Random Access System Research Articles

B-078 Serodiagnosis of Rheumatoid Arthritis Using the New Accentis Chemiluminescence Immunoassay Platform for Anti-CCP Antibody Detection

Abstract Background Rheumatoid arthritis (RA) is one of the most common autoimmune diseases and the most frequent chronic inflammatory arthropathy. Reliable diagnosis at the earliest possible stage is indispensable to keep the disease under control with suitable therapy and to prevent irreversible joint damage. Autoantibodies against cyclic citrullinated peptides (CCP) are a highly specific marker for RA with relevance for (differential) diagnosis, prediction and prognosis. In this study, we investigated the clinical and analytical performance of a newly developed anti-CCP chemiluminescence immunoassay (ChLIA) system. Methods Serum samples from 170 patients with clinically confirmed RA and from a control panel of 274 patients with other diseases were analyzed using the EUROIMMUN Anti-CCP ChLIA (IgG), which was processed on the fully automated random-access system Accentis (EUROIMMUN). The results were compared with those obtained with the EUROIMMUN Anti-CCP ELISA (IgG) with regard to sensitivity, specificity and inter-assay concordance. Results The ChLIA achieved a sensitivity of 71.2% (121/170) and a specificity of 97.4% (267/274), while the ELISA was less sensitive (67.5%, 114/170) and specific (96.4%, 264/274). The qualitative results showed positive and negative agreement rates between the two assays of 92.7% (115/124) and 95.9% (307/320), respectively, resulting in 95.0% (422/444) overall agreement and a kappa score (0.878) indicating near perfect agreement. Conclusions The new Accentis ChLIA showed improved sensitivity and specificity for the detection of anti-CCP antibodies compared to the corresponding ELISA. Given the enhanced clinical performance and the practical benefits of using a random-access system, this ChLIA platform can effectively support the diagnosis of RA.

Non-orthogonal resource scheduling with enhanced preamble detection method for cellular random access networks

In this article, we propose a non-orthogonal resource scheduling (NORS) scheme with an enhanced preamble detection (PD) method for cellular random access (RA) systems. The enhanced PD method is designed based on a deep learning technique that can classify three PD statuses: idle, collision-free, and collision. Because of this collision resolution capability, the base station (BS) can exploit radio resources non-orthogonally in contention-based RA protocols. In addition, we mathematically analyze the performance of our proposed NORS scheme in terms of RA success probability and resource utilization efficiency and conduct a comparison with the baseline orthogonal resource scheduling (ORS) scheme. Through simulations, we verify the validity of our mathematical analysis. As a result, our results will be a useful guideline for resource scheduling and provide support for massive connectivity.

Asynchronous Random Access Systems With Immediate Collision Resolution for Low Power Wide Area Networks

Asynchronous Random Access Systems With Immediate Collision Resolution for Low Power Wide Area Networks

Toward Massive Active Connectivity: Performance Analysis and Near-Optimal Detectors for Grant-Free Random Access Systems

Toward Massive Active Connectivity: Performance Analysis and Near-Optimal Detectors for Grant-Free Random Access Systems

Composite Preambles Based on Differential Phase Rotations for Grant-Free Random Access Systems

With the advantages of low signaling overhead and latency, grant-free random access (GFRA) becomes a promising technology for supporting massive machine-type communications (mMTC), but poses new challenges for active user detection (AUD) and channel estimation (CE), whose performance mainly depends on the preamble detection. In this paper, we design the composite preamble based on differential phase rotations by aggregating orthogonal Zadoff-Chu (ZC) sequences and multiple root ZC sequences with differential phase rotations to reduce the probability of preamble collisions, thereby improving the performance of AUD and CE. In particular, differential phase rotations extend the preamble set size so that users colliding in orthogonal sequences can be distinguished by phase rotations. In addition, it also reduces non-orthogonal interference and thus reduces CE errors. The preamble detection algorithm and CE scheme are proposed, along with the theoretical analysis of AUD and CE performance to verify the effectiveness of the designed preamble. In addition, the proposed preamble is extended to combine phase rotations with cyclic shifts to further enlarge the preamble set size with low non-orthogonality. Simulation results show that the proposed composite preamble outperforms existing preambles in terms of the probability of detection and CE accuracy.

Real-Time Transmission Control for Multichannel NOMA Random Access Systems

To improve the throughput per channel in multichannel non-orthogonal multiple access (NOMA) random access (RA) system, users (re)transmit their packet to one of channels using transmit power control such that the receive power of the packets at the base station (BS) can be one of the predefined levels called target receive power (TRP). The BS decodes the received packets in the descending order of the TRPs at each slot using successive interference cancellation (SIC). This work proposes real-time transmission algorithm for users to (re)transmit their packet for maximization of the system throughput. To do this, the BS estimates the number of backlogged users in real-time and adjusts and broadcasts the throughput-optimal (re)transmission probability in the algorithm. We analyze the average RA delay performance of the proposed algorithm and demonstrate its performance even with time-varying traffic.

Activity Detection in Distributed Massive MIMO With Pilot-Hopping and Activity Correlation

Many real-world scenarios for massive machine-type communication involve sensors monitoring a physical phenomenon. As a consequence, the activity pattern of these sensors will be correlated. In this letter, we study how the correlation of user activities can be exploited to improve detection performance in grant-free random access systems where the users transmit pilot-hopping sequences and the detection is performed based on the received energy. We show that we can expect considerable performance gains by adding regularizers, which take the activity correlation into account, to the non-negative least squares, which has been shown to work well for independent user activity.

Time-Offset ALOHA With SIC

Internet-of-Things (IoT) applications for real-time control gradually increase and become computationally demanding. To provide better quality-of-service (QoS) in random access (RA) system based on slotted ALOHA (S-ALOHA), this work proposes a novel S-ALOHA system with cross-slot successive interference cancellation (SIC). To facilitate SIC, we design each slot with several time offsets (TOs) and one packet transmission time, where the length of overall TOs is a fraction of a packet transmission time. Users (re)transmit at the boundary of a TO randomly selected. This enables the base station (BS) to distinguish who makes the first and last transmissions in a collision slot and ask immediate retransmissions from them in the subsequent one or two slots. With these retransmitted packets, the BS performs SIC for the previously collided packets. We analyze the system throughput and the distribution of RA delay. The results show that the proposed system can achieve throughput from 0.5 (packets per packet transmission time) at minimum to 0.856 at maximum, depending on the number of TOs and the length of TO. In addition, to run this system stably, we propose a Bayesian-optimized backoff algorithm that enables users to use throughput-optimal (re)transmission probability. It is demonstrated that the proposed backoff algorithms can achieve the throughput close to genie-aided (GA) system.

Analytic Distribution Design for Irregular Repetition Slotted ALOHA With Multi-Packet Reception

Associated with multi-packet reception at the access point, irregular repetition slotted ALOHA (IRSA) holds a great potential in improving the access capacity of massive machine type communication systems. This work investigates the packet repetition of the users in an IRSA-based random access system where the access point can simultaneously retrieve multiple packets in a slot. Specifically, we propose an analytical transmission probability distribution for IRSA with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dual-packet reception capability</i> . In addition, the energy efficiency optimization in terms of the maximum number of replica is also presented. Numerical results show that using the proposed transmission distribution for the replica can achieve a significant higher throughput than the existing benchmark distributions. With optimized repetition rate, the energy efficiency of users can be also improved remarkably.

Exploiting in-Slot Micro-Synchronism for S-ALOHA

Proliferation of the urban Internet-of-Things (IoTs) for smart cities has fuelled massive amounts of data over wireless cellular networks. Random access (RA) system of wireless cellular networks, e.g., 5G New Radio (NR), based on S-ALOHA system should cope with ever-growing IoT traffic. This work proposes S-ALOHA system with time offsets (TOs), where one slot consists of K TOs and one packet transmission time. The length of the overall TOs is a fraction of a packet transmission time. In the system users (re)transmit to the boundary of a TO randomly selected. This enables the base station (BS) to inform the users of who transmits the first and the last packets in the slot with collision so that the two users can retransmit successfully in the following two slots respectively. Our throughput analysis compared to simulations shows that adopting even with three and four TOs surpasses the throughput limit of S-ALOHA system without TOs. Additionally, we propose two Bayesian-optimized backoff algorithms for S-ALOHA system with TOs, with which users can apply throughput-optimal (re)transmission probability or uniform backoff window even in unsaturated traffic scenarios. Numerical results demonstrate that the proposed backoff algorithms can achieve the throughput close to an ideal system and drastically reduce the access delay compared to S-ALOHA system.

Learning-Based Sparse Recovery for Joint Activity Detection and Channel Estimation in Massive Random Access Systems

We consider the problem of joint activity detection and channel estimation in massive random access. When the receiver has multiple antennas, this is a joint sparse recovery problem with multiple measurement vectors (MMV). For the general setting where the channels could be correlated across antennas, we first develop a modified minimum mean squared error (MMSE) shrinkage function to be used in the Trainable Iterative Soft Thresholding Algorithm (TISTA). Then, we learn this MMSE shrinkage function using a model-based neural network. In the simulation results, the proposed learning-based method, L-MMSE-MMV-TISTA, offers a 30-40% reduction in preamble length requirement compared to TISTA. We also compare L-MMSE-MMV-TISTA with the state-of-the-art MMV sparse Bayesian learning (M-SBL) method. While M-SBL can provide better performance at the cost of higher complexity in highly measurement-constrained settings, LMMSE-MMV-TISTA provides a significant complexity advantage when only a slightly larger number of measurements are available.

Random Access With Massive MIMO-OTFS in LEO Satellite Communications

This paper considers the joint channel estimation and device activity detection in the grant-free random access systems, where a large number of Internet-of-Things devices intend to communicate with a low-earth orbit satellite in a sporadic way. In addition, the massive multiple-input multiple-output (MIMO) with orthogonal time-frequency space (OTFS) modulation is adopted to combat the dynamics of the terrestrial-satellite link. We first analyze the input-output relationship of the single-input single-output OTFS when the large delay and Doppler shift both exist, and then extend it to the grant-free random access with massive MIMO-OTFS. Next, by exploring the sparsity of channel in the delay-Doppler-angle domain, a two-dimensional pattern coupled hierarchical prior with the sparse Bayesian learning and covariance-free method (TDSBL-FM) is developed for the channel estimation. Then, the active devices are detected by computing the energy of the estimated channel. Finally, the generalized approximate message passing algorithm combined with the sparse Bayesian learning and two-dimensional convolution (ConvSBL-GAMP) is proposed to decrease the computations of the TDSBL-FM algorithm. Simulation results demonstrate that the proposed algorithms outperform conventional methods.

Performance Evaluation of Random Access Methods for IoT-over-Satellite

Low Earth orbit (LEO) satellite constellations are currently being explored to provide global and seamless coverage for IoT-over-Satellite applications. Random access techniques require low transmission overhead providing a compatible route for IoT-over-Satellite applications, however, coming at the expense of the offered quality-of-service. In this paper, we develop a realistic uplink performance framework that incorporates many practical parameters such as the satellite availability, packet collision and interference, Doppler shift, and impairments experienced in a typical Satellite-to-Ground channel. The framework is capable of assessing multiple key performance indicators of the overall IoT-over-Satellite random access system. The performance is presented in terms of the bit error rate, packet error rate, and the energy wasted per IoT device. To emulate a realistic IoT-over-Satellite network, LoRa modulated traffic is first generated and injected into the Satellite-to-Ground channel. The results indicate high resistance to Doppler shifts even without any Doppler correction and provide some resistance to highly congested environments.

Decentralized Power Control for an ALOHA-Type Random Multiple Access System with Short Packet Transmission

Machine to machine communication is an important scenario in a 6G communication network. Random multiple access has recently been revisited and considered a key technology for machine to machine communication scenarios due to many advantages such as without coordination setup time. It is a regret that packet collision probability will be extremely higher for random multiple access when massive devices randomly accessing base station. Decentralized power control is an efficient scheme in random multiple access systems which can support intraslot successive interference cancellation to recover multiple collided packets at receivers. However, existing studies of decentralized power control for random multiple access are with the assumption that blocklength of transmitted packets is infinite, which neglects that machine to machine communication is characterized by finite blocklength transmission (i.e., short packet) in 6G. This paper focuses decentralized power control with short packet transmission in random multiple access systems. First, the closed-form expression of signal to interference plus noise ratio (SINR) threshold for short packets is derived. Then, decentralized transmission power profile is defined based on derived SINR threshold of short packets, which can support intraslot successive interference cancellation deciding at receivers for an ALOHA-type random multiple access system. Further, we propose derivation method to maximize system throughput, which can reduce optimization cost. Theoretical findings in this paper can provide valuable benchmark for short packet transmission with decentralized power control in random multiple access systems.

Molecular-fingerprint machine-learning-assisted design and prediction for high-performance MOFs for capture of NMHCs from air

The capture of trace amounts of non-methane hydrocarbons (NMHCs) from air due to the toxicity of volatile organic compounds is a significant challenge. A total of 31399 hydrophobic metal–organic frameworks (MOFs) were first screened from 137953 hypothetical MOFs using high-throughput computational screening (HTCS), and their performance indices (adsorption capacity and selectivity) for the adsorption of NMHCs (C 3 –C 6 ) were obtained by molecular simulations. The discovery of a “second peak” near twice the kinetic diameter of the corresponding NMHC provided more choices for excellent MOFs that adsorb NMHCs. Four machine learning (ML) classification and regression algorithms predicted the performance of MOFs, and the relative importance values of the six descriptors were determined. The combination of the Random Forests algorithm and Molecular ACCess Systems molecular fingerprint (MF) had an excellent predictive ability for MOFs. According to the performance, the fingerprint commonalities of the 100 top-performing MOFs were counted, and the excellent bits (EBs) that could promote the performance were defined. Finally, new substructures containing all of the EBs were designed for each NMHC to build a new MOF database. This work combined the HTCS, ML, and MF to provide a detailed insight into the design of efficient MOFs for adsorbing NMHCs.

Short-Packet Transmission in Irregular Repetition Slotted ALOHA System Over the Rayleigh Fading Channel

Random access systems are potential for Internet of Things in the future wireless communication network for its operational simplicity. Irregular repetition slotted ALOHA (IRSA) system is one of the high-efficiency random access systems. In this paper, performance analysis of the irregular repetition slotted ALOHA systems with short-packet, i.e. finite-blocklength, transmission for the quasi-static Rayleigh fading channel is given. A cumulative distribution function of signal-to-interference power ratio (SIR) is derived and thus a closed-form expression of an average packet error probability (PEP) at the SIR with short packets for the Rayleigh fading channel is given. The closed-form expression makes it possible to optimize the degree distributions at a specific blocklength in the sense that the systems give the maximum system load.

Dynamic Multichannel Access via Multi-Agent Reinforcement Learning: Throughput and Fairness Guarantees

We consider a multichannel random access system in which each user accesses a single channel at each time slot to communicate with an access point (AP). Users arrive to the system at random and be activated for a certain period of time slots and then disappear from the system. Under such dynamic network environment, we propose a distributed multichannel access protocol based on multi-agent reinforcement learning (RL) to improve both throughput and fairness between active users. Unlike the previous approaches adjusting channel access probabilities at each time slot, the proposed RL algorithm deterministically selects a set of channel access policies for several consecutive time slots. To effectively reduce the complexity of the proposed RL algorithm, we adopt a branching dueling Q-network architecture and propose an efficient training methodology for producing proper Q-values over time-varying user sets. We perform extensive simulations on realistic traffic environments and demonstrate that the proposed online learning improves both throughput and fairness compared to the conventional RL approaches and centralized scheduling policies.

Unsourced Random Access Over Fading Channels via Data Repetition, Permutation, and Scrambling

We focus on an unsourced random access (URA) system for communication over fading channels where the payload of each packet is encoded for error-correction, repeated, permuted, and scrambled. Each packet is also equipped with a preamble that is used for channel estimation and detection of permutation and scrambling sequences utilized for payload encoding. We propose an algorithm to resolve multiple-access preamble transmission, based on the approximate message-passing (AMP), that is capable to support high numbers of active users and achieve low probabilities of miss-detection. We also develop a parallel interference cancellation technique for payload reception that iteratively refines the channel estimates and attempts to minimize the mean squared error (MSE) of the users’ data via selective error-correction decoding. Finally, we derive a detailed system performance analysis that closely matches the obtained numerical results. We demonstrate that the presented system can more than double the number of active users, supported by the state-of-the-art systems. Large gains in terms of the minimal required signal-to-noise ratios (SNR)s are also demonstrated for a wide range of active user numbers.

Stability and instability of a random multiple access system with an energy harvesting mechanism

Stability and instability of a random multiple access system with an energy harvesting mechanism

Online Estimation and Adaptation for Random Access with Successive Interference Cancellation

In slotted random access systems, when multiple users transmit packets simultaneously, owing to the successive interference cancellation (SIC) technique, the access point (AP) is able to decode them through SIC-enabled resolution procedures (SRPs), which may occupy multiple consequent slots. While such an SRP could potentially improve the system throughput, how to fully exploit it in practical systems is still questionable when SIC capability is limited. Moreover, the number of active users contending for the channel varies over time which complicates the random access algorithm design. In order to fully exploit the potential of such limited SIC capability and maximize the system throughput, a novel online estimation based on Bayesian approach is introduced to estimate the number of active users in real-time and controls each user&#x0027;s transmission accordingly. It is shown that the throughput of the proposed algorithm can reach up to 0.693 packets/slot under practical assumptions, which is the first result achieving the throughput limit proved by Yu-Giannakis. It is further shown that the system throughput of 0.594 packets/slot (85.7% of the throughput limit) is achievable even when the SIC capability is restricted by two. It is also shown that the proposed online estimation and adaptation framework can be extended to further exploit collision information and the imperfect SIC.