Network slicing is a key paradigm in 5G and expected to be inherited future 6G networks for the concurrent provisioning of diverse quality service (QoS). Unfortunately, effective Radio Access Networks (RAN) still challenging due time-varying network situations. This paper proposes new intelligent RAN strategy with two-layered control granularity, which aims at maximizing both long-term QoS services spectrum efficiency (SE) slices. The proposed method consists an upper-level controller ensure...

The unprecedented growth of mobile video traffic is adding significant pressure to the energy drain at both network and end user. Energy-efficient transmission techniques are thus imperative cope with challenge satisfying user demand sustainable costs. In this paper, we investigate how predicted rates can be exploited for energy-efficient streaming popular Hypertext Transfer Protocol (HTTP)-based adaptive (AS) protocols [e.g., dynamic over HTTP (DASH)]. To end, develop an predictive green...

The advances in wireless communication schemes, mobile cloud and fog computing, context-aware services boost a growing interest the design, development, deployment of driver behavior models for emerging applications. Despite progressive advancements various aspects modeling (DBM), only limited work can be found that reviews body literature, which targets subset DBM processes. Thus more general review diverse DBM, with an emphasis on most recent developments, is needed. In this paper, we...

Over-the-air federated learning (OTA-FL) is a distributed machine technique where multiple devices collaboratively train shared model without sharing their raw data with central server. The exchange updates concurrently over-the-air and they are aggregated the need for dedicated wireless resources each device. A major challenge in OTA-FL that edge limited computation, energy, communication resources. To address this, we investigate how deep neural network compression techniques can be...

Predictive resource allocation (PRA) techniques that exploit knowledge of the future signal strength along roads have recently been recognized as promising approaches to save base station (BS) energy and improve user quality service (QoS). Recent studies on human mobility patterns wireless measurements buses trains indeed supported practical potential PRA. An unresolved challenge, however, is modeling uncertainty in predictions, developing real-time robust solutions incorporate probabilistic...

Connected and autonomous vehicles (AVs) will need to communicate significant amounts of data provide various safety, navigation infotainment services consumers. This article discusses the recent technological advances standardization efforts in Cellular Vehicle-to-Everything (C-V2X) technologies that are being developed support ultra-reliable, low latency, high throughput required by AVs. The key V2X applications their communication requirements, with an emphasis on video delivery, then...

To meet the extremely stringent but diverse requirements of 5G, cost-effective network deployment and traffic-aware adaptive utilization resources are becoming essential. In this paper, a hotspot prediction based virtual small cell (VSC) operation scheme is adopted to improve both cost efficiency operational 5G networks. This paper focuses on how predict hotspots by using deep learning, then demonstrates predictions can be leveraged support beamforming VSC operation. We first leverage...

The ever increasing mobile data traffic and dense deployment of wireless networks have made energy efficient radio access imperative. As are designed to satisfy peak user demands, can be reduced in a number ways at times lower demand. This includes putting base stations (BSs) intermittent short sleep modes during low load, as well adaptively powering down select BSs completely where demand is for prolonged time periods. In order fully exploit such conserving mechanisms, should aware the...

Resource Allocation (RA) in cellular networks is a challenging problem due to the demanding user requirements and limited network resources. Moreover, mobility results channel gains that vary significantly with time. However, since location received signal strength are correlated, patterns can be exploited predict data rates they will experience future. In this paper, we show such predictions, long-term RA plans span multiple cells made. We formulate an optimal Predictive (PRA) framework for...

Cloud virtual reality (VR) gaming traffic characteristics such as frame size, inter-arrival time, and latency need to be carefully studied a first step toward scalable VR cloud service provisioning. To this end, in paper we analyze the behavior of Quality Service (QoS) when rendering is conducted remotely cloud. We build testbed utilizing server, commercial headset, an off-the-shelf WiFi router. Using testbed, collect process data from different games under number network conditions fixed...

Deep learning techniques have recently emerged to efficiently manage mmWave beam transmissions without requiring time consuming sweeping strategies. A fundamental challenge in these methods is their dependency on hardware-specific training data and limited ability generalize. Large drops performance are reported literature when DL models trained one antenna environment applied another. This paper proposes the application of Prototypical Networks address this utilizes DeepBeam real-world...

Mobile media has undoubtedly become the predominant source of traffic in wireless networks. The result is not only congestion and poor quality experience, but also an unprecedented energy drain at both network user devices. In order to sustain this continued growth, novel disruptive paradigms delivery are urgently needed. We envision that two key contemporary advancements can be leveraged develop greener platforms: proliferation navigation hardware software mobile devices created era...

The ever-growing wireless applications and their diverse Quality of Service (QoS) requirements bring the challenge tailored QoS provisioning with limited radio resources in future cellular networks. While resource constraint is ubiquitous, different communication equipment networks could experience very constraints multi-dimensional domains. To achieve stringent yet resources, a novel intelligent multiple access (MD-IMA) scheme proposed this paper to exploit disparate among heterogeneous for...

The past decade has witnessed a staggering evolution in cellular networks. Mobile wireless technologies have undergone four distinct generations; from uncomplicated voice calls the first generation to high-speed, low latency and video streaming fourth generation. numerous services brought users by 4G network caused an increasing load demand. This demand usage proven necessity of further service enhancements, such as predictive resource allocation techniques handover analysis. For these be...

To meet the ever-increasing communication services with diverse requirements, situation-aware intelligent utilization of multi-dimensional resources is becoming essential. In this paper, considering a time-division-duplex downlink cellular scenario, deep learning-based framework for multiple access (MD-IMA) scheme developed beyond 5G and 6G wireless networks to real-time quality service (QoS) requirements by fully utilizing available radio in heterogeneous domains. achieve operation MD-IMA,...

The open radio access network (O-RAN) architecture supports intelligent control algorithms as one of its core capabilities. Data-driven applications incorporate such to optimize (RAN) functions via RAN controllers (RICs). Deep reinforcement learning (DRL) are among the main approaches adopted in O-RAN literature solve dynamic resource management problems. However, despite benefits introduced by RICs, practical adoption DRL real deployments falls behind. This is primarily due slow convergence...

Reinforcement Learning (RL) algorithms have recently been proposed to solve dynamic radio resource management (RRM) problems in beyond 5G networks. However, RL-based solutions are still not widely adopted commercial cellular One of the primary reasons for this is slow convergence RL agents when they deployed a live network and network's context changes significantly. Concurrently, open access (O-RAN) paradigm promises give mobile operators (MNOs) more control over their networks, furthering...

Small cell deployments have proven to be a cost-effective solution meet the ever growing capacity and coverage requirements of mobile networks. While small cells are commonly deployed indoors, more recently outdoor roll-outs garnered industry interest complement existing macrocell infrastructure. However, problem where when deploy these remains challenge. In this paper, we investigate base station (SBS) placement in high demand environments. First, propose dynamic strategy (DPS) that...

As one of the key technologies in 5G networks, Carrier Aggregation (CA) is studied this paper. In CA, Component Carriers (CCs) can be activated and deactivated depending on multiple factors, e.g., energy consumption Quality Service (QoS) demand users. We propose CC management strategies where each User Equipment (UE) minimizes its average delay at same time power while considering that CCs only certain times, as real-world CA implementations. first model problem a centralized multi-objective...

Novel mobility-aware resource allocation schemes have recently been introduced for efficient transmission of stored videos. The essence such mechanisms is to lookahead at the future rates users will experience, and then strategically buffer content into user devices when they are peak radio conditions. For example, a approaching poor coverage be preallocated additional video segments ensure smooth streaming. Advances in mobility prediction real-time environment map updates driving forces...

Predictive resource allocations (PRAs) have recently gained attention in wireless network literature due to their significant energy-savings and quality of service (QoS) gains. This enhanced performance was primarily demonstrated while assuming the perfect prediction both mobility traces anticipated channel rates. While results are very promising, several technical challenges need be overcome before PRAs can practically adopted. Techniques that model uncertainty provide probabilistic...

Deep Reinforcement Learning (DRL) algorithms have been recently proposed to solve dynamic Radio Resource Management (RRM) problems in 5G networks. However, the slow convergence experienced by traditional DRL agents puts many doubts on their practical adoption cellular In this paper, we first discuss need accelerated algorithms. We then analyze exploration behavior of various state-of-the-art for slice resource allocation, and compare it with Access Network (RAN) slicing baselines. Finally,...

Emerging mobility-aware content delivery approaches are being proposed to cope with the increasing usage of data from vehicular users. The main idea is forecast user locations and associated link capacity, then proactively counter service fluctuations in advance. For instance, a that heading towards low coverage can be prioritized have video prebuffered. While reported gains encouraging, results primarily based on assumptions perfect prediction. Investigating predictability mobility future...

Mobile network traffic is increasing in an unprecedented manner, resulting growing demand from operators to deploy more base stations able serve devices while maintaining a satisfactory level of service quality. Base are considered the leading energy consumer infrastructure; consequently, number will increase power consumption. By predicting load on stations, optimization techniques can be applied decrease This research explores different machine learning and statistical methods capable...