Autonomous Interface Selection for Multi-Radio D2D Communication

Abstract

Device-to-device communications are considered as a key feature to enhance the performance of the fifth generation (5G) wireless networks. Several radio access technologies such as LTE Direct, Bluetooth, Wi-Fi, and ZigBee are expected to provide the opportunity of D2D communications. Therefore, it is possible to choose any of them autonomously to establish a D2D link. The primary focus of this work is to investigate the radio interface selection, where end users select an interface opportunistically among different available radio interfaces to establish outband D2D connectivity against interference. We model a non-cooperative game to select a radio interface for D2D users to minimize their communication cost. We have investigated Nash equilibrium in the game and argue that without any cooperation users can achieve a balanced strategy. In our model, each pair selects a radio interface based on a utility function that associates communication performance and cost. Finally, we propose three heuristic algorithms: Social, Greedy, and Local, that achieve Nash equilibrium with different information. Event-driven simulation experiments are then conducted to evaluate the utility and cost of the equilibrium strategy. Our results confirm that the proposed schemes can increase the utility, lower the cost, and lead to higher efficiency in terms of achievable throughput per consumed energy. INDEX TERMS D2D communications, game theory, multiple radio interfaces, Nash equilibrium. I. INTRODUCTION Wireless communications have boosted the opportunity for smart devices with a number of standards and technologies. Smart devices are now the most important computing and communication platform. In previous years wireless connectivity was only possible with a single operator/ technology. However, these days smart devices are capable of multiple wireless opportunities. These end users are often equipped with multiple radio interfaces (i.e., 3G/LTE, Bluetooth, Zigbee, and Wi-Fi), which complements their cellular communication capabilities. According to a recent market research report, 70% of the mobile phones have Bluetooth interface, while 80% are enabled WiFi [1]. The proliferation of smart devices and exponential demand of bandwidth have created spacious performance requirement on the future wireless networks [2]. Device-to-device communication (D2D) [3] is considered one of the major technology to enhance the boosting demand [4] of users. Motivated by the performance gain, many telecommunication The associate editor coordinating the review of this manuscript and approving it for publication was Fang Yang.

MCDM-based RAT Selection method between LTE-D2D and IEEE 802.11

“…These data are statistic samples recorded and timestamped in background, either through data traffic or through a periodic control traffic. A coefficient of variation (cv) 1 of the network criterion is calculated from its statistic samples to update its DLT .…”

“…This allows them to have multiple options for establishing communications for their diversified applications. For example, LTE D2D (or LTE direct) outperforms Wi-Fi direct for throughput intensive applications, while Wi-Fi direct outperforms LTE direct in the case of application with low data generation rate [1]. In such heterogeneous networks, answering how should a user select an access technology at a given time while guaranteeing application needs, and leading to efficient utilization of network resources is an open research area.…”

“…Previous works [9,10] on D2D communications for interface selection have considered mainly resource allocation and power control to select different technologies without considering distinct interference to neighbor users. This issue is considered in [1] during the interface selection process which is formulated as game theory issue. While the energy efficiency of the proposal is shown compared to legacy transmissions, it lacks consideration of QoS constraints of enduser applications in the utility decision function.…”

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

“…The star topology of cellular networks with a centralized control point, e.g., a BS or AP, suffers inefficiencies as all communication has to be relayed by the centralized control point. In contrast, D2D communication is a radio technology that enables direct data exchanges between two adjacent UEs without the involvement of the central control point or core network of the cellular network, i.e., without traversing the BS or AP [12]- [15]. This direct D2D communication brings several benefits, such as improved spectral efficiency, increased data rates between devices, reduced power consumption, and reduced end-to-end delay.…”

“…Effectively, the end devices share their resources and collaborate with the MEC to quickly provide computeand data-intensive services to their fellow end devices. This sharing among end devices is facilitated by recent advances in Device-to-Device (D2D) communication [12]- [15]. D2D communication enables an end device to exploit the resources of the end devices in its proximity via direct D2D connections, as illustrated by the red links in the right half of Figure 1; thus, potentially reducing the traffic load on the cellular network and MEC infrastructure.…”