Solving the performance puzzle of DSRC multi-channel operations

Abstract

The key goal of our work is to provision sufficient reliability for real-time safety messages in CCH while optimising non-safety service delivery in SCH. We have developed analytical models for both broadcasting and unicasting to explore the relationship among traffic density, CCH packet loss ratio, SCH throughput, and the duration of each kind of intervals.Unlike assigning fixed channel interval in error-prone default channel access approach, we develop a multi-channel coordination algorithm which adaptively adjusts the duration of intervals to achieve better performance and reliability based on these models. Theoretical analysis and extensive simulation results demonstrate the accuracy of our model and the efficacy of the proposed algorithm. The algorithm almost doubles SCH throughput in low traffic density scenario and reduces packet loss ratio by up to 30%. We also show that the proposed algorithm converges rapidly to the optimal channel interval division. Many thanks go to Dr. Lei Rao, for her time to organize weekly meeting to discuss this project and her constructive and insightful comments on my work.

Performance analysis of adaptive MAC protocol in VANETs considering the potential impact on throughput and transmission delays

“…Wang et al 33 proposed a two-directional VANET model for analyzing the performance of a broadcast with an enhanced distributed channel access mechanism in the 802.11p MAC layer, considering the hidden terminal problem and strict message priorities. Xiong et al 35 developed analytical models to explore the relationship between the traffic density, CCH packet loss ratio, SCH throughput, and duration of each type of interval. Yazid et al 34 proposed an extension of the existing Markov chain models of the IEEE 802.11e EDCA function to take into account the packet error rate and packet fragmentation.…”

“…Markov chains play a key role in calculating the optimal CCHI by predicting the number of successful WSA packet transmissions, the throughput, and the transmission delay. Markov chains have been studied using many approaches considering different aspects of the delay, throughput, and packet reception rate, as shown in Table 1 32 2013 Delay Packet reception rate 1-D and 2-D Nonsaturation and saturation Wang 33 2014 Delay Packet reception rate 1-D and 2-D Nonsaturation Yazid 34 2015 Throughput BER and 2-D Saturation packet length Xiong 35 2015 Throughput Packet loss ratio 1-D and 2-D Nonsaturation Duration of each kind of intervals Zhou 36 2016 Throughput Packet 1-D and 2-D Nonsaturation reception rate Song 37 2017 Delay Throughput 1-D and 2-D Nonsaturation and saturation Packet reception rate Abbreviations: EDCA, enhanced distributed channel access; KPI, key performance indicator.…”

“…Yazid et al 34 proposed an extension of the existing Markov chain models of the IEEE 802.11e EDCA function to take into account the packet error rate and packet fragmentation. Xiong et al 35 developed analytical models to explore the relationship between the traffic density, CCH packet loss ratio, SCH throughput, and duration of each type of interval. Zhou et al 36 proposed a more accurate model than existing models to capture the prioritized broadcast service in WAVE/IEEE 802.11p and analyzed the related performance indicators accordingly.…”

Performance Analysis of the IEEE 802.11p Multichannel MAC Protocol in Vehicular Ad Hoc Networks

“…Analysis and simulation results show that channel switching causes synchronization of backoff processes, which increases the frame collision probability, in particular for small sizes of contention windows. In [ 28 ], Xiong et al studied the relationship among the number of vehicles, packets loss ratio on the CCH, throughput of SCHs, and the duration of CCHI and SCHI. Through simulation and analysis, they found that the switch operation between fixed CCHI and SCHI cannot satisfy reliability requirement of safety applications on the CCH and high throughput requirement of non-safety applications on the SCHs.…”

Robust and Low‐Complexity Cooperative Spectrum Sensing via Low‐Rank Matrix Recovery in Cognitive Vehicular Networks

“…However, both theoretical analysis and simulation results indicate that the currently allocated bandwidth is not sufficient to provide reliable safety-related services under certain heavy traffic conditions [4][5][6][7]. The generation rate of a typical basic safety message (BSM) is from 2 to 10 messages per second to support many safety-related applications [6].…”