Modeling and Performance Analysis of UAV-Aided Millimeter Wave Cellular Networks with Stochastic Geometry

UAV-aided cellular networks,millimeter wave(mm-wave) communications and multi-antenna techniques are viewed as promising components of the solution for beyond-5G(B5G) and even 6G communications.By leveraging the power of stochastic geometry,this paper aims at providing an effective framework for modeling and analyzing a UAV-aided heterogeneous cellular network,where the terrestrial base stations(TBSs) and the UAV base stations(UBSs) coexist,and the UBSs are provided with mm-wave and multi-antenna techniques.By modeling the TBSs as a PPP and the UBSs as a Matern hard-core point process of type Ⅱ(MPH-Ⅱ),approximated but accurate analytical results for the average rate of the typical user of both tiers are derived through an approximation method based on the mean interference-to-signal ratio(MISR) gain.The influence of some relevant parameters is discussed in detail,and some insights into the network deployment and optimization are revealed.Numerical results show that some trade-offs are worthy of being considered,such as the antenna array size,the altitude of the UAVs and the power control factor of the UBSs.

Resource Allocation in Multi-User Cellular Networks:A Transformer-Based Deep Reinforcement Learning Approach

To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.

Outage Probability Analysis for D2D-Enabled Heterogeneous Cellular Networks with Exclusion Zone:A Stochastic Geometry Approach

Interference management is one of the most important issues in the device-to-device(D2D)-enabled heterogeneous cellular networks(HetCNets)due to the coexistence of massive cellular and D2D devices in which D2D devices reuse the cellular spectrum.To alleviate the interference,an efficient interference management way is to set exclusion zones around the cellular receivers.In this paper,we adopt a stochastic geometry approach to analyze the outage probabilities of cellular and D2D users in the D2D-enabled HetCNets.The main difficulties contain three aspects:1)how to model the location randomness of base stations,cellular and D2D users in practical networks;2)how to capture the randomness and interrelation of cellular and D2D transmissions due to the existence of random exclusion zones;3)how to characterize the different types of interference and their impacts on the outage probabilities of cellular and D2D users.We then run extensive Monte-Carlo simulations which manifest that our theoretical model is very accurate.

Distributionally robust optimization based chance-constrained energy management for hybrid energy powered cellular networks

Energy harvesting has been recognized as a promising technique with which to effectively reduce carbon emis-sions and electricity expenses of base stations.However,renewable energy is inherently stochastic and inter-mittent,imposing formidable challenges on reliably satisfying users'time-varying wireless traffic demands.In addition,the probability distribution of the renewable energy or users’wireless traffic demand is not always fully known in practice.In this paper,we minimize the total energy cost of a hybrid-energy-powered cellular network by jointly optimizing the energy sharing among base stations,the battery charging and discharging rates,and the energy purchased from the grid under the constraint of a limited battery size at each base station.In solving the formulated non-convex chance-constrained stochastic optimization problem,a new ambiguity set is built to characterize the uncertainties in the renewable energy and wireless traffic demands according to interval sets of the mean and covariance.Using this ambiguity set,the original optimization problem is transformed into a more tractable second-order cone programming problem by exploiting the distributionally robust optimization approach.Furthermore,a low-complexity distributionally robust chance-constrained energy management algo-rithm,which requires only interval sets of the mean and covariance of stochastic parameters,is proposed.The results of extensive simulation are presented to demonstrate that the proposed algorithm outperforms existing methods in terms of the computational complexity,energy cost,and reliability.

In Band Full Duplex (IBFD) Technology for Next Generation Wireless Networks: A Survey in Cellular Networks

Next-Generation(NextG)wireless communication networks with their widespread applications require high data rates,seamless connectivity and high quality of service(QoS).To cope up with an unprecedented rise of data hungry applications,users demand more spectral resources imposing a limitation on available wireless spectrum.One of the potential solutions to address the spectrum scarce issue is to incorporate in band full duplex(IBFD)or full duplex(FD)paradigm in next generation networks including 5G new radio(NR).Recently,FD has gained the research interest in cellular networks for its potential to double the wireless link capacity and enhancing spectral efficiency(SE).In half duplex(HD)cellular networks,base stations(BSs)can either perform uplink(UL)or downlink(DL)transmission at a particular time instant leading to reduced throughput levels.Due to the advancement in the self interference reduction(SIR)techniques,full duplex base stations(FD-BSs)can be employed to allow simultaneous UL and DL transmissions at the same time–frequency resources as compared to its HD counterpart.It ideally achieves twice the throughput without any additional complexity at user-equipment(UE).This paper covers a detailed survey on FD cellular networks.A series of SIR approaches,UE-UE mitigation techniques are summarized.Various existing MAC protocols and antenna architectures for FD cellular networks are outlined.An overview of security aspects for FD in cellular networks is also presented.Lastly,various open issues and possible research directions are brought up for FD cellular networks.

Uplink Performance Analysis in Multi-Tier Heterogeneous Cellular Networks with Power Control and Biased User Association

A K-tier uplink heterogeneous cellular network is modelled and analysed by accounting for both truncated channel inversion power control and biased user association. Each user has a maximum transmit power constraint and transmits data when it has sufficient transmit power to perform channel inversion. With biased user association, each user is associated with a base station(BS) that provides the maximum received power weighted by a bias factor, but not their nearest BS. Stochastic geometry is used to evaluate the performances of the proposed system model in terms of the outage probability and ergodic rate for each tier as functions of the biased and power control parameters. Simulations validate our analytical derivations. Numerical results show that there exists a trade-off introduced by the power cut-off threshold and the maximum user transmit power constraint. When the maximum user transmit power becomes a binding constraint, the overall performance is independent of BS densities. In addition, we have shown that it is beneficial for the outage and rate performances by optimizing different network parameters such as the power cut-off threshold as well as the biased factors.

A Combining Call Admission Control and Power Control Scheme for D2D Communications Underlaying Cellular Networks

As device-to-device(D2D) communications usually reuses the resource of cellular networks, call admission control(CAC) and power control are crucial problems. However in most power control schemes, total data rates or throughput are regarded as optimization criterion. In this paper, a combining call admission control(CAC) and power control scheme under guaranteeing QoS of every user equipment(UE) is proposed. First, a simple CAC scheme is introduced. Then based on the CAC scheme, a combining call admission control and power control scheme is proposed. Next, the performance of the proposed scheme is evaluated. Finally, maximum DUE pair number and average transmitting power is calculated. Simulation results show that D2 D communications with the proposed combining call admission control and power control scheme can effectively improve the maximum DUE pair number under the premise of meeting necessary QoS.

Physical Layer Security of Multi-hop Aided Downlink MIMO heterogeneous Cellular Networks

This study investigates physical layer security in downlink multipleinput multiple-output(MIMO) multi-hop heterogeneous cellular networks(MHCNs),in which communication between mobile users and base stations(BSs) is established by a single or multiple hops,to address the problem of insufficient security performance of MIMO heterogeneous cellular networks.First,two-dimensional homogeneous Poisson point processes(HPPPs) are utilized to model the locations of K-tier BSs in MIMO MHCNs and receivers,including those of legitimate users and eavesdroppers.Second,based on the channel gain distribution and the statistics property of HPPP,the achievable ergodic rates of the main and eavesdropper channels in direct and ad hoc links are derived,respectively.Third,the secrecy coverage probability and the achievable ergodic secrecy throughput of downlink MIMO MHCNs are explored,and their expressions are derived.Lastly,the correctness of the theoretical derivation is verified through Monte Carlo simulations.

Energy Efficiency Optimization for Heterogeneous Cellular Networks Modeled by Matérn Hard-Core Point Process

The Poisson point process(PPP) has been widely used in wireless network modeling and performance analysis due to the independence between its nodes. Therefore, it may not be a suitable model for many of the exclusive networks between the nodes. This paper analyzes the energy efficiency(EE) and optimizes the two-tier heterogeneous cellular networks(Het Nets). Considering the mutual exclusion between macro base stations(MBSs) distribution, the deployment of MBSs is modeled by the Matérn hard-core point process(MHCPP), and the deployment of pico base stations(PBSs) is modeled by the PPP. We adopt a simple approximation method to study the signal to interference ratio(SIR) distribution in two-tier MHCPP-PPP networks and then derive the coverage probabilities, the average data rates and the energy efficiency of Het Nets. Finally, an optimization algorithm is proposed to improve the EE of Het Nets by controlling the transmit power of PBSs. The simulation results show that the EE of a system can be effectively improved by selecting the appropriate transmit power for the PBSs. In addition, two-tier MHCPP-PPP Het Nets have higher energy efficiency than two-tier PPP-PPP Het Nets.

A Novel Handoff Algorithm for Hierarchical Cellular Networks

In this paper, we propose a novel speed and service-sensitive handoff algorithm and analytical model for hierarchical cellular networks.First, we use the Gauss-Markov mobility model to predict the speeds of mobile stations, and divide mobile stations into three classes based on the predicted speeds: fast, medium-speed, and slow.Then, according to the mobility classification,network conditions, and service types, mobile stations will be handoff to the proper target networks prior to the deterioration of the currently operating channel. We further develop an analytical model to evaluate the performance of such a hierarchical system with different speed classes and service types. Simulations and analytical results show that the proposed handoff algorithm can significantly improve the network performance in terms of the handoff failure probability, unnecessary handoff probability, and network throughput, comparing with the traditional algorithms.

Deep Learning Based Physical Layer Security of D2D Underlay Cellular Network

In order to improve the physical layer security of the device-to-device(D2D)cellular network,we propose a collaborative scheme for the transmit antenna selection and the optimal D2D pair establishment based on deep learning.Due to the mobility of users,using the current channel state information to select a transmit antenna or establish a D2D pair for the next time slot cannot ensure secure communication.Therefore,in this paper,we utilize the Echo State Network(ESN)to select the transmit antenna and the Long Short-Term Memory(LSTM)to establish the D2D pair.The simulation results show that the LSTMbased and ESN-based collaboration scheme can effectively improve the security capacity of the cellular network with D2D and increase the life of the base station.

A Method for Deploying the Minimal Number of UAV Base Stations in Cellular Networks

In this paper,we consider the scenario of using unmanned aerial vehicles base stations(UAV-BSs)to serve cellular users.In particular,we focus on frnding the minimum number of UAV-BSs as well as their deployment.We propose an optimization model which minimizes the number of UAV-BSs and optimize their positions such that the user equipment(UE)covered ratio is no less than the expectation of network suppliers,the UEs receive acceptable downlink rates,and the UAV-BSs can work in a sustainable manner.We show the NP-hardness of this problem and then propose a method to address it.The method first estimates the range of the number of UAV-BSs and then converts the original problem to one which maximizes the UE served ratio,given the number of UAV-BSs within that range.We present a maximizing algorithm to solve it with the proof of convergence.Extensive simulations based on a realistic dataset have been conducted to demonstrate the effectiveness of the proposed method.

Intelligent Fast Cell Association Scheme Based on Deep Q-Learning in Ultra-Dense Cellular Networks

To support dramatically increased traffic loads,communication networks become ultra-dense.Traditional cell association(CA)schemes are timeconsuming,forcing researchers to seek fast schemes.This paper proposes a deep Q-learning based scheme,whose main idea is to train a deep neural network(DNN)to calculate the Q values of all the state-action pairs and the cell holding the maximum Q value is associated.In the training stage,the intelligent agent continuously generates samples through the trial-anderror method to train the DNN until convergence.In the application stage,state vectors of all the users are inputted to the trained DNN to quickly obtain a satisfied CA result of a scenario with the same BS locations and user distribution.Simulations demonstrate that the proposed scheme provides satisfied CA results in a computational time several orders of magnitudes shorter than traditional schemes.Meanwhile,performance metrics,such as capacity and fairness,can be guaranteed.

Deep reinforcement learning-based resource allocation for D2D communications in heterogeneous cellular networks

Device-to-Device(D2D)communication-enabled Heterogeneous Cellular Networks(HCNs)have been a promising technology for satisfying the growing demands of smart mobile devices in fifth-generation mobile networks.The introduction of Millimeter Wave(mm-wave)communications into D2D-enabled HCNs allows higher system capacity and user data rates to be achieved.However,interference among cellular and D2D links remains severe due to spectrum sharing.In this paper,to guarantee user Quality of Service(QoS)requirements and effectively manage the interference among users,we focus on investigating the joint optimization problem of mode selection and channel allocation in D2D-enabled HCNs with mm-wave and cellular bands.The optimization problem is formulated as the maximization of the system sum-rate under QoS constraints of both cellular and D2D users in HCNs.To solve it,a distributed multiagent deep Q-network algorithm is proposed,where the reward function is redefined according to the optimization objective.In addition,to reduce signaling overhead,a partial information sharing strategy that does not observe global information is proposed for D2D agents to select the optimal mode and channel through learning.Simulation results illustrate that the proposed joint optimization algorithm possesses good convergence and achieves better system performance compared with other existing schemes.

Spatio-Temporal Cellular Network Traffic Prediction Using Multi-Task Deep Learning for AI-Enabled 6G

Spatio-temporal cellular network traffic prediction at wide-area level plays an important role in resource reconfiguration,traffic scheduling and intrusion detection,thus potentially supporting connected intelligence of the sixth generation of mobile communications technology(6G).However,the existing studies just focus on the spatio-temporal modeling of traffic data of single network service,such as short message,call,or Internet.It is not conducive to accurate prediction of traffic data,characterised by diverse network service,spatio-temporality and supersize volume.To address this issue,a novel multi-task deep learning framework is developed for citywide cellular network traffic prediction.Functionally,this framework mainly consists of a dual modular feature sharing layer and a multi-task learning layer(DMFS-MT).The former aims at mining long-term spatio-temporal dependencies and local spatio-temporal fluctuation trends in data,respectively,via a new combination of convolutional gated recurrent unit(ConvGRU)and 3-dimensional convolutional neural network(3D-CNN).For the latter,each task is performed for predicting service-specific traffic data based on a fully connected network.On the real-world Telecom Italia dataset,simulation results demonstrate the effectiveness of our proposal through prediction performance measure,spatial pattern comparison and statistical distribution verification.

Performance of Fixed Channel Assignment for Uplink Transmission and Direct Peer-to-Peer Communications in Multihop Cellular Networks

In this paper, we propose a clustered multihop cellular network （cMCN） architecture and study its performance using fixed channel assignment （FCA） scheme for uplink transmission. The proposed cMCN using FCA can be applied with some reuse factors. An analytical model based on Markov chain is developed to analyze its performance and validated through computer simulation. And then, we implement direct peer-to-peer communication （DC） in cMCN by considering more reasonable conditions in practice. DC means that two calls communicate directly instead of going through base stations. The results show that cMCN with FCA can reduce the call blocking probability significantly as compared with the traditional single-hop cellular networks with FCA and can be further reduced by using DC.

Computational analysis of(MAP_1,MAP_2)/(PH_1,PH_2)/N queues with finite buffer in wireless cellular networks

This paper studies a queueing model with the finite buffer of capacity K in wireless cellular networks, which has two types of arriving calls--handoff and originating calls, both of which follow the Markov arriving process with different rates. The channel holding times of the two types of calls follow different phase-type distributions. Firstly, the joint distribution of two queue lengths is derived, and then the dropping and blocking probabilities, the mean queue length and the mean waiting time from the joint distribution are gotten. Finally, numerical examples show the impact of different call arrival rates on the performance measures.

Dynamic Spectrum Control-Assisted Secure and Efficient Transmission Scheme in Heterogeneous Cellular Networks

Heterogeneous cellular networks(HCNs)are envisioned as a promising architecture to provide seamless wireless coverage and increase network capacity.However,the densified multi-tier network architecture introduces excessive intra-and cross-tier interference and makes HCNs vulnerable to eavesdropping attacks.In this article,a dynamic spectrum control(DSC)-assisted transmission scheme is proposed for HCNs to strengthen network security and increase the network capacity.Specifically,the proposed DSC-assisted transmission scheme leverages the idea of block cryptography to generate sequence families,which represent the transmission decisions,by performing iterative and orthogonal sequence transformations.Based on the sequence families,multiple users can dynamically occupy different frequency slots for data transmission simultaneously.In addition,the collision probability of the data transmission is analyzed,which results in closed-form expressions of the reliable transmission probability and the secrecy probability.Then,the upper and lower bounds of network capacity are further derived with given requirements on the reliable and secure transmission probabilities.Simulation results demonstrate that the proposed DSC-assisted scheme can outperform the benchmark scheme in terms of security performance.Finally,the impacts of key factors in the proposed DSC-assisted scheme on the network capacity and security are evaluated and discussed.

An Efficient Correlation-Aware Anomaly Detection Framework in Cellular Network

Nowadays,the fifth-generation(5G)mobile communication system has obtained prosperous development and deployment,reshaping our daily lives.However,anomalies of cell outages and congestion in 5G critically influence the quality of experience and significantly increase operational expenditures.Although several big data and artificial intelligencebased anomaly detection methods have been proposed for wireless cellular systems,they change distributions of the data and ignore the relevance among user activities,causing anomaly detection ineffective for some cells.In this paper,we propose a highly effective and accurate anomaly detection framework by utilizing generative adversarial networks(GAN)and long short-term memory(LSTM)neural networks.The framework expands the original dataset while simultaneously keeping the distribution of data unchanged,and explores the relevance among user activities to further improve the system performance.The results demonstrate that our framework can achieve 97.16%accuracy and 2.30%false positive rate by utilizing the correlation of user activities and data expansion.

A Tractable Approach to Analyzing the Physical-Layer Security in K-Tier Heterogeneous Cellular Networks

This study proposes a tractable approach to analyze the physical-layer security in the downlink of a multi-tier heterogeneous cellular network. This method is based on stochastic geometry, has low computational complexity, and uses the two-dimensional Poisson point process to model the locations of K-tier base stations and receivers, including those of legitimate users and eavesdroppers. Then, the achievable secrecy rates for an arbitrary user are determined and the upper and lower bounds of secrecy coverage probability derived on the condition that cross-tier interference is the main contributor to aggregate interference. Finally, our analysis results reveal the innate connections between information-theoretic security and the spatial densities of legitimate and malicious nodes.