Random Access Channel Procedure Research Articles

Deep reinforcement learning based flexible preamble allocation for RAN slicing in 5G networks

One of the most difficult challenges in radio access network slicing occurs in the connection establishment phase where multiple devices use a common random access channel in order to gain access to the network. It is now very well known that random access channel congestion is a serious issue in case of sporadic arrival of machine-to-machine nodes and may result in a significant delay for all nodes. Hence, random access channel resources are also needed to be allocated to different services to enable random access network slicing. In the random access channel procedure, the nodes transmit a selected preamble from a predefined set of preambles. If multiple nodes transmit the same preamble at the same random access channel opportunity, a collision occurs at the eNodeB. To isolate the one service class from others during this phase, one approach is to allocate different preamble subsets to different service classes. This research proposes an adaptive preamble subset allocation method using deep reinforcement learning. The proposed method can distribute preambles to different service classes according to their priority providing virtual isolation for service classes. The results indicate that the proposed mechanism can quickly adapt the preamble allocation according to the changing traffic demands of service classes.

RAN Slicing for Massive IoT and Bursty URLLC Service Multiplexing: Analysis and Optimization

Future wireless networks are envisioned to serve massive Internet of Things (mIoT) via some radio access technologies, where the random access channel (RACH) procedure should be exploited for IoT devices to access the networks. However, the theoretical analysis of the RACH procedure for massive IoT devices is challenging. To address this challenge, we first correlate the RACH request of an IoT device with the status of its maintained queue and analyze the evolution of the queue status by the probability theory. Based on the analysis result, we then derive the closed-form expression of the random access (RA) success probability, which is a significant indicator characterizing the RACH procedure of the device by the stochastic geometry theory. Besides, considering the agreement on converging different services onto a shared infrastructure, we investigate the radio access network (RAN) slicing for mIoT and bursty ultrareliable and low-latency communication (URLLC) service multiplexing. Specifically, we formulate the RAN slicing problem as an optimization one to maximize the total RA success probabilities of all IoT devices and provide URLLC services for URLLC devices in an energy-efficient way. A slice resource optimization (SRO) algorithm, exploiting relaxation and approximation with provable tightness and error bound, is then proposed to mitigate the optimization problem. Simulation results demonstrate that the proposed SRO algorithm can effectively implement the service multiplexing of mIoT and bursty URLLC traffic.

A Decoupled Learning Strategy for Massive Access Optimization in Cellular IoT Networks

Cellular-based networks are expected to offer connectivity for massive Internet of Things (mIoT) systems. However, their Random Access CHannel (RACH) procedure suffers from unreliability, due to the collision from the simultaneous massive access. Despite that this collision problem has been treated in existing RACH schemes, these schemes usually organize IoT devices' transmission and re-transmission along with fixed parameters, thus can hardly adapt to time-varying traffic patterns. Without adaptation, the RACH procedure easily suffers from high access delay, high energy consumption, or even access unavailability. With the goal of improving the RACH procedure, this paper targets to optimize the RACH procedure in real-time by maximizing a long-term hybrid multi-objective function, which consists of the number of access success devices, the average energy consumption, and the average access delay. To do so, we first optimize the long-term objective in the number of access success devices by using Deep Reinforcement Learning (DRL) algorithms for different RACH schemes, including Access Class Barring (ACB), Back-Off (BO), and Distributed Queuing (DQ). The converging capability and efficiency of different DRL algorithms including Policy Gradient (PG), Actor-Critic (AC), Deep Q-Network (DQN), and Deep Deterministic Policy Gradient (DDPG) are compared. Inspired by the results from this comparison, a decoupled learning strategy is developed to jointly and dynamically adapt the access control factors of those three access schemes. This decoupled strategy integrates predicted traffic into the learning process to improve training efficiency, where a Recurrent Neural Network (RNN) model is first employed to predict the real-time traffic values of the network environment, and then multiple DRL agents are employed to cooperatively configure parameters of each RACH scheme. Our results demonstrate that the decoupled strategy remarkably accelerate the training speedy.

Generalized RACH-Less Handover for Seamless Mobility in 5G and Beyond Mobile Networks

The fifth generation mobile networks must deal with the explosive growth of traffic load and connected devices, and also reliably reduce latency. Particularly, seamless mobility is essential for achieving low handover latency. In this letter, a generalized random access channel (RACH)-less handover scheme is proposed for achieving seamless mobility without requiring a synchronized network. The proposed scheme can be efficiently applied to more general cell deployment situations while avoiding the latency due to the RACH procedure. The simulation results show that the proposed scheme substantially reduces the handover latency compared with the handover schemes in the 3GPP standards.

Effect of RACH Procedure on the Performance of LTE-based M2M Communication

paper, the parameters of the Random Access Channel (RACH) of LTE/ LTE-A based M2M communication under congestion situation are investigated. These parameters include the effect of the Backoff Indicator (BI), Hybrid Automatic Repeat-reQuest (HARQ) and contention resolution timer (CRT) on the Performance of LTE-based M2M Communication. The results indicate that the parameters of the RACH procedure have an impact on the network's performance, such as delay and packets dropped.

General Model for RACH Procedure Performance Analysis

This letter introduces an analytical model aiming at modeling the performance of the Random Access CHannel (RACH) procedure, with a special focus on the case of highly synchronized machine type communication (MTC) traffic activated according to beta distribution. The accuracy of the proposed model is then verified through computer simulation. Obtained results demonstrate the effectiveness of the model to capture the RACH procedure behavior when MTC devices are activated in a highly synchronized way. Note that 1) the proposed model is not dedicated only to beta distribution, but other distributions could be used and 2) this model could be used with any congestion control method managing the number of new arrivals, such as access class barring (ACB).

Measurement-adaptive cellular random access protocols

This work considers a single-cell random access channel (RACH) in cellular wireless networks. Communications over RACH take place when users try to connect to a base station during a handover or when establishing a new connection. Within the framework of Self-Organizing Networks (SONs), the system should self- adapt to dynamically changing environments (channel fading, mobility, etc.) without human intervention. For the performance improvement of the RACH procedure, we aim here at maximizing throughput or alternatively minimizing the user dropping rate. In the context of SON, we propose protocols which exploit information from measurements and user reports in order to estimate current values of the system unknowns and broadcast global action-related values to all users. The protocols suggest an optimal pair of user actions (transmission power and back-off probability) found by minimizing the drift of a certain function. Numerical results illustrate considerable benefits of the dropping rate, at a very low or even zero cost in power expenditure and delay, as well as the fast adaptability of the protocols to environment changes. Although the proposed protocol is designed to minimize primarily the amount of discarded users per cell, our framework allows for other variations (power or delay minimization) as well.