Multiple Access Protocol Research Articles

Multiple Access in Large-Scale LoRaWAN: Challenges, Solutions, and Future Perspectives

Enabling energy-efficient and long-distance wireless networking is expected by various Internet-of-Things systems and consumer electronics applications. Low Power Wide Area Networks can commendably satisfy this requirement because of their combination of low-power and long-range features. Thereinto, open-standard Long Range Wide Area Network (LoRaWAN) is the representative player with hundreds of deployment cases worldwide. Unfortunately, current multiple access (MA) strategy for LoRaWAN has been proved to incur severe wireless signal interference under large-scale deployment. This is the inherent hindrance for current LoRaWAN to support large-scale wireless networking. In this context, this article aims to provide a detailed tutorial of MA issues in large-scale LoRaWAN for the first time. We start with several practical use cases of large-scale LoRaWAN. We then explain the detailed challenges of large-scale LoRaWAN networking. Representative MA solutions for better LoRaWAN networking are then described and compared. Future research directions for enhanced MA protocol design are also discussed.

Load-adaptive MAC protocol for frontier detection in Underwater Mobile Sensor Network

This work proposes a load-adaptive Medium Access Control (MAC) protocol for the frontier/boundary detection application of underwater phenomena using Underwater Mobile Sensor Network (UWMSN). A leader-follower architecture of a swarm of underwater vehicles is proposed here. Autonomous Underwater Vehicles (AUVs) traverse a random mobility pattern beneath one Autonomous Surface Vehicle (ASV) (leader) in the proposed network. ASV has to guide multiple-follower AUVs in the event of interest. The vehicular swarm aims to explore the frontiers in the event to build the map. Load-adaptive MAC protocol is therefore proposed and implemented in this hybrid multi-vehicular network to ensure seamless vehicular communications. The ASV has navigational capabilities to aid the AUVs in navigation and data collection. The proposed MAC protocol can adjust the dynamic mobility and load in the network. The protocol aims to provide dynamic Time Division Multiple Access (TDMA) slots for the AUVs wirelessly linked in the vicinity of the ASV. These slots are used for ranging/navigation and data transmission. Additional urgent data from any AUVs can be transmitted in open Carrier Sense Multiple Access (CSMA) protocol following the TDMA duration. Results have been generated by comparing protocols like CSMA, ALOHA, and TDMA with the proposed Load-Adaptive MAC protocol. The protocols have been compared to the throughput vs number of nodes and throughput vs simulation time. It has been observed that the proposed MAC can perform better than ALOHA and CSMA protocols. Nevertheless, it can produce comparable results for TDMA protocol while supporting the dynamic mobility and load in the network meantime supporting urgent data transmission for nodes in demand.

Resource Allocation for UAV-RIS-Assisted NOMA-Based URLLC Systems

This work focuses on maximizing the sum rate of ultra-reliable low-latency communication (URLLC) systems by leveraging unmanned aerial vehicle-mounted reconfigurable intelligent surface (UAV-RIS) to provide short packet services for users based on the non-orthogonal multiple access (NOMA) protocol. To optimize the sum rate of system, a joint optimization is performed with respect to the power allocation, UAV position, decoding order, and RIS phase shifts. As the original problem is a non-convex integer optimization problem, it is challenging to obtain the optimal solution. Therefore, approximate solutions are derived using successive convex approximation (SCA), slack variables, and penalty-based methods. The simulation results demonstrate the superiority of the proposed resource allocation algorithm compared with the benchmark algorithm with orthogonal multiple access (OMA) scheme. In addition, this work emphasizes the performance gap between the proposed communication system and the traditional Shannon communication system in terms of throughput and the performance capacity sacrificed to achieve lower latency.

Efficient resource allocation for IoT applications in mobile edge computing via dynamic request scheduling optimization

This study investigates the challenges of task scheduling and resource allocation in an ultra-dense edge cloud (UDEC) network incorporating micro (macro) base stations and different equipment users (UE) under 5G technology. By utilizing multiple access interference (MAI), we enable multiple access capabilities to the UEs under the Non-Orthogonal Multiple Access (NOMA) protocol. To accommodate the dynamic nature of user tasks, we propose a two-level scheduling framework. At the upper level, the non-convex, non-linear power allocation problem for mobile users is formulated and solved using the Inertia Weight Particle Swarm Optimization (IW-PSO) algorithm to minimize the transmission energy consumption. Simultaneously, at the lower level, the joint task offloading and resource allocation problem is formulated as MINP and solved with the binary Particle Swarm Optimization (PSO) algorithm to find an optimal schedule that maximizes the response rate and the overall welfare of the system while minimizing the energy consumption. In this study, various sensitivity analyses are conducted for different parameters, including the number of mobile users, channel power, and request profile parameters, including workload and request size. Comparative analyses with other methods from recent literature consistently reveal the comparable performance of the proposed framework and confirm its effectiveness in addressing the challenges of dynamic task scheduling.

FPGA-based point-controlled OFDM transmission system design

Abstract We proposed a low-overhead point-controlled multiple-access protocol. This protocol establishes a new frame structure and transmission rules, which avoids transmission conflict and reduces transmission overhead. The protocol is integrated into an OFDM communication system based on FPGA. The experiment shows that the scheme is feasible and reliable and effectively improves the transmission efficiency and level.

Communication-aware formation control for networks of AUVs

We propose a distributed formation control algorithm augmented with communication awareness. We consider autonomous underwater vehicles (AUVs) that are able to communicate over an acoustic link using a Time Division Multiple Access (TDMA) protocol, and to measure the Signal-to-Noise Ratio (SNR) of incoming messages. Based on the measured SNR and packet loss, we endow them with a distributed formation control scheme that accounts for the time-varying nature of the acoustic communication channel. This scheme allows a network of N AUVs to follow a pre-determined, twice-differentiable path while adapting their formation. The size of the formation is dynamically scaled by a formation adaptation mechanism to stabilize the estimated packet loss probability at a desired level. A distributed packet loss estimator is then built on top of the same average consensus routines used by the formation control algorithm, and thus comes with a minimal communication overhead. We test the algorithm by means of high-fidelity simulators, and verify its efficacy in making the network of agents retain formation-wide communication capabilities in a range of cases.

On the outage performance analysis of α − κ − µ fading channels with non-orthogonal multiple access protocol

In this paper, we consider effective performance transmission under generalized α − κ − µ the fading distribution. The source-user links are assumed to be non-orthogonal multiple access (NOMA) channels through a downlink power do-main. Two users are selected to service in a situation with perfect channel state information (CSI) in accordance with the NOMA protocol. The closed-form ex-pressions of outage probability (OP) and bit error rate (BER) are derived with the effect of power allocation coefficient, target rate, and channel fading param-eters. In addition, we calculate numerical results to demonstrate the asymptotic expansion in the high signal-to-noise ratio (SNR) analysis. Finally, Monte Carlo simulations are provided to validate and assess the accuracy of the analytical framework proposed.

Low-Latency Wireless Network Extension for Industrial Internet of Things.

The timely delivery of critical messages in real-time environments is an increasing requirement for industrial Internet of Things (IIoT) networks. Similar to wired time-sensitive networking (TSN) techniques, which bifurcate traffic flows based on priority, the proposed wireless method aims to ensure that critical traffic arrives rapidly across multiple hops to enable numerous IIoT use cases. IIoT architectures are migrating toward wirelessly connected edges, creating a desire to extend TSN-like functionality to a wireless format. Existing protocols possess inherent challenges to achieving this prioritized low-latency communication, ranging from rigidly scheduled time division transmissions, scalability/jitter of carrier-sense multiple access (CSMA) protocols, and encryption-induced latency. This paper presents a hardware-validated low-latency technique built upon receiver-assigned code division multiple access (RA-CDMA) techniques to implement a secure wireless TSN-like extension suitable for the IIoT. Results from our hardware prototype, constructed on the IntelFPGA Arria 10 platform, show that (sub-)millisecond single-hop latencies can be achieved for each of the available message types, ranging from 12 bits up to 224 bits of payload. By achieving one-way transmission of under 1 ms, a reliable wireless TSN extension with comparable timelines to 802.1Q and/or 5G is achievable and proven in concept through our hardware prototype.

Adaptive channel reservation multiple access protocol based on differentiated traffic guarantee

Ad Hoc network is widely used because of its distributed flexibility, in which nodes may generate different types of traffic, and some high-priority traffic requires lower delay and other Quality of service (QoS) requirements. However, as the load of traffic increases, the probability of conflict increases significantly, which makes it difficult to guarantee the requirement of these traffic. It is significant to design the efficient multiple access protocol to ensure the quality of different kinds of traffic. Channel reservation mechanism is an important method to ensure QoS. However, the existing channel reservation mechanism does not fine-adjust reservation parameters according to the QoS when dealing with various traffic differently, and the differentiation mechanism is rough. In addition, it is difficult to ensure the performance of common services when low-latency services are met. Therefore, a differentiated service guarantee adaptive reservation mechanism (DSGARM) to solve the above problems is proposed. The core idea of the protocol is to provide differentiated services in detail according to the delay tolerance requirements of each business. Absolute reservation mechanism is used to guarantee delay-sensitive services, and relative reservation mechanism is used to service relatively delay-insensitive services. In the protocol, a model is established to design an adaptive algorithm to quantitatively calculate reservation parameters that meet the delay requirements. On the premise of prioritized reservation for various services, appropriate reservation parameters can be adjusted according to network conditions to ensure the delay tolerance requirements of each type of service. The present work can be widely applied to scenarios with differentiated business requirements. The simulation results show the advantages of the present protocol and have great practical significance.

Ефективність адаптивних протоколів множинного доступу пакетних радіомереж в умовах самоподібного трафіку

Introduction. Self-similar traffic in modern telecommunication radio networks requires new methods for calculating parameters of adaptive Carrier Sense Multiple Access (CSMA) protocols, different from those adopted in classical teletraffic theory based on Poisson distribution models. Purpose. Construction of a mathematical model of the operation of adaptive synchronous CSMA protocols in conditions of self-generating traffic for three strategies for changing the length of data packets, and obtaining equations for the average transmission rate and comparing the effectiveness of adaptive control with the results of studies of the same protocols for traffic with a Poisson distribution. Methods. This goal is achieved by creating and analyzing a mathematical model of the operation of the flexible and rigid synchronous adaptive CSMA protocol for three strategies for changing the length of data packets under traffic conditions with a Pareto distribution. The model is described by the average protocol transmission rate equations, which are a function of traffic intensity, Pareto distribution parameters, and strategies for changing the length of data packets. Results. It has been proven that self-similar traffic significantly reduces the stability limit of adaptive CSMA protocols, which must be taken into account when using these protocols in radio networks with long-term dependence in traffic distribution. At the same time, the throughput of adaptive CSMA protocols remains virtually unchanged compared to the traffic model with a Poisson distribution. Conclusions. The proposed mathematical model of adaptive CSMA protocols allows to calculate the real values of the average speed, stability limit and throughput of these protocols under conditions of self-similar traffic for the proposed strategies for changing the length of data packets. The considered strategies (except for the second) are effective for adaptive control in conditions of self-generating traffic, but at a significantly lower value of traffic intensity compared to the Poisson distribution.

Joint Optimization of Resource Allocation, Phase Shift, and UAV Trajectory for Energy-Efficient RIS-Assisted UAV-Enabled MEC Systems

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) has been deemed a promising paradigm to provide ubiquitous communication and computing services for the Internet of Things (IoT). Besides, by intelligently reflecting the received signals, the reconfigurable intelligent surface (RIS) can significantly improve the propagation environment and further enhance the service quality of the UAV-enabled MEC. Motivated by this vision, in this paper, we consider both the amount of completed task bits and the energy consumption to maximize the energy efficiency of the RIS-assisted UAV-enabled MEC systems with non-orthogonal multiple access (NOMA) protocol, where the bit allocation, transmit power, phase shift, and UAV trajectory are jointly optimized by an iterative algorithm with a double-loop structure based on the Dinkelbach’s method and block coordinate decent (BCD) technique. Simulation results demonstrate that: 1) our proposed algorithm can achieve higher energy efficiency than baseline schemes while satisfying the task tolerance latency; 2) the energy efficiency first increases and then decreases with the increase of the mission period and the total amount of task-input bits of IoT devices; 3) the energy efficiencies of schemes with imperfect channel state information (CSI) are lower than corresponding schemes with perfect CSI, and the performance gain of NOMA over OMA diminishes under the imperfect CSI.

Multiple Access for Heterogeneous Wireless Networks with Imperfect Channels Based on Deep Reinforcement Learning

In heterogeneous wireless networks, when multiple nodes need to share the same wireless channel, they face the issue of multiple access, which necessitates a Medium Access Control (MAC) protocol to coordinate the data transmission of multiple nodes on the shared communication channel. This paper presents Proximal Policy Optimization-based Multiple Access (PPOMA), a novel multiple access protocol for heterogeneous wireless networks based on the Proximal Policy Optimization (PPO) algorithm from deep reinforcement learning (DRL). Specifically, we explore a network scenario where multiple nodes employ different MAC protocols to access an Access Point (AP). The novel PPOMA approach, leveraging deep reinforcement learning, adapts dynamically to coexist with other nodes. Without prior knowledge, it learns an optimal channel access strategy, aiming to maximize overall network throughput. We conduct simulation analyses using PPOMA in two scenarios: perfect channel and imperfect channel. Experimental results demonstrate that our proposed PPOMA continuously learns and refines its channel access strategy, achieving an optimal performance level in both perfect and imperfect channel scenarios. Even when faced with suboptimal channel conditions, PPOMA outperforms alternative methods by achieving higher overall network throughput and faster convergence rates. In a perfect channel scenario, PPOMA’s advantage over other algorithms is primarily evident in its convergence speed, reaching convergence on average 500 iterations faster. In an imperfect channel scenario, PPOMA’s advantage is mainly reflected in its higher overall network throughput, with an approximate increase of 0.04.

Analysis of UAV Radar and Communication Network Coexistence With Different Multiple Access Protocols

Analysis of UAV Radar and Communication Network Coexistence With Different Multiple Access Protocols

Novel multiple access protocols against Q-learning-based tunnel monitoring using flying ad hoc networks

Novel multiple access protocols against Q-learning-based tunnel monitoring using flying ad hoc networks

Digital twin-assisted resource allocation framework based on edge collaboration for vehicular edge computing

Vehicular Edge Computing (VEC) supports latency-sensitive and computation-intensive vehicular applications by providing caching and computing services in vehicle proximity. This reduces congestion and transmission latency. However, VEC faces implementation challenges due to high vehicle mobility and unpredictable network dynamics. These challenges pose difficulties to network resource allocation. Most existing VEC network resource management solutions consider edge–cloud collaboration and ignore collaborative computing between edge nodes. A reasonable collaboration between Roadside Units (RSUs) or small cells eNodeB can improve VEC network performance. Our proposed framework aims to improve VEC network performance by integrating Digital Twin (DT) technology which creates virtual replicas of network nodes to estimate, predict, and evaluate their real-time conditions. A DT is constructed centrally to maintain and simulate VEC network, thus enabling edge nodes collaboration and real-time resources information availability. We employ channel state information (CSI) for RSUs selection, and vehicles communicate with RSUs through a non-orthogonal multiple access (NOMA) protocol. We aim to maximize the VEC system computation rate and minimize task completion delay by jointly optimizing offloading decisions, subchannel allocation, and RSU association. In view of the resulting optimization problem complexity (NP-hard), we model it as a Markov Decision Process (MDP) and apply Advantage Actor–Critic (A2C) algorithm to solve it. Validated via simulations, our scheme shows superiority to the benchmarks in reducing task completion delay and improving VEC system computation rates.

Deep Reinforcement Learning and NOMA-Based Multi-Objective RIS-Assisted IS-UAV-TNs: Trajectory Optimization and Beamforming Design

In this paper, we discuss the co-optimized performance of multi-reconfigurable intelligent surface (RIS)-assisted integrated satellite-unmanned aerial vehicle-terrestrial network (IS-UAV-TN), where the multiple vehicle users are applied to the network under consideration. The performance optimization of IS-UAV-TNs faces two major challenges: one is the obstacles in the transmission path and the other is the highly dynamic communication environment caused by the UAV movement for the multiple ground vehicle users. To tackle these above issues efficiently, we will install RIS on the UAV for the purpose of reshaping the wireless transmission path. In addition, non-orthogonal multiple access (NOMA) protocols are considered as a new paradigm to address spectrum shortage and enhance connection quality. Considering the UAV energy consumption, the satellite transmission beamforming matrix and RIS phase shift configuration, a multi-objective optimization problem is proposed to maximize the system achievable rate and minimize the UAV energy consumption during a specific mission. On this foundation, to facilitate the online decision problem, the deep reinforcement learning (DRL) algorithm is utilized to achieve real-time interaction with the communication environment. A multi-objective deep deterministic policy gradient (MO-DDPG) algorithm is proposed to search for sub-optimal solutions about the learning problem of multi-objective control policies in IS-UAV-TNs. Experimental results show that the method can simultaneously consider three optimization objectives and effectively adjust the optimal update policy according to the settings of different weight parameters.

A novel distributed multi-slot TDMA-based MAC protocol for LED-based UOWC networks

Underwater optical wireless communication (UOWC) networks are promising for many civilian and industrial applications due to underwater high-speed data transmission demand. This paper focuses on the distributed time division multiple access (TDMA) protocol design for UOWC networks. Unlike existing research, we focus on a more general underwater communication scenario where nodes are scattered, and node mobility is considered. We propose a distributed TDMA-based medium access control(MAC) protocol, called cluster-based cross-layer multi-slot MAC(CCM-MAC), which can assign multiple slots to each node according to the slot-occupying information. When using CCM-MAC, the network is divided into clusters. Each node maintains knowledge of the cluster topology and slot-occupying information of surrounding nodes through routing techniques; by updating this knowledge, collisions can also be detected and eliminated dynamically. Analysis results are presented to evaluate the performance of CCM-MAC, and simulations show that high network throughput and low collision rate can be realized in comparison with existing contention-based MAC protocols.

UAV-Assisted Secure Communication for Coordinated Satellite-Terrestrial Networks

This letter investigates an unmanned aerial vehicle (UAV)-assisted satellite-terrestrial communication network. Specifically, a three-dimensional mobile UAV helps to enhance coverage and simultaneously enable secure communication between the satellite and ground users with the internal eavesdropper. Moreover, non-orthogonal multiple access protocol is adopted, such that the spectral efficiency can be further improved. Aiming to maximize the secrecy rate, a joint cooperative jamming, transmit power, and UAV trajectory optimization problem is proposed. To deal with its non-convexity, we propose an iterative optimization algorithm that first optimally addresses the jamming and transmit power optimization in close forms and then uses an iterative relaxation technique to design the UAV trajectory for the given power. Simulation results highlight the critical role of the mobile UAV and significantly confirm the effectiveness of the proposed algorithm over the benchmark schemes.

Timeliness of wireless sensor networks with random multiple access

In this paper, we consider a wireless sensor network in which N sensor nodes deliver the observed information of interest to a remote receiver by competing for a sharing channel through the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol or the slotted ALOHA protocol. For this network, we evaluate the information freshness of the two random multiple access protocols using the age of information (AoI) metric. In order to explicitly express the average AoI of the CSMA/CA based network, we establish an equivalent and tractable transmission model for the network, in which the transmission probabilities and the collision probabilities are assumed to be identical over time and among sensor nodes. For the slotted ALOHA based network, we derive the average AoI by focusing on a randomly chosen reference node. Our theoretical results show that 1) the transmission probability and collision probability of the two networks increase with both the arrival rate and the number of sensor nodes; 2) with the same transmission probability, the average AoI of the CSMA/CA based network is always smaller than that of the slotted ALOHA based network, no matter how the arrival rate and the number of nodes change. Our Monte Carlo simulation results also validate the correctness of our theoretical calculations.

Random Multi-access Scheme Based on Group Testing Theory for Massive Machine-type Communication

Considering emerging massive machine-type communication with very few devices simultaneously transmitting short messages, random multiple access has currently become a research hot spot since the traditional multiple access protocol struggles to work properly. Inspired by the superimposed code, a channel assignment scheme based on non-adaptive group testing theory is proposed to allow multiple users to share a single channel at the same time. A superimposed code with threshold value d, which can recover the original code through the Boolean sum of up to d codes, is constructed by studying the problem of group testing. When the number of active users with a message to be transmitted is no more than the threshold value, active users encode their messages by the superimposed code and multiple active users transmit their messages together by sharing a single channel. The receiver can decode the mixed messages by the disjunction of the superimposed code. We focus on the design of the superimposed code by group testing theory, formulate this problem as the construction of a transversal design, and use the Latin square to form a useful transversal design. Theoretical proof shows that our design works well under limited circumstances where the number of active users does not exceed the threshold value. Additionally, our design can recover the original codes by the mixture of codes, which are more easily than the algorithms based on compressive sensing. Simulation analysis presents that our design performs better than the traditional competitive access protocols in terms of successful probability, throughput, and packet loss rate.