cDERSA: Cognitive D2D Enabled Relay Selection Algorithm to Mitigate Blind-Spots in 5G Cellular Networks

Abstract

Blind-spots, where wireless signals do not reach within the coverage range, often emerge in a dynamic environment due to obstacles, geographical location or mobility of cellular users (CUs). Thus greatly reducing the overall system performance in terms of coverage and throughput. Relay-aided cognitive Device to Device (cD2D) communication system underlying the 5G cellular network can help mitigate blind-spots. Cognitive capability helps D2D users to acquire the spectrum opportunistically for proximity communication and establish a semi-independent network underlying the 5G network, which not only offloads 5G-New Radio (NR) base station but also enhances the overall system performance. In this work, we have developed a relay-aided cognitive D2D network that helps CUs falling into the blind-spots to retain access to the 5G network and increase wireless coverage. Relay selection requires mutual consent between the relay and the device in the blind-spot. The in-coverage devices are tempted to act as relays through incentive-based mechanism. For enhanced system performance a suitable match among the devices in blind-spots and the relays is required. cD2D enabled relay selection algorithm (cDERSA) is proposed in this work, in which a cognitive D2D user (cDU), which is a CU falling in the blind-spot, establishes a relayed cD2D link to access 5G-NR gNodeB. All cDUs as well as the tempted relays, i.e. cognitive D2D relays (cDRs), first scan their surroundings for devices capable of D2D communication and based on multicriteria objective functions, build a priority table . A stable marriage problem is formulated and solved using a unique, stable, distributed, and efficient matching algorithm based on the Gale-Shapley algorithm. A new incentive mechanism is also developed to keep relays motivated to share their resources. Simulation is performed and their results show improvement in throughput and average user satisfaction, which validates our proposed cDERSA.

Optimizing 5G Power Allocation With Device-to-Device Communication: A Gale-Shapley Algorithm Approach

“…For an in-depth understanding of cognitive D2D communication, we refer to the work of A. Iqbal et al, which provides comprehensive insights into this field [21].…”

Device Discovery in D2D Communication: Scenarios and Challenges

“…The UE1 can be a user out of the coverage area, or inside a coverage area with a signal strength below a threshold. These belowthreshold areas are called blind spots and they can be inside a tunnel or basement of a building [11,40].…”

“…This ultimately results in improved system capacity and end-user satisfaction [7][8][9]. It has many applications, like proximity services, multicasting, content distribution, video dissemination, machine-to-machine (M2M) communication, video dissemination, gaming and so on [10,11].…”

D2D Communication Network Interference Coordination Scheme Based on Improved Stackelberg

“…µ m is the price parameter that each cellular subscriber CUE provides to other links (D2D subscriber multiplex communication link, relay subscriber multiplex communication link) for multiplexing. Formulas ( 12)- (14) indicate that the value of the multiplexing coefficient of the D2D users and cellular users can only take 1 or 0, that is, only cellular resources can be multiplexed or not multiplexed, and the two cannot take one at the same time, that is, D2D users and relay users cannot reuse the link resources of the same cellular user at the same time.…”

“…In the relay-assisted D2D communication network, the literature [14] first senses the surrounding D2D devices by relaying, establishes a priority table, then uses the Gale-Shapley algorithm to match resources stably and reliably, and finally establishes an incentive mechanism to weaken interference. The proposed scheme improves the system throughput and user and average satisfaction before ensuring low interference.…”