Optimal Radio Resource Management Research Articles

Optimal radio resource management in 5G NR featuring network slicing

3GPP 5G New Radio (NR) has introduced several new features that the network slicing concept can leverage to guarantee the heterogeneous requirements in terms of throughput and delays of expected 5G network services. Mainly, these features are (i) Mixed-numerology to control the time slot duration, hence guaranteeing low latency requirements; (ii) Bandwidth Parts (BWP) to control the number of radio resources allocated to users, hence satisfying different throughput requirements. However, efficient radio resource management is already complex, and adding these new dimensions will further increase this complexity. In this paper, we first propose modeling radio resource management in 5G NR featuring network slicing through a Mixed Integer Linear Program (MILP). For our best knowledge, this is the first MILP modeling of the radio resource management featuring network slicing taking into account (i) Mixed-numerology, (ii) both latency and throughput requirements (iii) multiple slice attach per UE (iv) Inter-Numerology Interference (INI). After showing that solving the problem takes an exponential time, we considered a new approach to solve it in a polynomial time, which is highly required when scheduling radio resources. The new approach consists of formalizing this problem using a Deep Reinforcement Learning (DRL)-based solver. We evaluate the use of RL to solve the problem for different network configurations and compared its performance with the optimal solution obtained by solving the MILP problem.

Trajectory Mining‐Based City‐Level Mobility Model for 5G NB‐IoT Networks

Due to the large coverage of 5G NB‐IoT networks, a more realistic mobility model for a macroscopic scene will greatly facilitate the development of optimal radio resource management algorithms. However, models devised for a random motion scene are no longer applicable in circumstances. Therefore, in this paper, a city‐level mobility model is proposed based on the feature mining of the real trajectory of vehicles in the city of Shenzhen. The proposed model is separately designed in the motion trajectory to reduce the mutual influence between the time and spatial sequence. Simulation results show that it can better present specific node motions with the physical constraints of the city layout, which are motivated with a high degree of fit in terms of self‐similarity, hotspots, and long‐tail features.

Optimizing Live Layered Video Multicasting Over LTE With Mobile Edge Computing

Live video streaming has become one of the key applications in mobile wireless networks. To offload the bandwidth requirement in both backhaul and radio access networks, the integration of Mobile Edge Computing (MEC) and multicasting have become a natural candidate. However, less attention has been paid to the user Quality of Experience-driven (QoE-driven) approach to optimize the radio resource management of multicasting in mobile wireless networks. In this work, we study the optimal radio resource management, including modulation and coding scheme (MCS) selection, radio resource blocks allocation, and Forward Error Correction (FEC), for multicasting in LTE networks with the assistance of MEC. We formulate it as a convex optimization problem and propose a weighted sub-gradient (WSG) method to find the near-optimal solution. In addition, we also propose a heuristic algorithm based on the concept of Maximizing marginal Gain and Minimizing marginal Loss (MGML). Our simulation results show that both approaches are able to achieve near-optimal solutions and outperform previous work, including MSML [11] and OLM [14] . Our simulation results also show that WSG yields the best QoE fairness index while MGML yields the best system utility in most scenarios.

An RSS-Based Classification of User Equipment Usage in Indoor Millimeter Wave Wireless Networks Using Machine Learning

The use of millimeter wave technologies offer a promising solution for dense small cell networks, despite having to contend with challenging propagation characteristics. In particular, user-induced effects can lead to significant channel variations depending on the user equipment (UE) usage mode which in turn, can impact the quality of service. Estimation of UE operating conditions is therefore critical for optimal radio resource management. We propose a new approach to user activity recognition which makes use of both supervised and unsupervised machine learning. In particular, using information extracted from the received signal strength (RSS), a common metric readily available from many receiver chipsets, we perform a classification of user state ( static or mobile relative to an access point) and UE mode of operation ( voice call, using an app or in pocket ). To develop and then train our classification system, measured RSS data was obtained using a custom 60 GHz measurement system for a range of indoor office scenarios which considered various UE to ceiling mounted access point configurations. In our approach, differentiation between static and mobile states is performed in preprocessing using a k-means algorithm. Small-scale fading features are then estimated from the RSS data and, using different feature scaling mechanisms, various supervised learning approaches are applied to investigate the optimal classification accuracy for the considered use cases in this work. We compare the classification performance of various window sizes and types, and show that a sliding window length of 1s without overlap performs best for time series segmentation at 60 GHz for the activities considered in this study. Among the different supervised learning approaches, the Decision Tree (DT) classifier performs best for both the user static and mobile cases with an accuracy of 100% and 98.0%, respectively. For static cases, user orientation, i.e., line-of-sight (LOS), quasi-LOS, and non-LOS, can also be classified and here the DT classifier also performs best with an accuracy of 98.2%, 97.6% and 100% for the voice call , using an app or in pocket use cases. Additionally, a feature ranking algorithm, called ReliefF , is adopted to determine the small-scale fading features that have the most significant influences on the classification accuracy and three different feature sets, namely Full , Reduced and Constrained sets, are then proposed based on feature ranking results. This allows the proposed techniques to be deployed on wireless platforms with different levels of processing capability.

Power and Channel Optimization for WiFi Networks Based on REM Data

The very high commercial exploitation of the WiFi based technologies in recent years and the absence of solutions for optimal WiFi orchestration, usually leads to suboptimal spectrum usage and user performances. The combination of WiFi based radio resource management (RRM) and the radio environmental maps (REMs) can provide an efficient solution for a Smart-WiFi technology, which improves the underlying spectrum usage as well as network performance. The REM facilitates efficient utilization of the radio environmental data, like device location, estimated channel models, real-time interference levels between the networks, WiFi channels occupancies etc. This information can be utilized for an intelligent and optimal RRM decision making in WiFi related scenarios. This paper proposes a novel REM based RRM approach for management and optimization of commercial WiFi devices that utilizes the available underlying radio environmental information. The paper demonstrates the proposed approach on a commercially available platform, conducting on-the-fly radio environmental data acquisition and optimized WiFi RRM allocation. The simulation analysis results also show that the proposed Smart-WiFi leverage noticeable performance gains for large scale scenarios, compared to conventional WiFi networks.

Enhancing interoperability in heterogeneous mobile wireless networks for disaster response

Efficient communications are crucial for disaster response and recovery. However, most current public safety land mobile radio (LMR) networks only provide narrowband voice service with limited support of low-speed data services. In this paper, we study to enhance the interoperability of LMR with commercial wireless cellular networks, by which a wide variety of benefits can be offered to disaster responders, including new multimedia services, increased data rates and low cost devices. Our approach is based on Session Initiation Protocol (SIP) and a joint radio resource management framework. We use a novel SIP- based seamless handoff scheme to support the interoperability between cellular and LMR networks. In addition, an optimal radio resource management scheme is proposed to maximize the overall radio resource utilization and at the same time guarantee service availability and continuity quality of service (QoS) for disaster responders. The effectiveness of the proposed schemes is illustrated by numerical examples.