Autonomous Resource Selection Research Articles

Congestion Control in Autonomous Resource Selection of Cellular-V2X

Intelligent transportation systems have recently become a promising technology for future industry to provide safe, green, and automated driving. In this regard, the Third Generation Partnership Project (3GPP) has proposed to utilize cellular communication to enable direct communication between vehicles with and without cellular infrastructure assistance. 3GPP has introduced the Sensing-Based Semi-Persistent Scheduling (S-SPS) technique when coverage of the cellular system is absent. S-SPS faces resource collision and performance degradation problems when channel load increases in the network. This paper suggests a decentralized congestion control and transmission power control mechanism (TPC-DCC) with an adaptive threshold for the received signal as a combination method to decrease channel load in the network. Furthermore, this work introduces a novel channel load adjustment. The new adjusting algorithm is based on a constant difference between the upper and lower boundaries of channel load at each level to handle channel overload and provide more flexibility using DCC mechanisms. The interactions of the proposed algorithm with S-SPS and the Extended-Estimation Reservation Resource Allocation (E-ERRA) algorithms that the authors previously proposed are investigated. The results indicate that system performance can be substantially improved when the transmission power and reception threshold are adaptively adjusted to the proposed channel load adjustment. The results of E-ERRA with the proposed channel load adjustment method show promising results compared to S-SPS.

TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4.

Effective communication in vehicular networks depends on the scheduling of wireless channel resources. There are two types of channel resource scheduling in Release 14 of the 3GPP, i.e., (1) controlled by eNodeB and (2) a distributed scheduling carried out by every vehicle, known as Autonomous Resource Selection (ARS). The most suitable resource scheduling for vehicle safety applications is the ARS mechanism. ARS includes (a) counter selection (i.e., specifying the number of subsequent transmissions) and (b) resource reselection (specifying the reuse of the same resource after counter expiry). ARS is a decentralized approach for resource selection. Therefore, resource collisions can occur during the initial selection, where multiple vehicles might select the same resource, hence resulting in packet loss. ARS is not adaptive towards vehicle density and employs a uniform random selection probability approach for counter selection and reselection. As a result, it can prevent some vehicles from transmitting in a congested vehicular network. To this end, the paper presents Truly Autonomous Resource Selection (TARS) for vehicular networks. TARS considers resource allocation as a problem of locally detecting the selected resources at neighbor vehicles to avoid resource collisions. The paper also models the behavior of counter selection and resource block reselection on resource collisions using the Discrete Time Markov Chain (DTMC). Observation of the model is used to propose a fair policy of counter selection and resource reselection in ARS. The simulation of the proposed TARS mechanism showed better performance in terms of resource collision probability and the packet delivery ratio when compared with the LTE Mode 4 standard and with a competing approach proposed by Jianhua He et al.

NR Sidelink Enhancement in 3GPP Release 17

3GPP is currently studying enhancements to Sidelink (SL) operations for 5G New Radio (NR) in a Release 17 Work Item which is planned to be finished by end of 2021. The NR Sidelink (SL) Work Item in Release 17 includes several key features targeting reliability enhancements, power saving and coverage enhancements by expanding the scope of NR sidelink to target V2X, commercial D2D use-case and Public safety. This paper provides an insight on the current 3GPP Release 17 NR SL design describing necessary enhancements in the physical, protocol layer to support inter-UE coordination message for reliability enhancement for autonomous resource selection procedure by providing feedback on the half-duplex, persistent collision and hidden nodes. In addition, the power saving feature is addressed by introducing a SL DRX mechanism for the PC5 interface which defines active reception and transmission periods between a TX and the peer Rx UE(s) and also between Tx UE and gNB. Furthermore, this paper outlines details on the Sidelink Relay feature for coverage enhancement by describing possible solutions for UE to Network relay and UE to UE relay.

3GPP NR V2X Mode 2: Overview, Models and System-Level Evaluation.

Following the successful use of sidelink in Long Term Evolution (LTE) for Proximity Services (ProSe) and Cellular Vehicular-to-everything (C-V2X), the 3rd Generation Partnership Project (3GPP) is working towards its evolution in New Radio (NR) systems in the context of the so-called NR V2X. This new technology is expected to complement LTE C-V2X for advanced services by offering low latency, high reliability, and high throughput V2X services for advanced driving use cases. To do this, NR V2X is equipped with new features, such as the support for groupcast and unicast communication, a novel feedback channel, and a new control channel design. In this paper, we provide a complete history of sidelink technology in 3GPP followed by a detailed overview of NR V2X technology, with special emphasis on Mode 2 for out of coverage operation and autonomous resource selection. Furthermore, this paper presents a system-level NR V2X standard-compliant simulator, as an extension of the popular and open-source NR network simulator 5G-LENA, based on ns-3. In particular, we focus on the design, implementation, and evaluation of the sensing-based resource selection in NR V2X Mode 2, in a highway scenario. Through several and extensive simulation campaigns, we test the impact of different NR V2X parameters, such as the numerology, the resource selection window size, the number of retransmissions, the maximum number of resources per reservation, and the probability of keeping the same resources during reselection, in a sensing-based resource selection. Finally, we provide a comparison campaign that shows the gains attained by the sensing-based resource selection, proposed during 3GPP Release 16, over the random selection strategy, considered in 3GPP Release 17 for power saving purposes.

Context aware autonomous resource selection and Q-learning based power control strategy for enhanced cooperative awareness in LTE-V2V communication

C-V2X (Cellular Vehicle-to-Everything) standard introduced in 3GPP Release-14 is emerging as a potential technology for Cooperative Awareness Message (CAMs) dissemination among connected vehicles. But to attain its full potential, optimal resource scheduling and interference management must be enforced. To address this issue, we propose a weighted exponential averaging based Context-Aware Resource Reselection scheme (CARRs), enabling the periodic exchange of CAMs in a vehicular network. The proposed strategy allows autonomous resource selection by performing continuous power sensing and cooperative learning with the neighbors within the safety zone. CARRs is a two-stage learning process. In the first stage, it learns the exponential weighing factor for each resource available in the Vehicle-to-Vehicle resource pool and performs resource reselection. In the second stage, it learns to select transmit power level based on the interference experienced over the reselected resource. It is established through numerical results that CARRs outperforms the existing strategies. Packet reception ratio, average error rate, average blocking rate and update delay are considered as the performance metrics. CARRs improves packet reception ratio by 3.1% while reducing the average error rate by 28.2% and lowering update delay by 4.5% in comparison to existing schemes.

Weighted cooperative reinforcement learning‐based energy‐efficient autonomous resource selection strategy for underlay D2D communication

Underlay Device-to-Device (D2D) communication is a key technology responsible for high data rate, ultra-low latency with high spectral and energy efficiency in 5G cellular networks. But to achieve its full potential, optimal channel allocation and effective co-channel interference management must be accomplished. To address this challenge, we propose a multi-agent reinforcement learning based autonomous channel selection scheme for D2D communication. The proposed scheme, Weighted Cooperative Q -Learning based Resource Selection (WCopQLRS), allows a D2D pair to learn to select a channel from the available resources autonomously. Learning process of each D2D transmitter involves cooperation from neighboring D2D agents by exchanging their latest Q -values. An additional parameter called cooperation range is used to determine the neighboring pairs whose Q -values can be used for learning the optimal policy. The limited prior information prevents a linear increase in the dimensions of Q -value matrix of each learning agent when the number of D2D pairs within the cell is huge. Though WCopQL-RS involves additional information exchange among agents as compared to independent learning but also provides improved system throughput and convergence speed. It is shown through simulation results that WCopQL-RS outperforms other existing schemes in terms of average D2D user throughput, energy consumption and fairness value.

Joint Power Control and Resource Allocation Mode Selection for Safety-Related V2X Communication

Safety-related vehicle-to-everything (V2X) communication plays an essential role in road safety and requires stringent high reliability and low latency. In this paper, we propose a novel approach of joint power control and resource allocation mode selection to solve the resource allocation problem of the mixed centralized/distributed V2X communication system under different network load conditions. Two major resource allocation modes are considered for V2X communication: scheduled resource allocation (centralized system) and UE autonomous resource selection mode (distributed system). In order to meet the specific quality-of-service-related demands of safety-related V2X communication, we aim to maximize overall information value of V2X communication by considering both ProSe Per-Packet Priority and communication link quality of V2X messages while meeting the requirements of minimum SINR and maximum transmit power limitation for both pedestrian user equipments (PUEs) and vehicular user equipments (VUEs). In particular, scenarios where the network is lightly loaded we propose Vacant Resource Blocks and Power Allocation algorithm while for scenarios wherein the network is heavily loaded we propose Occupied Resource Blocks and Power Allocation algorithm. Furthermore, the proposed algorithm can achieve a stable matching between VUEs and their reuse PUEs partners. Extensive simulation results show that both the information value of VUEs as well as the spectral efficiency of PUEs can be improved efficiently by the proposed approach.

Enhanced Collision Avoidance for Distributed LTE Vehicle to Vehicle Broadcast Communications

In this letter, we investigate the distributed autonomous resource selection for LTE vehicle to vehicle (V2V) broadcast. The effectiveness of collision avoidance and location based resource allocation enhancements is examined. It is found that collision avoidance with multiple data resources reservation per schedule assignment (SA) is a key to improve broadcast reliability. However, in the existing collision avoidance algorithm reserving multiple resources per SA can lead to many data packet collisions if a SA collision happens. We propose an enhanced collision avoidance to address this issue. The idea is to use selected data packets to disseminate the reservation of data resources and SA resources, which can provide better communication among neighbor vehicles on resource reservation and reduce data collisions. Simulation results show that the proposed collision avoidance enhancement can effectively improve SA and data transmission reliability. The network capacity in terms of supported vehicles under given V2V service requirements is largely increased by 17% at a negligible cost of added overhead.