Time-varying Channel Conditions Research Articles

NOMA-based UAV communications for maritime coverage enhancement

The lack of communication infrastructure in the ocean inevitably leads to coverage blind zones. In addition to high-throughput marine satellites, unmanned aerial vehicles (UAVs) can be used to provide coverage for these blind zones along with onshore base stations. In this paper, we consider the use of UAV for maritime coverage enhancement. Particularly, to serve more ships on the vast oceanic area with limited spectrum resources, we employ non-orthogonal multiple access (NOMA). A joint power and transmission duration allocation problem is formulated to maximize the minimum ship throughput, with the constraints on onboard communication energy. Different from previous works, we only assume the slowly time-varying large-scale channel state information (CSI) to reduce the system cost, as the large-scale CSI is location-dependent and can be obtained according to a priori radio map. To solve the non-convex problem, we decompose it into two subproblems and solve them in an iterative way. Simulation results show the effectiveness of the proposed solution.

D2D-Assisted Multi-User Cooperative Partial Offloading, Transmission Scheduling and Computation Allocating for MEC

By fully exploiting the cooperative communication capacities among mobile terminals (MTs), the MTs can adapt the offloading designs well to the practical network with dynamic features. In this paper, joint multi-user cooperative partial offloading, transmission scheduling and computation allocating is discussed for device-to-device (D2D) underlay mobile edge computing (MEC). By considering stochastic application requests, unpredictable MTs states, time-varying channel states and computation resources, a customized application offloading model, which aims to minimize the network-wide response latency and energy consumption simultaneously, is formulated. In order to solve this non-convex and non-smooth optimization problem, an online resource coordinating and allocating scheme (ORCAS) is proposed by exploiting Lyapunov optimization theory, variable substitution technique and resource provisioning priority mechanism. Both theoretical analyses and simulation results demonstrate that the proposed ORCAS can 1) drive the application response cost converge to the minimum; 2) achieve superior performance (e.g., the average network-wide response cost under ORCAS is approximately 19.14% lower than that under partial offloading directly); 3) adapt to dynamic situations in terms of stochastic user demands and channel states.

Multi-source spinal coding for coded caching multicast transmissions in wireless networks

Recently, coded caching has been treated as a promising technique to alleviate the traffic burden in wireless networks. To support high efficient coded caching multicast transmissions, the time-varying heterogeneous channel conditions need to be considered. In this paper, a practical and novel multi-source spinal coding (MSSC) scheme is developed for coded caching multicast transmissions under heterogeneous channel conditions. By exploring joint design of network coding and spinal coding (SC), MSSC can achieve unequal link rates in multicast transmissions for different users. Moreover, by leveraging the rate-less feature of SC in our design, MSSC can well adapt the link rates of all users in multicast transmissions without any feedback of time-varying channel conditions. A maximum likelihood (ML) based decoding process for MSSC is also developed, which can achieve a linear complexity with respect to the user number in the multicast transmission. Simulation results validate the effectiveness of the MSSC scheme. Compared to the existing scheme, the sum rate of MSSC in multicast transmissions is improved by about 20%. When applying MSSC in coded caching systems, the total transmission time can be reduced by up to 48% for time-varying channels.

Delay-Aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach

As an emerging service architecture, microservice enables decomposition of a monolithic web service into a set of independent lightweight services which can be executed independently. With mobile edge computing, microservices can be further deployed in edge clouds dynamically, launched quickly, and migrated across edge clouds easily, providing better services for users in proximity. However, the user mobility can result in frequent switch of nearby edge clouds, which increases the service delay when users move away from their serving edge clouds. To address this issue, this article investigates microservice coordination among edge clouds to enable seamless and real-time responses to service requests from mobile users. The objective of this work is to devise the optimal microservice coordination scheme which can reduce the overall service delay with low costs. To this end, we first propose a dynamic programming-based offline microservice coordination algorithm, that can achieve the globally optimal performance. However, the offline algorithm heavily relies on the availability of the prior information such as computation request arrivals, time-varying channel conditions and edge cloud's computation capabilities required, which is hard to be obtained. Therefore, we reformulate the microservice coordination problem using Markov decision process framework and then propose a reinforcement learning-based online microservice coordination algorithm to learn the optimal strategy. Theoretical analysis proves that the offline algorithm can find the optimal solution while the online algorithm can achieve near-optimal performance. Furthermore, based on two real-world datasets, i.e., the Telecom's base station dataset and Taxi Track dataset from Shanghai, experiments are conducted. The experimental results demonstrate that the proposed online algorithm outperforms existing algorithms in terms of service delay and migration costs, and the achieved performance is close to the optimal performance obtained by the offline algorithm.

Identification of OFDM-Based Radios Under Rayleigh Fading Using RF-DNA and Deep Learning

The Internet of Things (IoT) is here and has permeated every aspect of our lives. A disturbing fact is that the majority of all IoT devices employ weak or no encryption at all. This coupled with recent advances within the areas of computational power and deep learning has increased interest in Specific Emitter Identification (SEI) as an effective means of IoT security. Deep learning is capable of in-situ extraction of discriminating features, making it well suited to discrimination of wireless transmitters without the need for feature engineering. However, the accuracy of the deep learning model is adversely affected by time-varying channel conditions. The time-varying nature is attributed to the mobility of the transmitter, receiver, objects within the operations environment, or combinations thereof. This can result in the channel conditions changing faster than the deep learning algorithm is capable of handling. This paper assesses deep learning-based SEI using waveforms that undergo Rayleigh fading, as well as channel estimation and equalization, prior to being input into a deep learning algorithm.

Energy-efficient computation offloading for vehicular edge computing networks

The demanding computing capacity of emerging vehicular applications has emerged as a challenge in Internet of vehicles (IoVs). Multi-access edge computing (MEC) can significantly enhance computing capability and prolong battery life of vehicles through offloading computation-intensive tasks for edge computing. Considering the impact of vehicles’ mobility on communication quality, this paper provides an energy-efficient computation offloading scheme for vehicular edge computing networks (VECN). An energy-efficiency cost (EEC) minimization problem is formulated to make a tradeoff between latency and energy consumption, for completing computational tasks in an effective manner. Since that multiple variables and time-varying channel conditions make the formulated problem difficult to solve, we transform the original non-convex problem into a two-level optimization problem and develop an iterative distributed algorithm to obtain an optimal solution. Numerical results verify the convergence and superiority of the proposed algorithm.

Deep Reinforcement Learning-Based Intelligent Reflecting Surface for Secure Wireless Communications

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system for physical layer security, where an IRS is deployed to adjust its surface reflecting elements to guarantee secure communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)'s beamforming and the IRS's reflecting beamforming is formulated given the different quality of service (QoS) requirements and time-varying channel condition. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, PDS is capable of tracing the environment dynamic characteristics and adjust the beamforming policy accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning-based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems.

Hierarchical Resource Allocation in Multi-Service Wireless Networks With Wireless Network Virtualization

To balance the contradiction between the rapid growth of data service demands and the limited spectrum resources, wireless network virtualization (WNV) has been proposed as a promising technology by isolating and sharing wireless resources among different virtual networks in the future wireless networks. In this paper, a two-dimension-time-scale hierarchical resource allocation scheme is proposed in the multi-service wireless virtualized network, which consists of three 5G generic scenarios. The resource slicing problem is decomposed into two time scales including large time period for inter-slice resource pre-allocation and small time slot for intra-slice resource scheduling. In large time period, the inter-slice resource pre-allocation problem is formulated as a multi-objective optimization problem (MOOP) by modeling the packets arriving and serving process of each slice as a queueing system. While in small time slot, the resource block (RB) and power scheduling in each slice is formulated as a stochastic optimization problem considering dynamic traffic arrivals and time-varying channel conditions, which is aimed at optimizing the overall performance subject to various quality of service (QoS) requirements such as network stability, delay, reliability, transmission rate and power constraints. The stochastic optimization problem can be transformed into a delay-aware optimization problem by applying Lyapunov optimization technique, and be solved by the proposed algorithm consisting of a heuristic algorithm and a concave optimization algorithm. The simulation results show that the proposed schemes are close to the optimal solution with a lower complexity, which can also achieve a performance-delay tradeoff related to the control factor.

Frequency-Domain Decision Feedback Equalization for Single-Carrier Transmissions in Fast Time-Varying Underwater Acoustic Channels

Frequency-domain equalization (FDE) is a computationally efficient method to recover underwater acoustic single-carrier transmissions in a multipath environment. The FDE receivers often operate in a block-by-block manner, assuming that the channel is time invariant during a block duration. This assumption implies that traditional block-based FDE receivers experience performance degradation in fast time-varying channel conditions, since the interfrequency interference (IFI) is often not considered. Here, we focus on the IFI cancelation (IFIC) in single-carrier transmissions. The IFI formulations are developed for a blockwise FDE receiver, where the time reversal (TR) processing is incorporated to alleviate the interblock interference. Based on the formulations, the IFI is estimated by the time-varying impulse responses, which are obtained from an adaptive basis expansion model. A soft two-step IFIC strategy is then employed to address the IFI. In the first step, the main IFI is removed by the time-varying TR processing and explicit IFI cancelation. The residual IFI is suppressed by the frequency-domain decision feedback equalization (FD-DFE) in the second step. To further enhance the communication performance, we develop a soft-symbol-based iterative receiver, referred to as IFIC-FD-DFE. The impulse responses, explicit IFI, and residual IFI are updated in an iterative fashion, based on the soft symbol estimates. The proposed receiver has been tested using the at-sea measurements collected at the Gulf of Mexico in August 2017. Communication packets at four ranges at the carrier frequency of 160 kHz with a symbol rate of 32 kHz have been decoded. The effectiveness of the IFIC-FD-DFE receiver has been demonstrated via comparisons with several FDE schemes in the current literature.

Delay-Aware Resource Management for Multi-Service Coexisting LTE-D2D Networks With Wireless Network Virtualization

To satisfy the rapid growth of data service demands on the limited spectrum resources, wireless network virtualization (WNV) has been proposed as a promising technology in the future wireless networks. Meanwhile, Device-to-Device (D2D) communication, as an underlay to cellular networks, is also a proposed solution to promote the spectrum efficiency. However, it is a challenge to design an appropriate virtual resource slicing strategy among different wireless service providers in the two-tier LTE-D2D networks. In this paper, considering dynamic traffic arrivals and time-varying channel conditions, the virtualized resource management in the multi-service coexisting LTE-D2D networks is formulated into a multi-objective stochastic optimization problem. Leveraging on weighted Tchebycheff approach and Lyapunov optimization technique, the multi-objective stochastic optimization problem can be transformed into a delay-aware optimization problem, which is a mixed-integer nonlinear programming (MINLP) problem and can be transformed into a convex one by a series of reformulation. A distributed algorithm based on the alternating direction method of multipliers (ADMM) is proposed to obtain a close-to-optimal solution with a lower computational complexity. Finally, the simulation results show that the proposed scheme can achieve a performance-delay tradeoff related to the control factor V with at most 0.2% performance loss compared with the optimal solution.

Dynamic Computation Offloading in IoT Fog Systems With Imperfect Channel-State Information: A POMDP Approach

Driven by the growing popularity of mobile applications, such as the Internet of Things (IoT), fog computing has been envisioned as a promising approach to enhance the computation capability of mobile devices and reduce the energy consumption. In this article, we aim to investigate the dynamic computation offloading problem in the IoT fog system under the fast time-varying wireless channel conditions. Our work differs from the existing work, which is based on the assumption that the channel-state information can be perfectly obtained by the offloading agent (e.g., the IoT device). In reality, due to hardware limitation, short sensing time, and network connectivity issues in IoT fog systems, it is difficult for the IoT device to have the perfect knowledge of a dynamic channel environment. Therefore, in this article, we propose a partially observable offloading scheme to enable the IoT device to make the optimal offloading decision with imperfect channel-state information. The optimization problem is formulated as a partially observable Markov decision process (POMDP) formulation, with the objective of minimizing the IoT device's energy consumption while meeting its requirement on task processing delay. To find the optimal offloading solution, an offline algorithm based on the deep recurrent $Q$ -network (DRQN) is developed. Finally, extensive simulation experiments are performed to evaluate the effectiveness of the proposed offloading scheme.

Opportunistic Waveform Scheduling for Wireless Power Transfer With Multiple Devices

In this paper, we study a waveform scheduling problem for a multi-receiver wireless power transfer (WPT) system considering time-varying channel conditions and minimum average output direct-current (DC) voltage requirement of each receiver. To this end, we formulate a stochastic optimization problem that aims at maximizing the average of the sum of output DC voltages of receivers while satisfying the minimum average output DC voltage requirements of all receivers, and by solving it, we develop a waveform scheduling algorithm. In the waveform scheduling algorithm, we need to solve a problem for maximizing the weighted-sum of output DC voltages of receivers, which is a non-convex optimization problem. To cope with this difficulty, we develop a low-complexity approximated algorithm with which the waveform for the multi-receiver WPT system is optimized to maximize the weighted sum of output DC voltages of receivers. Numerical results show that our waveform design algorithm provides the higher performance of the weighted-sum of the output DC voltages than the existing algorithms, and our opportunistic waveform scheduling provides good performance while well satisfying the minimum average output DC voltage requirement of each receiver.

Routing With Intelligent Spectrum Assignment in Full-Duplex Cognitive Networks Under Varying Channel Conditions

Recent developments of self-interference suppression techniques have enabled practical implementations of full-duplex (FD) cognitive-radio (CR) communication systems. Such systems can significantly enhance spectrum utilization by allowing a CR user to simultaneously transmit and receive over the same frequency channel. However, the CR FD-capabilities challenge the effectiveness of existing CR-based routing protocols as joint FD-aware channel-assignment and routing are essential to enhance network performance. In this letter, we propose a joint FD-aware channel-assignment and route selection protocol in FD-based CR networks (CRNs) under time-varying channel conditions and transmission rates. Specifically, for a given set of paths between a CR source-destination pair, our protocol computes the channel-assignment over each path that maximizes the end-to-end network throughput subject to interference constraints. This assignment problem is shown to be an NP-hard binary linear programming (BLP) that can be sub-optimally solved in polynomial-time using the sequential-fixing procedure. Then, our protocol determines the path with the highest end-to-end network throughput. Simulation results reveal that our proposed routing protocol significantly improves the end-to-end throughput compared to previous FD-aware routing protocols.

Dynamic Task Offloading and Resource Allocation for Mobile-Edge Computing in Dense Cloud RAN

With the unprecedented development of smart mobile devices (SMDs), e.g., Internet-of-Things devices and smartphones, various computation-intensive applications are explosively increasing in ultradense networks (UDNs). Mobile-edge computing (MEC) has emerged as a key technology to alleviate the computation workloads of SMDs and decrease service latency for computation-intensive applications. With the benefits of network function virtualization, MEC can be integrated with the cloud radio access network (C-RAN) in UDNs for computation and communication cooperation. However, with stochastic computation task arrivals and time-varying channel states, it is challenging to offload computation tasks online with energy-efficient computation and radio resource management. In this article, we investigate the task offloading and resource allocation problem in MEC-enabled dense C-RAN, aiming at optimizing network energy efficiency. A stochastic mixed-integer nonlinear programming problem is formulated to jointly optimize the task offloading decision, elastic computation resource scheduling, and radio resource allocation. To tackle the problem, the Lyapunov optimization theory is introduced to decompose the original problem into four individual subproblems which are solved by convex decomposition methods and matching game. We theoretically analyze the tradeoff between energy efficiency and service delay. Extensive simulations evaluate the impacts of system parameters on both energy efficiency and service delay. The simulation results also validate the superiority of the proposed task offloading and resource allocation scheme in dense C-RAN.

An adjustable channel hopping algorithm for multi-radio cognitive radio networks

In cognitive radio networks, achieving rendezvous for two nodes (switching to the same channel at the same time) is a premise for them to communicate. Applying a channel hopping protocol is a common solution to achieve rendezvous. Most existing channel hopping protocols work in a single-radio environment and use available channels evenly without considering the time-varying channel conditions for different channels. Such channel hopping protocols may suffer from poor performance due to inefficient channel allocation. To efficiently solve the rendezvous problem, we propose the Adjustable Multi-radio Channel Hopping (AMCH) protocol in this paper. Instead of using available channels evenly, nodes running AMCH are able to adjust the usage ratio of each available channel. Simulation results verify that such a ratio adjustable channel hopping mechanism provides an efficient way to solve the rendezvous problem.

Dynamic Resource Allocation for Scalable Video Streaming in OFDMA Wireless Networks

Mobile video streaming is a successful example of Cyber-Physical-Social Systems (CPSS). How to schedule network resources and provide better mobile video streaming services for mobile users are very important. Scalable video streaming is regarded as a promising technology in wireless networks where the cognitive femtocells are overlaid within the coverage area of a macrocell network. In this paper, we study dynamic resource allocation for scalable video streaming over cache-enabled wireless networks with time-varying channel conditions. We formulate the scalable video streaming problem as a stochastic optimization problem which aims at maximizing the time-averaged system utility subject to the time-averaged video cache constraint at the server and the cross-tier interference constraint on the primary user under the sparse deployment scenario of femtocells. By employing the Lyapunov optimization theory, we design a dynamic cache and resource allocation (DCRA) algorithm to solve this problem. Furthermore, the problem is decomposed into three subproblems, i.e., video layer selection, cache placement, and wireless resource allocation. Via solving these subproblems, we derive the video layer selection and cache placement strategies, and a wireless resource allocation algorithm to manage the cross-tier interference. Simulation results demonstrate the advantages of the proposed DCRA for streaming scalable video over time-varying wireless networks.

Computation Migration and Resource Allocation in Heterogeneous Vehicular Networks: A Deep Reinforcement Learning Approach

With the development of 5G technology, the requirements for data communication and computation in emerging 5G-enabled vehicular networks are becoming increasingly stringent. Computation-intensive or delay-sensitive tasks generated by vehicles need to be processed in real time. Mobile edge computing (MEC) is an appropriate solution. Wireless users or vehicles can offload computation tasks to the MEC server due to it has strong computation ability and is closer to the wireless users or vehicles. However, the communication and computation resources of the single MEC are not sufficient for executing the continuously generated computation-intensive or delay-sensitive tasks. We consider migrating computation tasks to other MEC servers to reduce the computation and communication pressure on current MEC server. In this article, we construct an MEC-based computation offloading framework for vehicular networks, which considers time-varying channel states and stochastically arriving computation tasks. To minimize the total cost of the proposed MEC framework, which consists of the delay cost, energy computation cost, and bandwidth cost, we propose a deep reinforcement learning-based computation migration and resource allocation (RLCMRA) scheme that requires no prior knowledge. The RLCMRA algorithm can obtain the optimal offloading and migration policy by adaptive learning to maximize the average cumulative reward (minimize the total cost). Extensive numerical results show that the proposed RLCMRA algorithm can adaptively learn the optimal policy and outperform four other baseline algorithms.

Unmanned-Aerial-Vehicle-Assisted Computation Offloading for Mobile Edge Computing Based on Deep Reinforcement Learning

Users in heterogeneous wireless networks may generate massive amounts of data that are delay-sensitive or require computation-intensive processing. Owing to computation ability and battery capacity limitations, wireless users (WUs) cannot easily process such data in a timely manner, and mobile edge computing (MEC) is increasingly being used to resolve this issue. Specifically, data generated by WUs can be offloaded to the MEC server for processing, which has greater computing power than WUs. However, as the location of MEC servers is fixed, unmanned aerial vehicles (UAVs) have been considered a promising solution in heterogeneous wireless networks. In this study, we design an UAV-assisted computation offloading scheme in an MEC framework with renewable power supply. The proposed model considers the instability of energy arrival, stochastic computation tasks generated by WUs, and a time-varying channel state. Owing to the complexity of the state, it is difficult to use traditional Markov decision process (MDP) with complete prior knowledge for offloading optimization. Accordingly, we propose UAV-assisted computation offloading for MEC based on deep reinforcement learning (UACODRL) to minimize the total cost, which is the weighted sum of the delay, energy consumption, and bandwidth cost. We first use the K-Means algorithm for classification to reduce the dimension of the action space. Subsequently, we use UACODRL to find the near-optimal offloading scheme to minimize the total cost. Simulations demonstrate that UACODRL converges satisfactorily and performs better than four baseline schemes with different parameter configurations.

Reinforcement Learning-Based Downlink Interference Control for Ultra-Dense Small Cells

The dense deployment of small cells in 5G cellular networks raises the issue of controlling downlink inter-cell interference under time-varying channel states. In this paper, we propose a reinforcement learning based power control scheme to suppress downlink inter-cell interference and save energy for ultra-dense small cells. This scheme enables base stations to schedule the downlink transmit power without knowing the interference distribution and the channel states of the neighboring small cells. A deep reinforcement learning based interference control algorithm is designed to further accelerate learning for ultra-dense small cells with a large number of active users. Analytical convergence performance bounds including throughput, energy consumption, inter-cell interference, and the utility of base stations are provided and the computational complexity of our proposed scheme is discussed. Simulation results show that this scheme optimizes the downlink interference control performance after sufficient power control instances and significantly increases the network throughput with less energy consumption compared with a benchmark scheme.

Dueling Deep-Q-Network Based Delay-Aware Cache Update Policy for Mobile Users in Fog Radio Access Networks

Fog radio access networks (F-RANs) can effectively alleviate fronthaul loads and reduce content transmission delay by migrating cloud services to the network edge. This paper addresses a cooperative caching scenario in F-RAN, where each mobile user can acquire the requested contents from any one of its associated fog-computing-based access points (F-APs). However, caching disparate contents in different F-APs will lead to different content delivery delays, since mobile users suffer from diverse channel fadings and interferences when they download contents from different F-APs. Considering limited caching storage in each F-AP, diverse user preferences, unpredictable user mobility and time-varying channel states, an average transmission delay minimization problem is formulated. With the aid of dueling deep-Q-network framework, a delay-aware cache update policy is proposed for mobile users in F-RAN. The proposed cache update policy will decide to replace the stored contents in F-APs with the proper contents at each time slot. Compared with first in first out, least recently used and least frequently used caching policies, simulation experiments are performed to evaluate the performance of the proposed algorithm. Simulation results illustrate that the proposed caching policy yields better average hit ratio and lower average transmission delay than other traditional caching policies.