Random Access Scheme Research Articles

QoS-Oriented uplink OFDMA random access for the next-gen WLAN

Abstract OFDMA, introduced by the IEEE 802.11ax standard, divides the channel into multiple resource units (RUs), enabling parallel access for multiple users. On this basis, we propose a QoS-oriented double arbitration random access (Q-DARA) scheme. This scheme ensures high-priority priority access through priority arbitration in the first phase and random code arbitration in the second phase. It can also greatly reduce collisions and effectively guarantee the QoS of high-priority traffic. Simulation shows that our proposed scheme has better performance on throughput and latency. Compared to the traditional EDCA, the throughput and latency increase by more than 70%. Compared to the PATB scheme, the throughput increases by about 20%, and the latency increases by about 23%. Moreover, our proposed scheme has almost no changes to the protocol and is suitable for 802.11ax and beyond.

Towards the development of an electrochemical random access DNA memory (e-RADM)

As a result of the exponentially growing amount of information being produced, new data storage solutions are required. DNA has attracted significant attention as a potential data storage medium thanks to several intrinsic properties, such as ultrahigh physical information density with up to 2 bits per nucleotide. Current DNA-based memories rely on sequencing strategies for data recovery. However, sequencing all DNA strands for data retrieval would be very time consuming and thus result in high levels of latency. Therefore, random access strategies are required to make DNA-based data storage a viable alternative. Here, we present our first steps towards the development of a compartmentalized electrochemical random access DNA memory (e-RADM) using cascade reactions controlled by DNA nanostructures immobilized on gold microelectrode arrays which will be triggered when a specific information retrieval query is put into the system. Electrodes containing the desired information can then be identified by Square Wave Voltammetry.Graphical abstract

A Slotted Pilot-Based Unsourced Random Access Scheme With a Multiple-Antenna Receiver

A Slotted Pilot-Based Unsourced Random Access Scheme With a Multiple-Antenna Receiver

High spatiotemporal mapping of cortical blood flow velocity with an enhanced accuracy.

Cerebral blood flow velocity is one of the most essential parameters related to brain functions and diseases. However, most existing mapping methods suffer from either inaccuracy or lengthy sampling time. In this study, we propose a particle-size-related calibration method to improve the measurement accuracy and a random-access strategy to suppress the sampling time. Based on the proposed methods, we study the long-term progress of cortical vasculopathy and abnormal blood flow caused by glioma, short-term variations of blood flow velocity under different anesthetic depths, and cortex-wide connectivity of the rapid fluctuation of blood flow velocities during seizure onset. The experimental results demonstrate that the proposed calibration method and the random-access strategy can improve both the qualitative and quantitative performance of velocimetry techniques and are also beneficial for understanding brain functions and diseases from the perspective of cerebral blood flow.

Resilient Recursive Filter Design for Complex Networks With Event-Triggering Random Access Scheme and Random Coupling Strengths

Resilient Recursive Filter Design for Complex Networks With Event-Triggering Random Access Scheme and Random Coupling Strengths

Connection-Based Aloha: Modeling, Optimization, and Effects of Connection Establishment

According to whether a connection to the receiver is established before data transmission, random access schemes can be divided into two categories: connection-based and connection-free. For cellular networks, its connection-based access mechanism has been heavily criticized for being ill-suited to Machine-to-Machine (M2M) communications. With the existing studies overwhelmingly focusing on the physical-layer transmission and detection design of various connection-free access schemes, less attention has been paid to the fundamental understanding of when connection establishment could be beneficial. The challenge originates from the lack of proper modeling of connection-based random access. In this paper, a scalable model that incorporates each node’s queueing behavior is proposed for connection-based slotted Aloha, based on which both the data throughput and the mean queueing delay of data packets are characterized, optimized, and compared with those for connection-free slotted Aloha. Conditions for beneficial connection establishment in terms of the optimal throughput and queueing delay performance are discussed and applied to cellular networks with the newly introduced Random Access-based Small Data Transmission (RA-SDT) schemes. The analysis offers important insights for the optimal access design of M2M communications.

Optimized Unsourced Random Access Schemes With Sparse-Correction-Based Approximate Message Passing for Massive MIMO Systems

Optimized Unsourced Random Access Schemes With Sparse-Correction-Based Approximate Message Passing for Massive MIMO Systems

Learning Random Access Schemes for Massive Machine-Type Communication With MARL

Learning Random Access Schemes for Massive Machine-Type Communication With MARL

A Random Access Scheme for Aggregate Traffic Based on Deep Fusion of Supermartingale and Improved SSA

A Random Access Scheme for Aggregate Traffic Based on Deep Fusion of Supermartingale and Improved SSA

A Random Access Scheme for Federated Learning Over Massive MIMO Systems

A Random Access Scheme for Federated Learning Over Massive MIMO Systems

A Grant-Based Random Access Scheme With Low Latency for mMTC in IoT Networks

The design of transmission schemes and receiver techniques with high reliability and low latency for the massive machine-type communications (mMTC) is an important challenge for the future Internet of Things (IoT) systems in the sixth-generation (6G) wireless communication networks. In this paper, we propose a new physical-layer transceiver scheme for mMTC, where users transmit their data based on a pre-designed sparse Tanner graph, and the base station (BS) employs blind channel estimation and message-passing decoding to decode user data. In particular, low latency is achieved by the construction of the transmission Tanner graph, as well as a hybrid modulation scheme that consists of BPSK symbols to facilitate blind channel estimation, and general M-ary modulation to reduce the transmission delay. Moreover, high reliability is achieved by the proposed message-passing decoder that fully exploits the diversity of the transmitted signal and offers soft demodulation and decoding capabilities. We provide both performance analyses based on density evolution, and simulation results, to demonstrate the superior performance of the proposed physical-layer transceiver solution for mMTC.

Deep-Reinforcement-Learning-Based NOMA-Aided Slotted ALOHA for LEO Satellite IoT Networks

The low earth orbit (LEO) satellites have received extensive attention as an essential supplement to the terrestrial network for supporting global Internet of Things (IoT) services. Considering the rapid growth of IoT devices and the significant satellite-to-ground latency, proposing low-latency, low-overhead access protocols for LEO satellite IoT systems is challenging. In this paper, we propose a multi-beam random access (RA) framework and deploy the deep reinforcement learning (DRL) algorithm to control the non-orthogonal multiple access (NOMA) aided random access strategy. First, we divide the satellite coverage region into multiple beams and assume that the adjacent beams share parts of regions. Hence, the devices in the sharing region are allowed to transmit packets in two periods allocated for the two beams. Then, packets in multiple beams can be decoded jointly by an inter-slot successive interference cancellation (SIC) decoder. In addition, we consider the heterogeneity among devices and assign different power levels for heterogeneous types of devices, which enables power-domain NOMA and the intra-slot SIC decoder in this system to mitigate the collision resolution. To maximize the average throughput, the deep deterministic policy gradient (DDPG) algorithm is adopted to achieve an online decision to optimize the random access protocol where the packet repetition strategies of devices are adjusted dynamically. The simulation results show that the proposed scheme outperforms the traditional benchmark schemes with significant throughput gain.

Dynamic spectrum access‐based augmenting coverage in narrow band Internet of Things

SummaryThe Third Generation Partnership Project launched the Narrowband Internet of Things (NB‐IoT) as part of 5G technology under Release‐13. These networks are authorized Low Power Wide Area Networks. It uses less power and sends message over long distances. The NB‐IoT is can be used widely in industries, environment, and home for automation. While comparing NB‐IoT to regular Long Term Evaluation Machine Type Communication, the repetition in uplink is 128. The extreme coverage by 164 dB in terms of Maximum Coupling Loss. The NB‐IoT is employed in deep coverage for users. If we use many repetitions in NB‐IoT technology, the results are shorter battery life and higher maintenance cost. Hence, we suggest abandoning the random spectrum access strategy in favor of a novel technique termed Dynamic Spectrum Access, which uses reinforcement learning algorithm to eliminate the shortcomings. This method is used to decrease the number of re‐transmissions so that NB‐IoT system power goes to less and augments coverage.

Stability Region and Transmission Control of Multi-Cell Aloha Networks

As one of the most representative random-access schemes, slotted Aloha has been adopted in various wireless communication networks. A multi-cell Aloha network may easily become unstable as the inter-cell interference grows if the traffic input rates and transmission probabilities of nodes are not properly regulated. Yet how to stabilize a multi-cell Aloha network has remained largely unknown. To address the above open issue, an analytical framework is proposed in this paper for multi-cell Aloha networks to characterize the stability region of traffic input rates and operating region of transmission probabilities of nodes for achieving network stability. Specifically, the inter-cell interference level is captured by the overlapping ratio of each cell, and shown to be a key factor that determines the stability performance. For a two-cell Aloha network, the stability region of input rates and complete operating regions of transmission probabilities are obtained as functions of the overlapping ratios of cells. For the general <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> -cell case, a transmission control algorithm is further proposed to stabilize the network only based on the local information exchange between neighboring cells, with effectiveness demonstrated through simulations.

Access Delay Optimization of Double-Contention Random Access Scheme in Machine-to-Machine Communications

Machine learning-based random access schemes have gained significant attention in recent years. However, optimizing the access delay of such schemes remains a challenge. In this letter, we focus on analyzing and optimizing the mean access delay of the deep neural network-based double-contention random access (DCRA) scheme, proposed in our earlier work. Specifically, by leveraging the characterization of the state transition of each access request, the mean sojourn time of each state can be derived explicitly, based on which the mean access delay of the DCRA scheme is further obtained. To optimize the mean access delay, we propose optimizing the backoff parameters of every MTDs. Simulation results demonstrate that the mean access delay of the DCRA scheme can be significantly reduced compared to existing schemes, by appropriately selecting the backoff parameters of every MTDs.

Adaptive Random Access and Data Transmission Scheme With Mixed Traffic in NGSO Satellite Networks

In non-geostationary orbit satellite networks, the resource is limited and the traffic under each satellite is in variation due to the motion of satellites and the non-uniform traffic on the ground. Generally, the uplink communication is guaranteed by successful random access (RA) and sufficient data transmission (DT) resources for various traffic types. Different traffic types usually have different requirements for resources, and joint resource management for mixed traffic tries to meet the needs of various traffic types at the same time and may improve uplink resource efficiency. However, existing strategies have not given integrative consideration to jointly allocate resources for RA and DT under dynamic mixed traffic demands, which leads to inefficient network resource utilization. Considering such a problem, we propose an adaptive random access and data transmission scheme for NGSO satellite networks in the mixed traffic scenario, considering two types of users, i.e., real-time users and best-effort users. Specifically, the utility functions of different types of users are introduced, and a utility-based access control and resource allocation algorithm is developed by solving the network utility maximization problem. Our scheme aims to achieve optimal uplink network utilization by adaptively controlling random access and allocating resources according to the dynamic demands of mixed traffic. Simulation results show that the proposed scheme outperforms other methods in terms of network utility, and exhibits near-optimal performance.

Massive Access in 5G and Beyond Ultra-Dense Networks: An MARL-Based NORA Scheme

Power-domain Non-Orthogonal Multiple Access (NOMA) and Ultra-Dense Network (UDN) are promising candidates to cope with the massive access challenge of Machine-Type Communications (MTC). The power level pool is crucial for NOMA to bring performance gains. The existing related literatures rarely consider the power level pool design problem, or only resolve it in the single-cell scenario. However, this problem in multi-cell scenario is more complex and difficult to solve due to the presence of inter-cell interference. Towards this end, we propose a Non-Orthogonal Random Access (NORA) scheme to enable the coexistence of Human-Type Communications (HTC) and MTC for 5G and beyond UDN, where the power level pool design problem in multi-cell scenario is our focus. In order to deal with the complexity caused by multiple optimization objectives and inter-cell interference, we present a Multi-Agent Reinforcement Learning (MARL)-based solution to solve this problem, where each small base station acts as an agent to learn a suitable gap between adjacent power levels. Extensive numerical comparisons demonstrate the superior performances of our proposed scheme in multiple perspectives.

A Hybrid Grant NOMA Random Access for Massive MTC Service

Nonorthogonal multiple access (NOMA) is classified into grant-based (GB) random access (RA) and grant-free (GF) RA in 5G systems. GF NOMA RA allows user equipments (UE) to directly transmit data packet over uplink resources without grant information so that the signaling overhead is substantially reduced. This approach is suitable for small-size data packet IoT services. However, power collision interference resulting from uncoordinated resource selection leads to serious transmission failure in massive MTC scenarios. As a result, the number of transmissions and access delay are sharply increased. In this article, we make the best of the advantages of both GB RA and GF RA and propose a hybrid grant (HG) NOMA RA scheme to address the signaling overhead and power collision issues. The objective of the proposed scheme is to achieve an acceptable tradeoff among signaling overhead, access failure probability, and access delay. The UE selects the access resource without uplink grant, and the BS assists to avoid inter-NOMA power collision by limited interference resolution (IR) signaling. To comprehensively analyze the performance, the closed-form expression of outage probability is derived by adopting the order statistic theory and the sum of independent exponential distributions, and the maximum number of transmissions and access delay are theoretical analyzed. A Monte Carlo simulation framework is built to verify the theoretical analysis. Finally, the analytical and simulation results show that HG NOMA RA outperforms GB NOMA RA and GF NOMA RA in massive access scenarios.

Deep Learning Based Double-Contention Random Access for Massive Machine-Type Communication

With the rapid development of 5G, massive machine-type communication is expected to experience significant growth, leading to severe random access collisions. To address this issue, we first adopt deep neural networks to detect random access collisions by learning the features of the received signals. Based on the collision-detection results, we propose a double-contention random access (DCRA) scheme, with which the base station can schedule one more contention process for devices experiencing collisions. To fully harness the collision-resolution capability of the proposed DCRA scheme, we further analyze its performance and illustrate how to tune the backoff parameters to optimize the network throughput. It is revealed that the maximum throughput of the DCRA scheme depends on the number of random access preambles and the collision recognition accuracy. The corresponding optimal backoff parameters are then obtained, which greatly facilitates implementations in practice. Simulation results show that with a high collision recognition accuracy, the proposed scheme can achieve significant throughput improvement.

Adaptive–Persistent Nonorthogonal Random Access Scheme for URLL Massive IoT Networks

Critical massive Internet of Things networks are emerging technologies that face new challenges in designing transmission protocols for beyond 5G communication systems. Conventional transmission schemes are ill-suited to provide ultrareliable, lowlatency, and scalability requirements of IoT networks with the massive number of nodes having sporadic data traffic behavior. This article overcomes such challenges with proposing a random access transmission scheme that exploits nonorthogonal multiple access (NOMA) with short-packet transmissions and automatic request and repeat (ARQ) strategy with the limited number of retransmissions. To utilize the spectrum further and to meet ultrareliable low-latency requirement, an adaptive–persistent technique is proposed in which each node distributively controls its transmission based on the number of active devices without extra signaling. Since the nodes’ data traffic behavior is assumed sporadic, NOMA-based clustering is performed dynamically at each frame, avoiding additional signaling overhead. Network metrics, such as reliability, effective sum rate, and the distribution of packet latency, are analytically derived. Furthermore, the effects of different network parameters, such as blocklength, the maximum number of packet replicas, number of nodes, and number of resource blocks on network metrics, are investigated and compared with S-ALOHA-ARQ. The analysis show that the proposed scheme outperforms conventional schemes in terms of effective sum rate, reliability, and average packet latency.