Millimeter wave (mmWave) spectrum promises unprecedented data rates in 5G New Radio (NR). However, mmWave links are susceptible to severe path and propagation losses. Implementation of directional antennas that can manifest multiple beams becomes necessary at 5G base stations (gNBs) and User Equipments (UEs). Random access procedure (RAP), which is the first stage to establish connections to gNB, in its current state would be inadequate for such systems. To support RAP, 5G NR introduced mapping of different subsets of preambles in Physical Random Access Channel (PRACH) to different beams. We explore changes in RAP due to preamble-beam mapping and present a novel online algorithm to dynamically distribute preambles amongst beams to achieve fair access opportunities. The proposed algorithm can also eliminate overload problem of the beams with large UE population especially for high arrival rates. We extend the proposed algorithm to be applicable for RAP with access class barring mechanism which enables UEs to transmit a preamble with an optimal probability and, therefore, reduce access congestion. Through extensive simulations, we validate the efficiency of our proposed algorithms in providing fair channel access opportunities, improving throughput (or stability region), and reducing access delay when the system has high rate of UE arrivals and has unbalanced UE arrivals over different beams.

A larger capacity of random access (RA) channel is demanded to cope with massive accesses from Internet of Things (IoT) devices. To do this, this work proposes multipacket reception (MPR) S-ALOHA with reservation: When making a successful RA, the user can reserve the channel for the next slot transmission with probability$r$, if its queue is not yet empty. This reservation can be continued with probability$r$until its queue becomes empty. In this system, as the number of the reserved channels grows, the number of available capacity for RA reduces. Thus, the number of RA attempting users needs to be controlled to avoid collision, whereas the unbounded growth of other users’ queue should be prevented. This work analyzes the throughput and stability condition of the proposed system and designs a throughput-optimal backoff algorithm based on the backlog size estimation. In the numerical studies, MPR S-ALOHA without reservation and time division multiple access (TDMA) are compared as benchmarks. As a result, it is proven that as$r\rightarrow 1$, the MPR channel capacity is fully utilized if the proposed RA algorithm is jointly used with the reservation scheme. Moreover, it is demonstrated that the system can be also stabilized by the proposed backoff algorithm.

For industrial Internet of Things network operators, undesired and abrupt network failure is a critical problem to be resolved quickly. In order to provide reliable communication services to devices in faulty cells, in this article, we propose a distributed autonomous self-healing mechanism that allows a random-access-based instantaneous communication to the neighbor cells. The design of the self-healing mechanism is challenged by the diverse device locations and the different available number of channels provided by the neighbor cells due to their intracell traffic load. By estimating the number of devices communicating with each neighbor cell in an online manner, our proposed mechanism can control the channel access probability of each cell to maximize throughput. In addition, the devices can reselect their serving cells in a distributed manner while realizing maximum but fair throughput among devices. Through extensive simulations, we show that our proposed mechanism can provide effective performance for autonomous self-healing.

Internet of Things (IoT) imbued with several heterogeneous services and applications is integral to the ultimate realization of connected living. However, massive connectivity would pose huge challenges not only due to immense scalability but also due to diverse characteristics of periodicity, delay-criticality, and quality-of-service (QoS) requirement. One important trend of this diversity is that the connected devices would manifest different priorities. Random access procedure (RAP) is the first step by which most devices establish connection to the base station. A major transformation is urgently needed in RAP for cellular IoT as the volume of connected devices would far exceeds human oriented connections of the legacy networks. To prioritize the RAP for IoT devices, we propose novel online control algorithm that enables the most likely successful preamble transmission for the devices that manifest higher priority requirements. In the proposed algorithm, the number of active devices in each priority is recursively estimated based on Bayesian rule and then the preambles to each priority are accordingly allocated. We extend our proposal by adopting access class baring (ACB) to optimize the algorithm and subsequently, enhance it further by incorporating access delay requirements. Extensive simulations show the effectiveness of proposed algorithms over multiple priorities and confirm that the proposed algorithms are able to resolve congestions for massive activation of IoT devices.

This paper considers non-orthogonal multiple access (NOMA) systems in which users randomly transmit packets in each slot while the transmit power is adjusted so that the receive power of each packet at the base station can be one of two predetermined values. While the NOMA random access system can support simultaneous transmissions from up to two users with different target receive powers, it complicates the access protocol design as each user not only has to optimize the transmission probability but also has to suitably choose the target receive power. By considering the fact that users may have different capability of choosing the target receive power due to their diverse locations in a cell, we propose an online distributed channel access algorithm which enables the users to adaptively choose the target receive power and control the transmission probability over time through observing channel outcomes such as idle, success and collision. Numerical results demonstrate that the proposed algorithm can optimize the system performance in terms of throughput and access fairness.