Channel Access Scheme Research Articles

Hybrid vehicular access protocol and message prioritization for real-time safety messaging

Real-time safety services rely on the exchange of messages to enhance the operations of connected and automated vehicles (CAVs). These safety messages convey vital information about traffic conditions, enabling drivers to take necessary measures to prevent accidents. The timely and reliable delivery of these messages is essential, necessitating efficient channel access. Vehicular Deterministic Access (VDA) is employed as a channel access scheme with distinct priorities and stringent timing guidelines, particularly for urgent safety warnings. In this paper, we propose a hybrid approach that combines VDA and Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocols, along with a message prioritization algorithm, to ensure efficient and reliable communication of safety messages in vehicular networks. Our approach leverages the strengths of both VDA and CSMA/CA to avoid message collisions; VDA is more efficient under high traffic loads, while CSMA/CA is better suited for low traffic loads. Additionally, the incorporation of the message prioritization algorithm ensures strict deadline guarantees for high-priority messages, such as Decentralized Environmental Notification Messages (DENMs). We evaluate our proposed solution using the Artery simulation framework. Our results show over a 93% delivery rate for DENM exchanges while maintaining low collision probability across various traffic loads. This research provides practical guidance for the development of efficient and reliable communication systems for CAVs. It also offers a detailed analysis of the trade-offs among different access protocols and message prioritization strategies in vehicular networks.

Opponent-Awareness-Based Anti-Intelligent Jamming Channel Access Scheme: A Deep Reinforcement Learning Perspective

Opponent-Awareness-Based Anti-Intelligent Jamming Channel Access Scheme: A Deep Reinforcement Learning Perspective

Exploiting Successive Interference Cancellation for Spectrum Sharing Over Unlicensed Bands

Harmonious coexistence among different unlicensed wireless technologies has became increasingly important due to the spectrum shortage problem. As a representative case, we focus on addressing LTE-LAA and WiFi coexistence in unlicensed bands. Traditionally, collision avoidance-based medium access control (MAC) protocols adopted by both LAA and WiFi have led to low channel utilization and fairness. In this paper, we explore physical-layer interference suppression techniques (e.g., successive interference cancellation, SIC) to enhance the spectrum utilization of coexisting LAA and WiFi networks. We propose a SIC-aware MAC protocol that embraces concurrent transmissions and optimizes the channel access strategy at the MAC layer, so as to mitigate the interference (same-technology or cross-technology) due to excess channel contentions. We theoretically analyze the network throughput by extending Bianchi's Markov model, considering the impact of concurrent transmissions and SIC. We also extend the analysis to consider MIMO and MU-MIMO links with SIC. We validate our theoretical analysis and the effectiveness of the proposed MAC protocol via extensive simulations. We also implement a prototype LAA/WiFi SIC receiver on USRP devices to demonstrate the feasibility of cross-technology SIC and the proposed MAC protocol.

WiFi 7 with Different Multi-Link Channel Access Schemes: Modeling, Fairness and Optimization

WiFi 7 with Different Multi-Link Channel Access Schemes: Modeling, Fairness and Optimization

Low-cost and high-performance channel access strategies for Internet of Nano-Things applications

Nanodevices, which are only a few nanometers (nm) in size, are interconnected to form the Internet of Nano-Things (IoNT) that performs complex operations. One of the key challenges is ensuring efficient channel access control for nanodevices, especially when dealing with large network sizes. Medium access control (MAC) protocols serve this purpose, but traditional approaches are not practical due to the inherent constraints of nanodevices. In this paper, we propose two novel MAC protocols for IoNT applications. The first protocol, Slot Assignment-Based (SAB) MAC, is a contention-free method relying on scheduling. In contrast to its counterparts, it enables simultaneous packet transmission through Time Spread On-Off Keying (TS-OOK), effectively minimizing the collision probability and end-to-end delay. The second protocol, Receiver-Initiated and Directed (RID) MAC, adopts a contention-based approach to reduce unnecessary transmissions caused by flooding. It achieves this by limiting the number of active nanodevices within a time interval using directional antennas without incurring scheduling overhead. We evaluated the performance of these protocols through comprehensive simulations, comparing them with counterparts in terms of packet transmission success, energy consumption, end-to-end delay and setup overhead. In dense topologies, SAB-MAC outperforms Transparent (TRN) MAC by approximately twice the packet transmission success reaching up to 95.73%. It accomplishes this with 1000 times lower end-to-end delay and reduced setup overhead than Time Division Multiple Access (TDMA). Conversely, RID-MAC achieves twice the packet transmission success of TRN-MAC and ten times that of unicast-based methods, all with lower end-to-end delay and nearly equivalent energy consumption. Consequently, due to its superior performance SAB-MAC is the optimal choice for communication between nanorouters (NRs). However, RID-MAC is more suitable for communication between nanosensors (NSs), as it incurs no setup overhead.

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.

Reinforcement learning based multi-parameter joint optimization in dense multi-hop wireless networks

Carrier Sense Multiple Access with Collision Avoid (CSMA/CA) restricts the channel utilization efficiency although it always is regarded as a promising distributed channel access scheme, especially in dynamic dense wireless network scenarios. This paper investigates a Reinforcement Learning (RL) based multi-parameter joint optimization mechanism to build suitable access configuration for dense wireless multi-hop networks. Specifically, we propose a light-weight CSMA/CA mechanism based on Q-Learning (QL), namely QL-CSMA/CA, which jointly optimizes multiple channel access parameters, including Contention Window (CW), Maximum Backoff Exponent (MaxBE), and Maximum CSMA Backoffs (MaxCSMABackoffs), by setting mixed rewards and thus leading to higher network performance. Due to the limitation of the QL-CSMA/CA in large-scale state-space scenarios, we further present an enhanced CSMA/CA mechanism based on Deep Q-Network (DQN), namely DQN-CSMA/CA. Despite introducing a little complexity, the DQN-CSMA/CA can significantly improve transmission performance by utilizing more comprehensive network state information in complex dynamic network scenarios. Simulation results demonstrate the effectiveness of the proposed algorithms in terms of throughput, packet loss rate, delay, and collision rate, especially in high-density scenarios. Specifically, compared to SCAMA and traditional CSMA/CA algorithms, the QL-CSMA/CA algorithm achieves 9% and 5% performance improvements in terms of throughput, while DQN-CSMA/CA algorithm achieves 14% and 10% improvements in terms of throughput, respectively.

Data Freshness Optimization Under CAA in the UAV-Aided MECN: A Potential Game Perspective

As a promising enabler for edge intelligence, Unmanned Aerial Vehicles (UAV) have become more and more important in Mobile Edge Computing Networks (MECN), such as communication, computation, collection and control service supply. Although Age of Information (AoI) minimization is indispensable for fresh information collection and computation in the UAV-aided MECN, some attackers can launch attacks to deteriorate the availability of precious channel resources, such as revealed channel access attacks (CAAs). Moreover, recent research has not considered the system's active probability and security issues concurrently, e.g., CAA, in the average AoI minimization process. In this paper, to deal with this problem, we consider an AoI-oriented channel access problem under CAA with a game theory viewpoint. Firstly, to obtain a MECN-based AoI indicator under CAA, the system model with active probability consideration is established. Next, the channel access-based AoI minimization problem is formulated from the viewpoint of the Ordinary Potential Game (OPG). Furthermore, two algorithms called AACSD and DCASD are proposed to determine channel access strategies, by which the Nash Equilibrium (NE) solution of the OPG could be reached. Finally, experiments are conducted under homogeneous and heterogeneous parameter settings, and the simulation results evaluate the correctness and effectiveness of our proposals.

Dynamic multiple access based on deep reinforcement learning for Internet of Things

With the development of wireless communication technology, the rational use of spectrum resources utilizing efficient multiple access technology has become a research hotspot. The channel access strategy in dynamic spectrum access technology will directly affect the system’s spectrum utilization. Deep reinforcement learning is proposed to train users in the network to learn optimal channel access strategy to achieve reasonable allocation of spectrum resources on the channel. At the same time, the multiple access scheme adopted by users who have access to the channel will also affect the throughput of the system. Traditional multiple access schemes include Orthogonal Multiple Access scheme and Non-orthogonal Multiple Access (NOMA) scheme. In a dynamic environment, the multiple access scheme suitable for users on the channel is also dynamic. Therefore, DRL is introduced to help users learn the multiple access scheme selection strategy to maximize user throughput. Finally, we propose a dynamic multiple access algorithm based on deep reinforcement learning, which optimizes the channel access strategy and scheme selection strategy respectively, and improves the performance. The simulation results show that the proposed scheme is superior to other schemes in various scenarios, especially, the average throughput of the proposed scheme is about 0.8 Mbps and 0.5 Mbps higher than that of the FDMA scheme and NOMA scheme, respectively.

Metaheuristic for Optimal Dynamic K-Coloring Application on Band Sharing for Automotive Radars

The number of vehicles equipped with radars on the road has been increasing for years and is expected to reach 50% of cars by 2030. This rapid rise in radars will likely increase the risk of harmful interference, especially since radar specifications from standardization bodies (e.g., ETSI) provide requirements in terms of maximum transmit power but do no mandate specific radar waveform parameters nor channel access scheme policies. Techniques for interference mitigation are thus becoming very important to ensure the long-term correct operation of radars and upper-layer ADAS systems that depend on them in this complex environment. In our previous work, we have shown that organizing the radar band into time-frequency resources that do not interfere with each other vastly reduces the amount of interference by facilitating band sharing. In this paper, a metaheuristic is presented to find the optimal resource sharing between radars, knowing their relative positions and thereby the line-of-sight and non-line-of-sight interference risks during a realistic scenario. The metaheuristic aims at optimally minimizing interference while minimizing the number of resource changes that radars have to make. It is a centralized approach where everything about the system is known (e.g., the past and future positions of the vehicles). This and the high computational load induce that this algorithm is not meant to be used in real-time. However, the metaheuristic approach can be extremely useful for finding near optimal solutions in simulations, allowing for the extraction of efficient patterns, or as data generation for machine learning.

Thompson Sampling-Based Dynamic Spectrum Access in Non-Stationary Environments

In dynamic spectrum access (DSA), unlicensed secondary users (SUs) estimate the idle probabilities of primary channels by using historical sensing results and opportunistically access the channel with the highest idle probability for transmission. Due to the rapid traffic changes and irregular user mobility, the primary channels can be highly dynamic and their idle probabilities are generally time-varying. In this paper, we investigate DSA in non-stationary environments. Specifically, we consider two channel state models, i.e., the non-stationary Bernoulli model with a time-varying mean and the non-stationary Markovian model with a time-varying transition matrix. For the single-SU scenario, we propose a Thompson sampling based method with a change detection technique, which is capable of detecting the variations of channel statistics and adjusting the channel access strategy accordingly. For the multi-SU scenario, we propose a Thompson sampling based collision alleviation method to coordinate the transmissions of SUs, which does not need any prior protocol or information exchange among SUs. Numerical results show that the proposed methods outperform the existing ones in terms of successful transmission ratio in various network settings.

Optimal Secure Channel Access in Distributed Cooperative Networks With Untrusted Relay

This paper investigates optimal channel access strategies to improve secure communications over a distributed cooperative wireless network with multiple user pairs and an untrusted amplify-and-forward (AF) relay. The user pairs communicate directly or via the relay. To enable secure communication, we formulate a channel access problem to maximize the average secrecy system throughput with destination-based jamming. Using optimal sequential planned decision theory, we propose an optimal channel access strategy where the winning user pair uses direct transmission, cooperative AF relaying, or cooperative jamming in an adaptive manner. We rigorously prove the optimality of the solution and provide a distributed channel access algorithm based on the pure threshold structure, which is easy to implement. Numerical results demonstrate effectiveness and efficiency of the proposed strategy compared to alternatives.

Cooperative Body Channel Communications for Energy-Efficient Internet of Bodies

The Internet of Bodies (IoB) is a network formed by wearable, implantable, ingestible, and injectable smart devices to collect physiological, behavioral, and structural information from the human body. Thus, the IoB technology can revolutionize the quality of human life by using these context-rich data in myriad smart-health applications. Radio frequency (RF) transceivers have been typically preferred due to their availability and maturity. However, for most RF standards (e.g., Bluetooth low energy), the highly radiative omnidirectional RF propagation (even at the lowest settings) reaches tens of meters of coverage, thereby reducing energy efficiency, causing interference and co-existence issues, and raising privacy and security concerns. On the other hand, body channel communication (BCC) confines low-power and low-frequency (10 kHz–100 MHz) signals to the human body, leading to more secure and efficient communications. Since energy efficiency is one of the critical design parameters of IoB networks, this article focuses on energy-efficient orthogonal body channel access (OBA) and non-OBA (NOBA) schemes with and without cooperation. To this aim, three main BCC topologies are presented: 1) point-to-point channel; 2) medium access channel; and 3) broadcast channel. These topologies are then used as building blocks to create IoB networks relying on OBA and NOBA schemes for downlink (DL) and uplink (UL) traffic. For all schemes and traffic directions, optimal transmit power and phase time allocations are derived in closed-form, which is essential to reduce energy consumption by eliminating computational power. The closed-form expressions are further leveraged to obtain maximum network size as a function of data rate requirement, bandwidth, and hardware parameters.

TXOP Tuning-based Channel Access Scheme for Performance Enhancement of IEEE 802.11ah Multi-rate NOMA-IoT Networks

TXOP Tuning-based Channel Access Scheme for Performance Enhancement of IEEE 802.11ah Multi-rate NOMA-IoT Networks

Power-Optimized NOMA With TXOP Tuning-Based Channel Access Scheme for IEEE 802.11ah Dense IoT Networks

In this letter, we propose a power-optimized NOMA with TXOP tuning based novel channel access scheme for IEEE 802.11ah dense IoT networks. Using the proposed method, we form the NOMA-clusters to mitigate the number of contending nodes in a group. Further, the proposed scheme enables nodes in a NOMA-cluster to transmit simultaneously using power-domain NOMA, as well as multiple frames based on the TXOP limit in single resource block. With an analytical model, we evaluate the throughput and connectivity performance. From the analytical and simulation results, it is evident that the proposed scheme significantly improve the performance of NOMA-IoT networks.

Distributed Channel Access for Control Over Unknown Memoryless Communication Channels

We consider the distributed channel access problem for a system consisting of multiple control subsystems that close their loop over a shared wireless network. We propose a distributed method for providing deterministic channel access without requiring explicit information exchange between the subsystems. This is achieved by utilizing timers for prioritizing channel access with respect to a local cost which we derive by transforming the control objective cost to a form that allows its local computation. This property is then exploited for developing our distributed deterministic channel access scheme. A framework to verify the stability of the system under the resulting scheme is then proposed. Next, we consider a practical scenario in which the channel statistics are unknown. We propose learning algorithms for learning the parameters of imperfect communication links for estimating the channel quality and, hence, define the local cost as a function of this estimation and control performance. We establish that our learning approach results in collision-free channel access. The behavior of the overall system is exemplified via a proof-of-concept illustrative example, and the efficacy of this mechanism is evaluated for large-scale networks via simulations.

Age Efficient Optimization in UAV-Aided VEC Network: A Game Theory Viewpoint

The timeless and efficient vehicle data transmission are the two common requirements for the Internet of Vehicles (IoV), especially the Unnamed Aircraft Vehicle (UAV)-aided Vehicular Edge Computing (VEC) network. Moreover, since the Age of Information (AoI) performance greatly influences these two indicators, data quality should be guaranteed in vehicle communication. However, few researchers pay attention to the AoI performance optimization issue regarding wireless resource constraint, transmission interference, and vehicle cooperation in recent years. To close this research gap, we propose an AoI-oriented channel access strategy in the UAV-aided VEC network from the game theory viewpoint. Firstly, the UAV-aided VEC network model and edge computing-based AoI expression are established and derived in the closed form, respectively. Subsequently, we transform the AoI minimization problem into an AoI-based channel access issue from the game theory viewpoint. Moreover, the stochastic learning-based algorithm is proposed to find the Nash Equilibrium (NE) solution of the formulated problem. Finally, simulation results evaluate the correctness and effectiveness of the proposed algorithms, where our scheme can achieve the better AoI value compared with baselines.

Dynamic Multichannel Access Based on Deep Reinforcement Learning in Distributed Wireless Networks

With the emergence of innovative applications in vertical industries such as smart home and industrial automation, machine communication has shown a spurt of development. Different from the traditional human-oriented cellular communication, machine communication is characterized by strong uncertainty and abruptness, large-scale concurrent device connection as well as uneven and unsaturated data traffic. This article investigates the dynamic multiple-devices multiple-channels access for unsaturated traffic with retransmission mechanism, which is aimed at reducing the long-term data packet loss resulting from buffer overflows and transmission failure. The instant channel selection will lead to a non-negligible impact on the future decision, motivating us to model this problem as a Markov decision process. Limited by the unknown environment knowledge, we proposed a dynamic access policy based on deep reinforcement learning algorithm to optimally select the channel for transmission or keep silent for Internet-of-Things devices. Simulation results confirm that our proposed channel access strategy can reduce the collision and the packet loss of network. Furthermore, it can also work well when coexisting with the devices that adopt time division multiple access (TDMA) or ALOHA protocol.

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.

Artificial neural network assisted polling scheme for central coordinated low-latency WLANs

Focusing on uplink transmission , a low-latency medium access control scheme is proposed and investigated for central coordinated wireless local area networks (WLANs). By categorizing user-stations using a trained artificial neural network and avoiding polling empty user-stations during the polling period, effective polling of user-stations can be realized and network latency can be reduced. The artificial neural network is trained by samples collected from a request-based polling scheme, and the boundary for categorization decisioning is selected according to the calculated outputs of training samples. Compared to the conventional controlled channel access scheme, the proposed scheme achieves up to 32.9% reduction in network latency, and significantly improves the network capacity in delivering services requiring 200 μs average latency . Moreover, to improve service quality for user-stations delivering mission-critical services, a latency guarantee mechanism is incorporated. The incorporated scheme records the last access time for each of these user-stations and requires these user-stations to report their buffer statuses when they are in danger of exceeding their latency targets. Simulation results show that the incorporated scheme can ensure a delay below 500 μs for 99.9% of packets of these user-stations, enabling a WLAN to realize ultrahigh quality low-latency performance.