ALOHA has become an essential random access protocol for low-power wide-area networks (LPWANs) due to its compatibility with low-cost, low-power consumption and long-range communication requirements. However, its inherent throughput limitation of approximately 0.183 packets per packet transmission time for a large user population significantly restricts its scalability and suitability for the rapidly growing number of IoT devices. To surpass this fundamental bound, we propose a non-orthogonal multiple access (NOMA)-based collision resolution scheme that enables devices to initiate immediate contention-resolving retransmissions following a collision event. This approach requires only an additional collision timer for each user to determine collision resolution participation and appropriate transmission power selection, thereby substantially enhancing throughput without incurring additional hardware costs. We present an analytical framework to evaluate the achievable system throughput and develop an online backoff algorithm that leverages an extended Kalman filter (EKF)-based backlog estimator to dynamically maximize system performance. Numerical results confirm the reliability of the proposed scheme, demonstrating that the system throughput can be improved to approximately 0.5 packets per packet transmission time under ideal conditions. Moreover, the EKF-based approach closely approximates optimal throughput and delay performance under both Poisson and bursty traffic conditions.

This study investigates ALOHA with listen-before-talk (LBT) to enhance the scalability of low-power wide-area networks (LPWANs), such as long range (LoRa). The LBT allows devices to sense the channel prior to accessing so that it can mitigate interference by preventing devices from transmitting during ongoing transmissions. However, its effectiveness is compromised by inherent imperfections in channel sensing, such as false negatives and false positives. A false negative occurs when devices incorrectly find the channel idle while it is actually in use. Thus, this leads devices to unintended interferences with ongoing transmissions. A false positive arises when the channel is erroneously sensed as busy, despite the fact that it is free. This deprives devices of access opportunities. This work analyzes the impact of these imperfections of LBT on the performance of ALOHA in terms of throughput, access delay, and system stability. Additionally, we propose an online backoff control algorithm to optimize system performance under imperfect LBT. The results show that even when devices falsely identify the channel as idle or mistakenly detect it as busy nearly half the time, the throughput still outperforms that of ALOHA without LBT. The proposed backoff control algorithm is also shown to be essential to maximize the throughput in the presence of sensing errors. To demonstrate our analysis and algorithm, we incorporate LoRa’s physical layer parameters into simulations and validate the results accordingly.

ALOHA can be a viable solution as a light-weight medium access control (MAC) protocol in low power wide area networks (LPWANs) for Internet of Things (IoT). However, the maximum throughput of traditional ALOHA is too low to accommodate a large number of IoT devices. To address this limitation, this work proposes an enhanced ALOHA, where successive interference cancellation (SIC) aids in collision resolution. In the proposed system, each user measures the time interval from their transmission epoch to the end of a collision using a collision timer. Upon a collision, the access point (AP) with SIC and the users’ collision timer work jointly to resolve the collision. This work characterizes the throughput of the proposed system and further proposes an online backoff algorithm to maximize the throughput. Numerical results demonstrate that the proposed ALOHA with SIC-aided collision resolution (SACR) can offer significantly improved throughput compared to slotted ALOHA and the other systems.

Along with the rapid growth of Internet of Things (IoT), low power wide area networks (LPWANs) based on unslotted ALOHA, without the need for strict synchronization among IoT devices, becomes one of the cost-effective solutions to support wireless access for sensors requiring long-range connectivity, low cost, and low-power consumption. However, the increasing number of IoT devices poses potential congestion for ALOHA with a low throughput limit of 0.183 (data frames per frame transmission time). This work addresses the throughput limitations of ALOHA by implementing immediate collision resolution (ICR) and integrating an online Bayesian backoff algorithm. The focus is on lifting the throughput limit to enhance LPWANs performance. The work further details translating MAC layer modeling into practical implementation, emphasizing the integration of additional functionalities like uplink local sensing (ULS) and receive window. Our implemented ALOHA with ICR shows a substantial 29% throughput improvement, validating theoretical predictions.

Due to flexibility, ad-hoc networking is an attractive structure for future emerging networks, such as Internet of Things (IoT), wireless sensor networks and vehicular networks. However, due to infrastructure-less, optimizing transmission control to avoid collision in ad-hoc networks is challenging. In this paper, we proposed an adaptive slotted-contention-based media access control (A-SCMAC) protocol which can adapt to network dynamics so that it can reduce collision and improve throughput. With A-SCMAC, each time slot consists of two periods: reservation period (RP) and transmission period (TP). By observing the channel outcomes in the RP the nodes learn the network status such as the number of nodes who have packets to transmit and control the transmission optimally for reserving the TP. Simulation results show that the proposed A-SCMAC can achieve the maximum throughput even the network traffic load changes.

Internet of Things (IoT) represents one of the most significant paradigm shifts recently, with several heterogeneous services and applications to the ultimate realization of connected living. In many status-sensitive IoT services, information usually has a higher value when it is fresher. A new metric, termed the age of information (AoI), was proposed to capture the freshness of status updates. In this paper, we propose a novel online control weight-based age-dependent activation control (W-ADAC) algorithm that prioritizes the activation of the devices that are divided into different classes possibly according to different AoI requirements. With the proposed W-ADAC, we introduce the concept of weighted AoI which is applied to control the activation probability of each device. In particular, higher weights are given to the devices that manifest higher priority requirements. As it is hard to obtain the system weighted AoI due to a lack of information on the distributed devices, we further introduce the capability of estimating the system weighted AoI into the proposed W-ADAC. Extensive simulations show the effectiveness of the proposed W-ADAC over multiple priorities and confirm that the proposed algorithm not only improves the overall system throughput but also provides the near-minimum AoI.

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.

This work considers a bulk transmission scheme for slotted ALOHA (S-ALOHA) systems with a finite population of unsaturated terminals. In the system, once a user makes a successful random access (RA) via S-ALOHA, it can transmit C packets to the traffic channel. We examine an effective capacity, which is defined as the maximally admissible mean arrival rate to the system, subject to the constraint that each user’s queue does not overflow. To obtain this capacity, we use the large deviation theory on the user’s queueing process. For comparison, we also use the dominant pole method in order to obtain the butter overflow probability. We then show that results from the method of using the large deviation theory are asymptotically identical to those from the dominant pole method due to the same decaying rate, and that keeping the traffic load within the effective capacity prevents a user’s queue from overflowing in practice.

Unslotted ALOHA has been often employed by several low-power wide-area networks (LPWANs) for Internet of Things (IoT) as a random access (RA) protocol. This work analyzes the performance of unslotted ALOHA systems in terms of throughput and RA delay, and investigates their optimization. Our analysis consists of: 1) two-heterogeneoususer case, whose backoff rate and packet length are different; 2)$N$-homogeneoususer case, whose backoff rate and packet length are identical; and 3) homogeneous users of infinite population model. In the two-user case, we investigate the throughput region of unslotted ALOHA by using a multiobjective optimization problem (MOOP) and derive the Laplace Stieltjes transform (LST) of the probability density function (PDF) of RA delay. For$N$-homogeneous user case, we show how the throughput behaves according to the population size, packet length, and backoff rate. Our work may provide a comprehensive analytical framework for unslotted ALOHA systems.

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.