Distributed and Scalable Radio Resource Management for mmWave V2V Relays towards Safe Automated Driving

The millimeter-wave (mmWave) Vehicle-to-Vehicle (V2V) communication system has drawn attention as a critical technology to extend the restricted perception of onboard sensors and upgrade the level of vehicular safety that requires a high data rate. However, co-channel inter-link interference presents significant challenges for scalable V2V communications. To overcome such limitations, this paper firstly analyzes the required data rate ensuring maneuver safety via mmWave V2V relays in an overtaking traffic scenario. Based on these preparations, we propose a distributed radio resource management scheme that integrates spatial, frequency, and power domains for two transmission ranges (short/long). In the spatial domain, ZigZag antenna configuration is utilized to mitigate the interference, which plays a decisive role in the short inter-vehicle distance. In frequency and power domains, two resource blocks are allocated alternately, and transmit power is controlled to suppress the interference, which has a decisive impact on interference mitigation in the long inter-vehicle distance. Simulation results reveal that the achievable End-to-End (E2E) throughput maintains consistently higher than the required data rate for all vehicles. Most importantly, it works effectively in scalable mmWave V2V topology.

Efficient radio resource allocation scheme for 5G networks with device-to-device communication

<p>A vital technology in the next-generation cellular network is device-to-device (D2D) communication. Cellular user enabled with D2D communication provides high spectral efficiency and further increases the coverage area of the cell, especially for the end-cell users and blind spot areas. However, the implementation of D2D communication increases interference among the cellular and D2D users. In this paper, we proposed a radio resource allocation (RRA) algorithm to manage the interference using fractional frequency reuse (FFR) scheme and Hungarian algorithm. The proposed algorithm is divided into three parts. First, the FFR scheme allocates different frequency bands among the cell (inner and outer region) for both the cellular and the D2D users to reduce the interference. Second, the Hungarian weighted bipartite matching algorithm is used to allocate the resources to D2D users with the minimum total system interference, while maintaining the total system sum rate. The cellular users share the resources with more than one D2D pair. Lastly, the local search technique of swapping is used for further allocation to minimize the interference. We implemented two types of assignments, fair multiple assignment, and restricted multiple assignment. We compared our results with existing algorithms which verified that our proposed algorithm provides outstanding results in aspects like interference reduction and system sum rate. For restricted multiple assignment, 60-70% of the D2D users are allocated in average cases.</p>

A Deep Learning Based Automatic Interference Avoidance Resource Allocation Scheme for SCMA Systems

Sparse code multiple access (SCMA) is able to provide high spectral efficiency and massive connectivity, hence it is considered as a promising scheme for the fifth generation (5G) systems. This paper proposed a radio resource allocation scheme based on deep learning for SCMA systems, with the aim to automatically avoid the inter-cell interference. A long short term memory (LSTM) network is adopted to learn the past interference characteristics and predict the interference power in the current subframe. Radio resource blocks with less predicted interference power are then selected for users to transmit signals. Simulation results show that the proposed scheme outperforms the moving average prediction method and has significant gains over the random radio resource block allocation in terms of achievable bit error rate in SCMA systems.