5G cellular networks are assumed to be the key enabler and infrastructure provider in ICT industry, by offering a variety of services with diverse requirements. The standardization is being expedited, which also implies more candidate technologies will adopted. Therefore, it worthwhile provide insight into techniques as whole examine design philosophy behind them. In this article, we try highlight one most fundamental features among revolutionary era, i.e., there emerges initial intelligence...

Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values. Learning long-range dependencies are embedded in time is often an obstacle for most algorithms, whereas LSTM solutions, specific kind of scheme deep learning, promise to effectively overcome the problem. In this article, we first give brief introduction structure forward propagation mechanism LSTM. Then, aiming at reducing considerable computing...

Network slicing is born as an emerging business to operators by allowing them sell the customized slices various tenants at different prices. In order provide better-performing and costefficient services, network involves challenging technical issues urgently looks forward intelligent innovations make resource management consistent with users' activities per slice. that regard, deep reinforcement learning (DRL), which focuses on how interact environment trying alternative actions reinforcing...

With the blossoming of network functions virtualization and software-defined networks, networks are becoming more agile with features like resilience, programmability, open interfaces, which help operators to launch a or service flexibility shorter time market. Recently, concept slicing has been proposed facilitate building dedicated customized logical virtualized resources. In this article, we introduce hierarchical NSaaS, helping offer end-to-end cellular as service. Moreover,...

Software defined Internet of Things (SD-IoT) networks profit from centralized management and interactive resource sharing, which enhances the efficiency scalability applications. But with rapid growth in services applications, they are vulnerable to possible attacks face severe security challenges. Intrusion detection has been widely used ensure network security, but classical methods usually signature-based or explicit-behavior-based fail detect unknown intelligently, makes it hard satisfy...

Network slicing is a key technology in 5G communications system. Its purpose to dynamically and efficiently allocate resources for diversified services with distinct requirements over common underlying physical infrastructure. Therein, demand-aware resource allocation of significant importance network slicing. In this paper, we consider scenario that contains several slices radio access base stations share the same (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL)...

A range of applications in cognitive radio networks, from adaptive spectrum sensing to predictive mobility and dynamic access, depend on our ability foresee the state evolution spectrum, raising a fundamental question: To what degree is (RSS) predictable? In this paper, we explore limits predictability RSS dynamics by studying patterns bands several popular services, including TV bands, ISM Cellular etc. From an information theory perspective, introduce methodology using statistical entropy...

Traffic learning and prediction is at the heart of evaluation performance telecommunications networks attracts a lot attention in wired broadband networks. Now, benefiting from big data cellular networks, it becomes possible to make analyses one step further into application level. In this paper, we first collect significant amount application-level traffic network operators. Afterward, with aid "big data," comprehensive study over modeling framework traffic. Our results solidly demonstrate...

Although the research on traffic prediction is an established field, most existing works have been carried out traditional wired broadband networks and rarely shed light cellular radio access (CRANs). However, with explosively growing demand for access, there urgent need to design a traffic-aware energy-efficient network architecture. In order realize such design, it becomes increasingly important model predictability theoretically discuss networking practice technically. of that...

Network slicing aims to efficiently provision diversified services with distinct requirements over the same physical infrastructure. Therein, in order allocate resources across slices, demand-aware inter-slice resource management is of significant importance. In this letter, we consider a scenario that contains several slices radio access network base stations share (e.g., bandwidth or slots). We primarily leverage advantage actor-critic (A2C), one typical deep reinforcement learning (DRL)...

Human mobility prediction is of great importance in a wide range modern applications different fields such as personalized recommendation systems, the fifth-generation (5G) mobile communication and so on. Generally, goal varies from application scenarios. For 5G network including resource allocation management, it essential to predict positions users near future dozens seconds few minutes make preparation advance, which actually trajectory problem. In this paper, with particular focus on...

Network slicing promises to provision diversified services with distinct requirements in one infrastructure. Deep reinforcement learning (e.g., deep Q-learning, DQL) is assumed be an appropriate algorithm solve the demand-aware interslice resource management issue network by regarding varying demands and allocated bandwidth as environment state action, respectively. However, allocating a finer resolution usually implies larger action space, unfortunately DQL fails quickly converge this case....

With the development of deep learning (DL), natural language processing (NLP) makes it possible for us to analyze and understand a large amount texts. Accordingly, we can achieve semantic communication in terms joint source channel coding over noisy with help NLP. However, existing method realize this goal is use fixed transformer NLP while ignoring difference information contained each sentence. To solve problem, propose new system based on Universal Transformer. Compared traditional...

Modern communications are usually designed to pursue a higher bit-level precision and fewer bits while transmitting message. This article rethinks these two major features introduces the concept advantage of semantics that characterizes new kind semantics-aware communication framework, incorporating both semantic encoding problem. After analyzing underlying defects existing techniques, we establish confidence-based distillation mechanism for joint semantics-noise coding (JSNC) problem...

This paper proposes a distributed cooperative framework for improving the energy efficiency of green cellular networks. Based on traffic load, neighboring base stations (BSs) cooperate to optimize BS switching (sleeping) strategies so as maximize saving while guaranteeing users' minimal service requirements. The inter-BS cooperation is formulated following principle ecological self-organization. An interaction graph defined capture network impact operation. Then, we formulate problem...

Recent works have validated the possibility of improving energy efficiency in radio access networks (RANs), achieved by dynamically turning on/off some base stations (BSs). In this paper, we extend research over BS switching operations, which should match up with traffic load variations. Instead depending on dynamic loads are still quite challenging to precisely forecast, firstly formulate variations as a Markov decision process. Afterwards, order foresightedly minimize consumption RANs,...

As an inevitable trend of future fifth generation (5G) networks, software-defined architecture has many advantages in providing centralised control and flexible resource management. However, it is also confronted with various security challenges potential threats emerging services technologies. the focus network security, intrusion detection systems (IDSs) are usually deployed separately without collaboration. They unable to detect novel attacks limited intelligent abilities, which hard meet...

Full-duplex (FD) communications with simultaneous transmission and reception on the same carrier have long been deemed a promising way to boost spectrum efficiency, but hindered by techniques for self-interference cancellation (SIC). Recent breakthroughs in analog digital signal processing yield feasibility of over 100 dB SIC capability, make it possible FD demonstrate nearly doubled efficiency point-to-point links. Now is time shift at least partially our focus networking, such as cellular...

Channel estimation in vehicle-to-everything (V2X) communications is a challenging issue due to the fast time-varying and non-stationary characteristics of wireless channel. To grasp complicated variations channel with limited number pilots IEEE 802.11p systems, data pilot-aided (DPA) has been widely studied. However, error propagation DPA procedure, caused by noise variation within adjacent symbols, limits performance seriously. In this letter, we propose deep learning based scheme, which...

Network slicing (NS) management devotes to providing various services meet distinct requirements over the same physical communication infrastructure and allocating resources on demands. Considering a dense cellular network scenario that contains several NS multiple base stations (BSs), it remains challenging design proper real-time inter-slice resource strategy, so as cope with frequent BS handover satisfy fluctuations of service requirements. In this paper, we propose formulate challenge...

Semantic communication has witnessed a great progress with the development of natural language processing (NLP) and deep learning (DL). Although existing semantic technologies can effectively reduce errors in interpretation, most these solutions adopt fixed bit length structure, along rigid transmission scheme, which is inefficient lacks scalability when faced different meanings signal-to-noise ratio (SNR) conditions. In this paper, we explore impact adaptive lengths on coding (SC) under...

Summary Phytophthora sojae is a destructive pathogen of soybean [ Glycine max (L.) Merr.] which causes stem and root rot on plants worldwide. However, the pathogenesis molecular mechanism plant defence responses against P. are largely unclear. Herein, we document underlying mechanisms function novel BTB/POZ protein, GmBTB/POZ, contains domain found in certain animal transcriptional regulators, host response to . It located cell nucleus transcriptionally up‐regulated by Overexpression...

We introduce a new semantic communication mechanism - SemanticRL, whose key idea is to preserve the information instead of strictly securing bit-level precision. Unlike previous methods that mainly concentrate on network or structure design, we revisit learning process and point out blindness commonly used objective functions. To address this gap, schematic shift learns from similarity, relying conventional paired supervisions like cross entropy bit error rate. However, developing such...

Large language models (LLMs) have triggered tremendous success to empower our daily life by generative information. The personalization of LLMs could further contribute their applications due better alignment with human intents. Towards personalized services, a collaborative cloud-edge methodology is promising, as it facilitates the effective orchestration heterogeneous distributed communication and computing resources. In this article, we put forward NetGPT capably synergize appropriate at...