Reliable and enhanced cooperative cross‐layer medium access control scheme for vehicular communication

Abstract

In an unreliable cluster-based, broadcast vehicular network setting, we investigate the transmission reliability and throughput performance of random network coding (RNC) as a function of the percentage of packet generation rate and transmit power to noise ratio. In the paper, a novel scheme called reliable and efficient cooperative cross-layer MAC (RECMAC) is proposed. The proposed scheme consists of a source vehicle broadcasting packets to a set of receivers (i.e. one-to-many) over independent broadcast erasure channels. The source vehicle performs RNC on N packets and broadcasts the encoded message to a set of receivers. In each hop, several vehicles form a cluster and cooperatively transmit the encoded or re-encoded packet. The combination of RNC, cluster based, and cooperative communications enables RECMAC to optimally minimize data redundancy, which means less overhead, and improve reliability as opposed to coding-based solutions. Theoretical analyses and simulation results show that under the same conditions RECMAC scheme can achieve improved performance in terms of transmission reliability and throughput.

Design Optimization of Resource Allocation in OFDMA-based Cognitive Radio-enabled Internet of Vehicles (IoVs)

“…The average life span of CRV-SUs' cluster formation [1] is the total duration when all the ℝ CRV-SUs in a particular cluster (usually presumed to be exponentially distributed) maintain membership of the same CC [19]. Therefore, the CRV-SUs' cluster formation mobility can be modeled using a transition rate (i.e., speed) matrix Ɱ and given by Equation (1 (1) where = | | denotes the number of locations in a service area, | | represents the cardinality of set , and the element ɱ( , ′ ) represents the rate at which the CRV-SUs' cluster formation changes from ℒ to ℒ ′ location.…”

“…In a shared channel, the activity of the PUs is modeled through a two-state Markov chain (see Equation (2)), such as the ON-OFF model, which corresponds to the busy and idle states, respectively. Consequently, a transition probability matrix is used to model the state transition of shared channel , as expressed below: ̃= [̃( 0, 0)̃(0, 1) (1, 0)̃ (1,1) ] ← idle ← busy (2) where 0 and 1 represent the idle and busy states, respectively. Therefore, the probability of the shared wireless channel m being in an idle state can be obtained from the expression = 1 −̃(1, 1) ((̃(0, 1) −̃(1, 1) + 1)) ⁄ .…”

“…The average life span of CRV-SUs' cluster formation [1] is the total duration when all the R CRV-SUs in a particular cluster (usually presumed to be exponentially distributed) maintain membership of the same CC [19]. Therefore, the CRV-SUs' cluster formation mobility can be modeled using a transition rate (i.e., speed) matrix multicarrier OFDMA technology can lead to improvements of the spectral efficiency, as well as the robustness needed when dealing with time-varying wireless multi-path interferences, which is likely the case with vehicular networks [14].…”

“…The average life span of CRV-SUs' cluster formation [1] is the total duration when all the ℝ CRV-SUs in a particular cluster (usually presumed to be exponentially distributed) maintain membership of the same CC [19]. Therefore, the CRV-SUs' cluster formation mobility can be modeled using a transition rate (i.e., speed) matrix Ɱ and given by Equation (1)…”

“…In the near future, most vehicles are expected to be equipped with wireless communication technologies, such as On-Board Units (OBUs), and ultrasonic sensors to enable a variety of new services, such as safety applications, improved traffic management, and enhanced infotainment services [1]. Therefore, the vehicular ad hoc network (VANET) has gained an increased importance, receiving a great amount of attention from academia, auto-manufacturing industries, and government agencies.…”