Handoff and Radio Resource Management in Cellular Systems

Featuring edge caching and computing, fog radio access networks have been seen as promising architectures. However, the joint optimization of cache and radio resource can put heavy burden on the resource manager in the cloud. To overcome this issue, hierarchical radio and cache resource management is studied in this paper. The core idea is to fully utilize the resource management capabilities of fog access points (FAPs) to divide time-domain resource on a small timescale by participating a coalitional game while make the resource manager allocate cache resource on a large timescale to maximize a long-term utility. Based on defined FAP preference, a low-complexity and distributed coalition formation algorithm is first developed under per-FAP fronthaul capacity constraints. Then, facing the challenges incurred by no closed form, discrete variables and the curse of dimensionality, multi-agent reinforcement learning (MARL) based caching is proposed for the resource manager. In the proposal, multiple agents are created, one for each content-FAP pair, who jointly learn a caching strategy via the interaction with a history network environment. By simulation, the effectiveness of the MARL based caching is demonstrated.

The ever increasing user QoS demands and emergence of new user applications make job of network operators and manufacturers more challenging for efficiently optimisation and managing radio resources in radio the radio resources pools of different wireless networks. A group of strategies or mechanisms which are collectively responsible for efficient utilisation of radio resources available within the Radio Access Technologies (RAT) are termed as Radio Resource Management (RRM). The traditional RRM strategies are implemented independently in each RAT, as each RRM strategy considers attributes of a particular access technology. Therefore traditional RRM strategies are not suitable for heterogeneous wireless networks. Common Radio Resource Management (CRRM) or joint radio resource management (JRRM) strategies are proposed for coordinating radio resource management between multiple RATs in an improved manner. In this paper a fuzzy algorithm based CRRM strategy is presented to efficiently utilise the available radio resources in heterogeneous wireless networks. The proposed CRRM strategy balances the load in heterogeneous wireless networks and avoids the unwanted congestion situation. The results such as load distribution, packet drop rate and average throughput at mobile nodes are used to demonstrate the benefits of load balancing in heterogeneous wireless networks using proposed strategy.

The consideration of realistic uplink inter-cell interference is essential for the overall performance testing of future cellular systems, and in particular for the evaluation of the radio resource management (RRM) algorithms. Most beyond-3G communication systems employ orthogonal multiple access in uplink (SC-FDMA in LTE and OFDMA in WiMAX), and additionally rely on frequency-selective RRM (scheduling) algorithms. This makes the task of accurate modeling of uplink interference both crucial and non-trivial. Traditional methods for its modeling (e.g., via additive white Gaussian noise interference sources) are therefore proving to be ineffective to realistically model the uplink interference in the next generation cellular systems. In this article, we propose the use of realistic precomputed interference patterns for LTE uplink performance analysis and testing. The interference patterns are generated via an LTE system-level simulator for a given set of scenario parameters, such as cell configuration, user configurations, and traffic models. The generated interference patterns (some of which are made publicly available) can be employed to benchmark the performance of any LTE uplink system in both lab simulations and field trials for practical deployments. It is worth mentioning that the proposed approach can also be extended to other cellular communication systems employing OFDMA-like multiple access with frequency-selective RRM techniques. The proposed approach offers twofold advantages. First, it allows for repeatability and reproducibility of the performance analysis. This is of crucial significance not only for researchers and developers to analyze the behavior and performance of their systems, but also for the network operators to compare the performance of competing system vendors. Second, the proposed testing mechanism evades the need for deployment of multiple cells (with multiple active users in each) to achieve realistic field trials, thereby resulting in significant cost (and time) savings in the field trails.

As heterogeneous networks (HetNets) emerge as one of the most promising developments toward realizing the target specifications of Long Term Evolution (LTE) and LTE-Advanced (LTE-A) networks, radio resource management (RRM) research for such networks has, in recent times, been intensively pursued. Clearly, recent research mainly concentrates on the aspect of interference mitigation. Other RRM aspects, such as radio resource utilization, fairness, complexity, and QoS, have not been given much attention. In this paper, we aim to provide an overview of the key challenges arising from HetNets and highlight their importance. Subsequently, we present a comprehensive survey of the RRM schemes that have been studied in recent years for LTE/LTE-A HetNets, with a particular focus on those for femtocells and relay nodes. Furthermore, we classify these RRM schemes according to their underlying approaches. In addition, these RRM schemes are qualitatively analyzed and compared to each other. We also identify a number of potential research directions for future RRM development. Finally, we discuss the lack of current RRM research and the importance of multi-objective RRM studies.

The technological evolution of the wireless cellular systems has been very rapid in last two decades. In the coming decade of “converging wireless networks/systems/ecosystems”, there is an increasing demand on achieving very high data rates ubiquitously even with high mobile speeds as if we connected to a wired ADSL! Radio Resource Management (RRM) for the emerging wireless systems will be the key mechanism for achieving such high data rates. Indeed, RRM has already been a hot research area in both academia and industry for decades. And due to the complexity of the emerging wireless systems, an interdisciplinary approach and/or methodology is needed to tackle the new RRM challenges. In this paper, we provide a control engineering view onto some of the RRM challenges in emerging wireless networks, with a special emphasis on distributed power control. For example, we establish a link between power control design and dynamic neural networks, two different areas whose scope of interest, motivations and settings are completely different. Here, we emphasize the importance and the need of interdisciplinary approach. Some subjects to be addressed within the paper shall include future-generation cellular/“noncellular” systems, radio resource management challenges, energy efficiency and distributed power control algorithms, variable-structure-systems based power control, channel/frequency allocation, spectral-clustering based channel allocation, Hopfield neural networks.

This article provides an overview of radio resource management (RRM) in the context of network slicing considering a range of different deployment options and practicalities. Initially, it elaborates the 3GPP radio access network (RAN) sharing in terms of business and architecture. Slice‐tailored RRM solutions are then considered for various base station deployments focusing on RRM principles and customization, which result in different paradigms of resource sharing including full isolation, sharing, and hybrid approaches. The concept of slice broker is also elaborated, which aims to allocate radio resources related to slice requests considering the desired service performance, highlighting the need of traffic and mobility forecasting. The notion of resource sharing is further analyzed, considering various RRM and scheduling schemes including multi‐objective and hierarchical RRM, slice‐aware flexible function split, TDD service‐oriented solutions, and joint radio and backhaul optimization and licensed assisted configurations.

To optimally cover users in millimeter-Wave (mmWave) networks, clustering is needed to identify the number and direction of beams. The mobility of users motivates the need for an online clustering scheme to maintain up-to-date beams towards those clusters. Furthermore, mobility of users leads to varying patterns of clusters (i.e., users move from the coverage of one beam to another), causing dynamic traffic load per beam. As such, efficient radio resource allocation and beam management is needed to address the dynamicity that arises from mobility of users and their traffic. In this paper, we consider the coexistence of Ultra-Reliable Low-Latency Communication (URLLC) and enhanced Mobile BroadBand (eMBB) users in 5G mmWave networks and propose a Quality-of-Service (QoS) aware clustering and resource allocation scheme. Specifically, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used for online clustering of users and the selection of the number of beams. In addition, Long Short Term Memory (LSTM)-based Deep Reinforcement Learning (DRL) scheme is used for resource block allocation. The performance of the proposed scheme is compared to a baseline that uses K-means and priority-based proportional fairness for clustering and resource allocation, respectively. Our simulation results show that the proposed scheme outperforms the baseline algorithm in terms of latency, reliability, and rate of URLLC users as well as rate of eMBB users.

Radio resource management is categorized among the biggest challenges for the 5G networks due to the proliferation of heterogeneous devices. Moreover, the introduction of 5G verticals and the need to fulfill heterogeneous stringent requirements based on novel applications further complicates the radio resource management process. Network slicing can be considered as a promising scheme to meet these heterogeneous requirements produced by various 5G verticals. In network slicing, the physical resources (radio access network resources, i.e., subcarrier power, spectrum, infrastructure, etc.) are sliced into multiple logical networks based on the application requirements. In this chapter, first, we discuss about the radio access network resources. Then, we briefly elaborate on network slicing and its challenges for achieving efficient resource management. Moreover, we aim to expose the readers to the resource management approaches that can support us to build efficient network slices for 5G verticals. Finally, we provide a use case for vertical industries pertaining to the resource management in virtual reality.

Owing to the proliferation of different Radio Access Technologies (RAT) in the same operating area, a mobile user is capable of connecting concomitantly to diverse wireless networks in order to meet more easily its target performance. In this paper, we consider the downlink of a multi-class hybrid network with two RATs: WiMAX and 3G LTE. We put forward a semi-distributed Radio Resource Management (RRM) scheme for elastic traffic where both the system and mobile users intervene in the resource management policy. The proposed scheduling scheme is original in the sense that users with elastic traffic have a counterintuitive behavior: they will try to occupy the least amount possible of bandwidth to accommodate QoS stringent streaming traffic. A non-cooperative game is used to load balance the traffic of elastic users between the two available RATs aiming at minimizing their bandwidth consumption. We characterize the Nash Equilibriums (NE) of the RRM game and study the efficiency of a best response algorithm to achieve those equilibriums. Moreover, we propose a fully decentralized algorithm based on replicator dynamics to attain NEs. The system role is to apply an admission control algorithm that limits the number of ongoing connections so that elastic traffic is sustained with a guaranteed minimal rate.

Nowadays, The Long-term Evolution-advanced (LTEA) system is widely used to provide the fifth generation (5G) communication network due to its improved network capacity and less delay during the communication process. The main issues in 5G network are insufficient resources of users and Burst errors, because it creates losses in the data transmission, in order to overcome this effective radio resource management to be developed in the 5G network. In this paper the binary crow search algorithm is proposed to develop the radio resource management in the 5G network. The proposed radio resource management is used for assigning adequate power and bandwidth to the desired user equipments of the network. In radio resource management a request queue is used for avoiding the unwanted resource allocation in the network. Moreover, the losses during the Transmission are minimized by using frequency Interleaving and Guard level Insertion. The performance of the proposed system is analyzed in terms of bit rate and sum rate.KeywordsQuality-of-Service (QoS)Long-term evolution (LTE)Data rateRadio resource organization approach (RROA)Long-term evolution-advanced (LTE-A)

This dissertation is addressed to novel and high innovative technical solutions for broadband wireless access (BWA) networks.The proposed networks are compatible with the existing IEEE 802.11n wireless local area networks (WLANs), wireless metropolitan area networks (MANs) IEEE 802.16e and WiMAX systems(Worldwide Interoperability for Microwave Access).The proposed designs are linked to the specific requirements which will be incorporated into emerging IEEE 802.16m and LTE advanced (Long Term Evolution) standards.These standards aim at defining an air interface that can ensure high mobility, transmit high data rates and offer higher performance compared to the current standards.The dissertation focuses on advanced radio resource allocation schemes involving cross-layer optimization and hopes to influence these future standards.Radio Resource Management (RRM) algorithms optimize the radio resources especially spectral efficiency and reduce the power consumptions.This work studies mechanisms for downlink (DL) RRM in an environment which suffers from interference created by neighboring transmitters.Beamforming, scheduling and admission control are evaluated.After the description of state-of the-art techniques, advanced algorithms -strategies are developed and simulated.These algorithms take information coming from the radio site (RSSI, CSI, SNR, etc) or from the application layer side (QoS).

The efficiency of a WiFi system with dozens of base stations in relatively small physical area is determined by the optimal allocation of the radio channels to the mobile devices. Based on the increased penetration rate of the high traffic capable smartphones and accentuated usage of these devices in densely populated buildings intelligent hardware tools are needed to offer QoS level to the users. The Radio Resource Management (RRM) of IEEE 802.11 network is provided by a wireless LAN controller. This node maintains an allocated control connection to each of the base stations providing enhanced quality level of the WiFi hot zone services. Modern base stations have the capability to listen to the radio channels periodically to detect the received signal intensity. The controller samples periodically each of the radio channels without affecting the own radio frame processing and collects in this way radio resource usage. Based on specific criteria like the number of active nodes, traffic intensity, interference, noise intensity the periodically executed RRM management algorithm modifies the distribution of active channels on the supervised hot zone level. The radio signal intensity scanning task is considered to be performed by the base stations as a sampling process of individual sensors distributed in physical space. Eighteen WiFi access points with thirteen channel sensors in 2.4 GHz range and sixteen channel sensors in 5 GHz range were used to capture radio signal intensity in a densely populated building. Holding special criteria in the scanned signal intensity values is considered as a complex event. Clustering method based on similarity measure was used to analyse sensor grouping behavior of the wireless controller RRM algorithm. Our work is focused on the usability of different statistical metrics (i.e. Hurst, Davies-Bouldin, etc.) to characterize intrusion detection efficiency of the wireless LAN controller.

The evolving small-cell (originally femtocell) networks are envisioned to provide improved capacity and coverage in next generation cellular systems. Small-cell deployments pose many challenges among which radio resource management (i.e., interference management, resource allocation, and power control) is the most significant one. The aim of this talk is to provide an overview of the radio resource management problem in small-cell networks considering OFDMA (e.g., LTE, WiMAX) femtocells, and the ongoing research on this topic. After a brief overview of the small-cell networks, we outline the major challenges in successful deployment of small-cells in next generation cellular wireless systems. In particular, the challenges related to resource allocation, co-tier/cross-tier interference management and power control are discussed. Then we provide a comprehensive overview of the different proposed techniques for resource allocation and interference management in small-cell networks. In this context, two different approaches are described: one based on clustering and semi-centralized computations, and one based on cooperative game theory. The requirements for strategic interference management via power control and resource allocation mechanisms are then discussed.

This paper discusses load balancing architecture among heterogeneous wireless networks (HWNs). Load balancing is an important issue for efficient use of radio resources not only in a single network but among different radio access technologies (RATs) in HWNs. Cooperative management of radio resources among heterogeneous RATs is considered to reduce imbalance of utilization of radio resources and enhance availability of the system. For effective load balancing, we define a ldquocommunityrdquo concept, in which radio resources of various RATs are managed together. Radio resource management functional entities of RATs cooperatively work in the community. We present a load balancing architecture applied to 3GPP LTE, WiBro and WLAN and describe architectural considerations. We also present a straightforward load balancing algorithm that takes account of bandwidth usage and received signal to noise ratio. The load balancing algorithm of community radio resource management enhances utilization of radio resources and reduces call blocking probability of individual RATs in the community and further enhances average performance of the whole community.

In the fifth generation (5G) of mobile broadband systems, radio resource management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated RRM functionalities and the growing variety of scenarios, while carrying out the prompt decisions required in 5G, this manuscript presents a lean RRM architecture that capitalizes on recent advances in the field of machine learning in combination with the large amount of data readily available in the network from measurements and system observations. The architecture consists of a learner (or a few), which learns RRM policies directly from the data gathered in the network using a single general-purpose learning framework, and a set of distributed actors, which execute RRM policies issued by the learner and repeatedly generate samples of experience. Thus, the complexity of RRM is shifted to the design of the learning framework, while the RRM algorithms derived from this framework are executed in a computationally efficient distributed manner at the radio access nodes. The potential of this approach is verified in a pair of pertinent scenarios, and future directions on applications of machine learning to RRM are discussed. Although we focus on a mobile broadband context, the concepts proposed hereafter extend to any radio access network technology where one can conceive the idea of a central learning unit gathering data from distributed actors.