0.7% time cost. It is more beneficial for providing long-time network service and balancing the workload among UAVs.

Quasi-Optimization of Resource Allocation and Positioning for Solar-Powered UAVs

Unmanned Aerial Vehicles (UAVs) will be an integral part of future smart cities to provide applications such as traffic management, environment monitoring and data collection. UAVs offer flexible deployment, dynamic mobility, and Ultra-Reliable and Low Latency Communications (URLLC). However, UAVs are power-hungry devices, and their limited battery capacity cannot support their flight and communication operations for a long period. Additionally, multi-carrier (MC) techniques will be vital for supporting futuristic multi-user communication systems. To overcome these issues, we propose a solar-powered UAV MC system to support URLLC services for multi-users. In this regard, we aim to maximize the system sum throughput and we jointly optimize UAV positioning and sub-carrier allocation. To solve the optimization problem, we propose the low-complexity coordinate descent approximation algorithm (CDAA). Lastly, we show the proposed algorithm converges quickly and simultaneously yields superior performance than fixed benchmark schemes for two simulated environments.

Intelligent Reflecting Surfaces for Sum-Rate Maximization in Cognitive Radio Enabled Wireless Powered Communication Network

In this paper, we study a cognitive radio (CR)-enabled wireless powered communication network (WPCN) with an intelligent reflecting surface (IRS). A cognitive wireless powered communication network (CWPCN) consisting of one secondary wireless powered communication network, in which a hybrid access point broadcasts wireless energy to multiple users on downlink and receives information signals on uplink, shares the same spectrum with the existing primary wireless communication network. In this paper, we consider an underlay CWPCN in which the secondary network is regulated by a given interference temperature constraint (ITC) such that interference with the primary network is kept no greater than a predefined threshold. To enhance the performance of the CWPCN, we consider an IRS-assisted network in which multiple passive reflection elements are configured to reflect the signals in any desired phase/direction. Our main goal is to maximize the sum throughput of secondary users while managing the interference constraint. For this, we jointly optimize the uplink and downlink phase shift matrices of the IRS elements with optimal time slots for wireless energy transfer (WET) on downlink and wireless information transfer on uplink. In finding the optimal solution, the formulated optimization problem is non-convex, complex, and intractable. In this paper, we propose an alternating optimization (AO)-based solution with a successive convex approximation (SCA) technique. We show through simulation results that the proposed IRS-assisted network provides a significant enhancement in performance over the conventional CWPCN.

MIMO Unmanned Surface Vessels Enabled Maritime Wireless Network Coexisting With Satellite Network: Beamforming and Trajectory Design

Due to the flexible deployment, unmanned surface vessels (USVs) have attracted much interest recently. To solve the resource scarcity problem at sea, USV needs to leverage existing terrestrial and satellite systems for efficient backhaul and spectrum sharing. In this case, the multiple input multiple output (MIMO) technology can be applied for diversity gain improvement and interference coordination. However, how to adopt MIMO technology into maritime networks with a sparse scattering environment is still an open issue. In this paper, we employ a multi-antenna USV to support on-demand communications. Utilizing the two-ray channel, we aim to maximize the sum throughput over all USV intended users, by jointly optimizing the cooperative beamforming and trajectory, subject to several practical constraints, including the USV kinetics, quality of service requirement and backhaul capacity. Different from existing whole period designs, we decompose the problem into sequential one-slot problems. Within each slot, the non-convex problem is solved iteratively by using problem decomposition and successive convex optimization methods. Then, channel estimation errors are considered to investigate a robust beamforming scheme. Numerical simulations validate that the USV coexists well with the satellite network and show that the beamforming scheme and trajectory design complement each other for performance improvement.

Resource Allocation for Multi-Cluster NOMA-UAV Networks

Combining non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAVs) could achieve better performance for wireless networks. However, effective resource allocation for quality of service (QoS) provision among all users still remains as a great challenge for multi-cluster NOMA-UAV networks. In this paper, we propose a NOMA-UAV scheme, where a UAV is deployed as the mobile base station to serve ground users. To meet the QoS requirements of all users with limited resource, the user clustering and optimal routing are first developed by the K-means algorithm and genetic algorithm, respectively. Then, the sum throughput is maximized by jointly optimizing the transmission power, hovering locations and transmission duration of UAV. To solve this non-convex problem with coupled variables, we decompose it into three subproblems. Among them, the power and location optimizations are also non-convex, which can be transformed into convex ones by successive convex approximation. The duration optimization is a linear programming which can be solved directly. Then, we propose an iterative algorithm to solve these three subproblems alternately. Finally, simulation results are presented to show the effectiveness of the proposed scheme.

CFX: Contention-Free Channel Access for IEEE 802.11ax

Orthogonal frequency-division multiple access (OFDMA) has attracted great attention as a key technology for uplink enhancement for Wi-Fi, since it can effectively reduce network congestion and channel access delay. Unfortunately, the traditional random access protocol of Wi-Fi seldom allows these benefits to be achieved, especially in dense network environments, as the access point (AP) rarely gains the channel access needed to trigger OFDMA uplink transmissions due to severe frame collisions. To address this problem, we propose a new channel access scheme called Contention-Free Channel Access for 802.11ax (CFX). In the proposed scheme, users can access the channel without contention, since they are guaranteed a transmission opportunity immediately after another user's transmission. To realize CFX on top of the existing Buffer Status Report/BSR Poll (BSR/BSRP) exchange protocol of 802.11ax, we develop an additional scheme based on shared channel access that helps the AP to obtain the buffer status of users and manage a contention-free channel access schedule. In addition, in order to appropriately utilize the savings from the reduced frame collisions, we conduct sum throughput maximization using an actor-critic proximal policy optimization (PPO)-based deep reinforcement learning approach. The results of an extensive evaluation show that CFX not only significantly improves the uplink performance of Wi-Fi in terms of throughput and channel access delay but can also dynamically adjust the parameters in response to changes in the network status.

Analyzing the impact of medium access control protocol design and control-plane uplink in asymmetric RF/OWC networks with RF congestion

As the demand for wireless capacity continues to grow, highly directional wireless communication technologies have the potential to provide massive gains in area spectral efficiency. However, novel challenges arise when considering bidirectional connectivity and multi-cell/multi-user systems with highly directional links. Some of these challenges can be alleviated with the introduction of asymmetric connectivity where the highly directional links are used solely for downlink transmission. As an example, we consider asymmetric links with an optical wireless communication (OWC) downlink and sub-6 GHz RF uplink. More specifically, we consider visible light communication as an instance of OWC, although the presented analysis and validation are applicable to alternative OWC technologies and other simplex downlink transmission technologies. While asymmetric connectivity has been previously demonstrated in scenarios like this, the impact of control-plane asymmetry has not been explored, to our knowledge. In this paper, we first introduce the novel challenges related to local handshaking in wireless networks with control-plane asymmetry. We then develop a theoretical framework for throughput analysis in a network where the sub-6 GHz RF channel is shared between a conventional RF link and an asymmetric RF/OWC link. This analysis is validated via simulation and verified in a testbed system using Mango WARP3 software defined radios and a commercially available RF access point. Finally, we use the derived throughput equations to analyze the impact of various protocol parameters and demonstrate one potential use of the derived equations to evaluate sum throughput in the presence of an unreliable OWC link.

Optimal User Cooperation in IRS Assisted WPCN for Practical Non-Linear Phase-Shift Model

This letter is a novel attempt to consider intelligent reflecting surfaces (IRS) assisted user cooperation adopting the practical non-linear phase-shift model in a wireless-powered communication network (WPCN). Specifically, we aim to maximize both max-min fairness and sum throughput among the energy harvesting users while adopting the practical IRS model. We propose an alternating optimization (AO) algorithm exploiting convex relaxation and semidefinite programming to optimize the phase-shift matrices at IRS along with time and power allocation at the hybrid access point serving the users. Simulation results corroborate key analytical claims and show that the proposed jointly-optimal design provides better performance gain for max-min and sum-rate maximization over the existing benchmark schemes.

Resource Optimization of D2D-Assisted CR Network With NOMA for 5G and Beyond Systems

Device-to-device (D2D) communication has emerged as a potential candidate for 5G and beyond networks to optimize spectrum utilization, reduce power consumption, and support higher data rates. The energy and spectral efficiency of D2D communication can be enhanced further by adapting cognitive radio (CR), nonorthogonal multiple access (NOMA), and radio-frequency energy harvesting (RF-EH) technologies. Therefore, this article proposes a sum-rate maximization problem for a D2D-assisted CR network with NOMA, considering the RF-EH mechanism. A joint optimization problem is modeled to maximize the sum throughput of cellular and D2D nodes by considering power assignments, radio resource allocation, user pairing, and transmission time ratio assignment. The problem is then converted into a standard convex optimization problem subject to power allocations at individual nodes, interference temperature limits, individual data rate, and secrecy capacity. The duality theory is adopted to decompose the problem into multiple subproblems, and Karush–Kuhn–Tucker (KKT) conditions are applied to provide the solution. Finally, the proposed schemes are validated through simulation results. The proposed schemes outperform the existing schemes in terms of sum throughput and energy harvesting under different quality of service requirements.

Transmission Optimization and Resource Allocation for Wireless Powered Dense Vehicle Area Network With Energy Recycling

The wireless-powered communication paradigm brings self-sustainability to the on-vehicle sensors by harvesting the energy from radiated radio frequency (RF) signals. This paper proposes a novel transmission and resource allocation strategy for the scenario where multiple wireless powered vehicle area networks (VAN) co-existed with high density. The considered multi-VAN system consists of a remote master access point (MAP), multiple on-vehicle hybrid access points (HAPs) and sensors. Unlike previous works, we consider that the sensors can recycle the radiated radio frequency energy from all the HAPs when HAPs communicate with MAP, so the dedicated signals for energy harvesting (EH) are unnecessary. The proposed strategy can achieve simultaneous wireless information and power transfer (SWIPT) without complex receiver architecture requirements. The extra EH and interference caused by the dense distribution of VANs, which are rarely explored, are fully considered. To maximize the sum throughput of all the sensors while guaranteeing the transmission from HAPs to the MAP, we jointly optimize the time allocation, system energy consumption, power allocation, and receive beamforming. Due to the non-convexity of the formulated problem, we address the sub-problems separately through the Rayleigh quotient, Frobenius norm minimization and convex optimization. Then an efficient iterative algorithm to obtain sub-optimal solutions. The simulation results and discussions illustrate the proposed scheme's effectiveness and advantages.

Joint Optimization Method of Spectrum Resource for UAV Swarm Information Transmission

For the problems brought by malicious interference in the unmanned aerial vehicle (UAV) swarm network, we establish a cluster-based UAV swarm information transmission model. We mainly consider four aspects: cluster head selection, channel allocation, power allocation and UAV position. In order to improve the backhaul information rate of UAV swarm, we propose a joint optimization method of spectrum resource with the goal of maximizing the sum throughput of the cluster head UAV. We decompose the original mixed integer nonlinear programming (MINLP) problem into multiple sub-problems based on the block coordinate descent (BCD) technique, and then solve them based on convex optimization and successive convex approximation (SCA) technique. Simulation results show that the proposed method can obtain the spectrum resource optimization strategy of UAV swarm information transmission, reduce the impact of malicious interference, and effectively improve the backhaul information rate of UAV swarm.

Self-Interference Cancelation-Based Workload-Driven Duplex-Model Selection in Machine-Type Communication Networks

The machine-type communication (MTC) enables a broad range of applications from mission-critical services to massive deployment of autonomous devices in the Internet of Things (IoT) networks. To release more spectrum resources for facilitating the explosive traffic of MTC in ultradense Cellular-IoT (CIoT) networks, full-duplex (FD) technology has been considered as a candidate mechanic due to its respective advantages in terms of the spectral efficiency (SE). Compared with the traditional half-duplex (HD) technology, the FD technology can (in theory) attain twice the SE gain. Nevertheless, the performance of FD technology is severely limited by both the self-interference (SI) and the mutual interference (MI) in the presence of multiple users. What is more serious is that when FD technology is used in a multiuser communication system, the system performance is very sensitive to the service load of the entire network, and it may even appear fragile in an ultrahigh load environment. In this article, we will delve into investigating the performance of the FD technique in the CIoT networks by considering the impacts of a variety of aspects, including the SI cancelation capability (SICC) of the FD-mode devices, the network’s workload, as well as the devices’ distribution density (DDD), the purpose of which is to maximize both the SE and the sum throughput (ST) of the network by optimizing those critical parameters. It is shown that the FD mode is capable of improving the ST of the CIoT networks in either the low-traffic-volume or low-device-density regime, provided that the devices’ SICC could be up to 100dB. At the same time, research results show that further increasing both the devices’ density and the traffic load, even without compromising the FD devices’ SICC, will still help improve the performance superiority of FD technology. Numerical results show that by implementing an appropriate workload-driven mode-selection scheme, we can sufficiently exploit the FD/HD gain according to the instantaneous radio frequency environment.

Cooperative Multi-Agent Reinforcement-Learning-Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks

With the development of wireless communication and Internet of Things (IoT), there are massive wireless devices that need to share the limited spectrum resources. Dynamic spectrum access (DSA) is a promising paradigm to remedy the problem of inefficient spectrum utilization brought upon by the historical command-and-control approach to spectrum allocation. In this article, we investigate the distributed DSA problem for multiusers in a typical multichannel cognitive radio network. The problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP), and we propose a centralized off-line training and distributed online execution framework based on cooperative multi-agent reinforcement learning (MARL). We employ the deep recurrent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (DRQN) to address the partial observability of the state for each cognitive user. The ultimate goal is to learn a cooperative strategy which maximizes the sum throughput of a cognitive radio network in a distributed fashion without information exchange between cognitive users. Finally, we validate the proposed algorithm in various settings through extensive experiments. The experimental results show that the proposed CoMARL-DSA algorithm outperforms the state-of-the-art deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning for spectrum access (DQSA) in terms of successful access rate and collision rate by at least 14% and 12%, respectively.

Wireless-Powered Intelligent Radio Environment With Nonlinear Energy Harvesting

This article investigates a wireless-powered intelligent radio environment, where a fractional nonlinear energy harvesting (NLEH) is proposed to enable an intelligent reflecting surface (IRS)-assisted wireless-powered Internet of Things (WP IoT) network. The IRS engages in downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT). We aim to improve the overall performance of the considered network, and the approach is to maximize its sum throughput subject to constraints of two different types of IRS beam patterns and time durations. To solve the formulated problem, we first consider the Lagrange dual method and Karush–Kuhn–Tucker (KKT) conditions to optimally design the time durations in closed form. Then, a quadratic transformation (QT) is proposed to iteratively transform the fractional NLEH model into the subtractive form, where the IRS phase shifts are optimally derived by the complex circle manifold (CCM) method in each iteration. Finally, numerical results are demonstrated to promote the proposed scheme in comparison to the benchmark schemes, where the benefits are induced by the IRS compared with the benchmark schemes.