Future Channel State Research Articles

Joint Scheduling and Power Optimization for Delay Constrained Transmissions in Coded Caching Over Wireless Fading Channels

Coded caching has become a hot research topic in recent years. However, existing studies rarely consider the delay constrained transmissions over wireless fading channels. For strict delay constrained content delivery over wireless fading channels, power optimization is usually required to achieve energy-efficient transmissions. Moreover, in coded caching, since the data to different users is transmitted via many coded multicast transmissions, the scheduling of these transmissions over fading channels is also critical for minimizing energy consumption. To this end, a joint scheduling and power optimization problem is formulated to minimize the expected energy consumption over wireless fading channels under strict delay constraints. Causal channel state information (CSI) is considered in this paper for practical cases, which nonetheless makes the optimal solution to this problem hard to achieve due to the uncertain future channel states. Therefore, a heuristic approach is developed to solve this problem. The number of slots for each coded multicast transmission is first determined by obtaining the optimal result of a nonlinear integer programming (NLIP) problem based on statistical channel estimations. Next, a closed-form inverse-waterfilling algorithm is carried out to allocate power for the coded transmissions under delay constraints, and then the one that saves the maximum energy consumption is scheduled for transmission. The heuristic approach is proved to achieve a result that is upper-bounded by a parameter times the optimal result of the original problem under non-causal CSI. Simulation results further show that compared to the benchmark with only slot allocation for each coded transmission, our approach with joint scheduling and power optimization can significantly save energy consumption while ensuring strict delay constraints.

Deep Learning-Based Beam Tracking for Millimeter-Wave Communications Under Mobility

In this paper, we propose a deep learning-based beam tracking method for millimeter-wave (mmWave) communications. Beam tracking is employed for transmitting the known symbols using the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sounding beams</i> and tracking time-varying channels to maintain a reliable communication link. When the pose of a user equipment (UE) device varies rapidly, the mmWave channels also tend to vary fast, which hinders seamless communication. Thus, models that can capture temporal behavior of mmWave channels caused by the motion of the device are required, to cope with this problem. Accordingly, we employ a deep neural network to analyze the temporal structure and patterns underlying in the time-varying channels and the signals acquired by inertial sensors. We propose a model based on long short term memory (LSTM) that predicts the distribution of the future channel behavior based on a sequence of input signals available at the UE. This channel distribution is used to 1) control the sounding beams adaptively for the future channel state and 2) update the channel estimate through the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">measurement update step</i> under a sequential Bayesian estimation framework. Our experimental results demonstrate that the proposed method achieves a significant performance gain over the conventional beam tracking methods under various mobility scenarios.

Dynamic Computation Offloading in Ultra-Dense Networks Based on Mean Field Games

In ultra-dense networks, the increasing popularity of computation intensive applications imposes challenges to the resource-constrained smart mobile devices (SMDs), which may be solved by offloading these computation tasks to the nearby mobile edge computing centers. However, when massive SMDs offload computation tasks in a dynamic wireless environment simultaneously, the joint optimization of their offloading decisions becomes prohibitively complex. In this paper, we firstly model the joint optimization problem as a multi-user non-cooperative dynamic stochastic game, then propose a mean field game based algorithm to solve it with a drastically reduced complexity. We derive the two partial differential equations ruling the optimal strategies of the mean field game, namely the Hamilton-Jacobi-Bellman and Fokker-Planck-Kolmogorov equations, which are solved in an iterative manner in our proposed algorithm. Numerical results demonstrate that the proposed mean field game-based offloading algorithm requires a lower cumulated cost than the conventional strategies under the latency constraints of computation tasks, with perfect prediction of future channel states. It also appears that the performance of the mean field game-based offloading strategy depends on the accuracy of the future channel knowledge provided to the system, as the uncertainty may compromise its cumulated cost performance.

Neural-Network-Based Power Control Prediction for Solar-Powered Energy Harvesting Communications

In this article, we design neural network (NN)-based transmit power control prediction for solar-powered energy harvesting (EH) communications under single-user (SU) and multiuser (MU) scenarios with real solar data. Although the directional water filling (DWF) is known as the optimal scheme for the SU case, it necessitates the full (past and future) channel state information (CSI) and energy state information (ESI) in realizing the optimal solution over a time period. To conquer this impracticality, an SU-GreenPCNet, which only requires the past short-term CSI and ESI for predicting the SU transmit power, is proposed and trained with the historical solar data. For the MU case, two iterative algorithms, namely, weighted-sum minimum mean-square error (WMMSE) and iterative DWF (IDWF), are investigated to tackle the original nonconvex power control problem when the full state knowledge is assumed to be known in advance. The solutions with the historical solar data are then served as benchmarks for designing two MU-GreenPCNets. As an extension of the SU-GreenPCNet, a centralized scheme is proposed at the central controller for jointly determining the MU transmit power values based on the past short-term state knowledge of all users. A distributed scheme is further investigated to reduce the signaling overhead, in which each transmitter merely utilizes the past short-term CSI, ESI, and MU interference (MUI) knowledge associated with its user pair. The simulation results show that the proposed power control prediction schemes can pragmatically achieve satisfied sum rate performance in both SU and MU scenarios, as compared with the benchmark schemes.

Power Control Optimization for Large-Scale Multi-Antenna Systems

Large-scale multi-antenna systems can effectively improve data transmission reliability and throughput for smart grid. However, the massive number of antennas and radio frequency (RF) chains also result in high complexity and energy cost. In this paper, we develop a new performance benchmark named energy economic efficiency for measuring the time-average throughput per energy cost. Then, we investigate how to maximize long-term energy economic efficiency via the joint optimization of communication and energy resource allocation. The formulated joint optimization problem is NP-hard because it not only involves long-term nonlinear optimization objective and constraints, but also involves both integer and continuous optimization variables. Next, we propose an online joint antenna selection and power control algorithm by combining nonlinear fractional programming, Lyapunov optimization, and bisection method. The proposed algorithm can achieve bounded performance deviation from the optimum performance without requiring the prior knowledge of future channel state information (CSI), energy arrival, and electricity price. Finally, a comprehensive theoretical analysis is provided, and the proposed algorithm is verified through simulations under various system configurations.

The Application of Deep Reinforcement Learning to Distributed Spectrum Access in Dynamic Heterogeneous Environments With Partial Observations

This papera <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> investigates deep reinforcement learning (DRL) based on a Recurrent Neural Network (RNN) for Dynamic Spectrum Access (DSA) under partial observations, referred to as a Deep Recurrent Q-Network (DRQN). Specifically, we consider a scenario with multiple independent channels and multiple heterogeneous Primary Users (PUs). Two key challenges in our problem formulation are that we assume our DRQN node does not have any prior knowledge of the other nodes' behavior patterns and attempts to predict the future channel state based on previous observations. The goal of the DRQN is to learn a channel access strategy with a low collision rate but a high channel utilization rate. With proper definitions of the state, action and rewards, our extensive simulation results show that a DRQN-based approach can handle a variety of communication environments including dynamic environments. Further, our results show that the DRQN node is also able to cope with multi-rate and multi-agent scenarios. Importantly, we show the following benefits of using recurrent neural networks in DSA: (i) the ability to learn the optimal strategy in different environments under partial observations; (ii) robustness to imperfect observations and (iii) the ability to utilize multiple channels, and (iv) robustness in the presence of multiple agents. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> A parton of this work was presented at MILCOM 2018 in [1].

Joint Long-Term Cache Updating and Short-Term Content Delivery in Cloud-Based Small Cell Networks

Explosive growth of mobile data demand may impose a heavy traffic burden on fronthaul links of cloud-based small cell networks (C-SCNs), which deteriorates users' quality of service (QoS) and requires substantial power consumption. This paper proposes an efficient maximum distance separable (MDS) coded caching framework for a cache-enabled C-SCNs, aiming at reducing long-term power consumption while satisfying users' QoS requirements in short-term transmissions. To achieve this goal, the cache resource in small-cell base stations (SBSs) needs to be reasonably updated by taking into account users' content preferences, SBS collaboration, and characteristics of wireless links. Specifically, without assuming any prior knowledge of content popularity, we formulate a mixed timescale problem to jointly optimize cache updating, multicast beamformers in fronthaul and edge links, and SBS clustering. Nevertheless, this problem is anti-causal because an optimal cache updating policy depends on future content requests and channel state information. To handle it, by properly leveraging historical observations, we propose a two-stage updating scheme by using Frobenius-Norm penalty and inexact block coordinate descent method. Furthermore, we derive a learning-based design, which can obtain effective tradeoff between accuracy and computational complexity. Simulation results demonstrate the effectiveness of the proposed two-stage framework.

Online Regularization of Complex-Valued Neural Networks for Structure Optimization in Wireless-Communication Channel Prediction

This paper proposes online-learning complex-valued neural networks (CVNNs) to predict future channel states in fast-fading multipath mobile communications. CVNN is suitable for dealing with a fading communication channel as a single complex-valued entity. This framework makes it possible to realize accurate channel prediction by utilizing its high generalization ability in the complex domain. However, actual communication environments are marked by rapid and irregular changes, thus causing fluctuation of communication channel states. Hence, an empirically selected stationary network gives only limited prediction accuracy. In this paper, we introduce regularization in the update of the CVNN weights to develop online dynamics that can self-optimize its effective network size by responding to such channel-state changes. It realizes online adaptive, highly accurate and robust channel prediction with dynamical adjustment of the network size. We demonstrate its online adaptability in simulations and real wireless-propagation experiments.

Mobility-Aware Resource Allocation in VLC Networks Using T-Step Look-Ahead Policy

Visible light communication (VLC) uses huge license-free spectral bandwidth of visible light for high-speed wireless communication. Since each VLC access point covers a small area, handovers of mobile users are inevitable. In order to deal with these handovers, developing fast and effective resource allocation algorithms is a challenge. This paper introduces the problem of mobility-aware optimization of resources in VLC networks using the T-step look-ahead policy and employing the notion of handover efficiency. It is shown that the handover efficiency can correlate the overall performance of the network with future actions based on the mobility of users. Due to the stationary nature of indoor optical wireless channels, future channel state information (CSI) can be predicted by anticipating the future locations of users. A resource allocation algorithm is thus proposed, which uses CSI to dynamically allocate network resources to users. To solve this mathematically intractable problem, a novel relaxation method is proposed, which proves to be a useful tool to develop efficient algorithms for network optimization problems with a proportional fairness utility function. The resulting algorithm is extremely faster than the previous method and awareness of mobility enhances the overall performance of the network in terms of rate and fairness utility function.

A Low-Complexity Policy for Outage Probability Minimization With an Energy Harvesting Transmitter

Outage probability in an energy harvesting (EH) block-fading communication system is studied in the finite-horizon online setting. First, the offline version of the problem is considered, and formulated as a mixed integer linear program (MILP). Then, the infinite-horizon online problem (IIL) is considered relaxing the battery constraints. Solutions of these two problems provide lower bounds on the finite-horizon online problem, for which we provide a low-complexity heuristic scheme, called the fixed threshold transmission (FTT) scheme. Numerical results show that the FTT scheme achieves an outage performance close to the MILP lower bound for a wide range of operation regimes, and close to IIL when the EH rate is low. It is also observed that the power allocated by the FTT scheme resembles the optimal offline solution with high probability, despite the lack of information about future channel states and energy arrivals.

Wireless resource sharing for multiple operators: Generalization, fairness, and the value of prediction

Sharing wireless channel resources among cellular network operators saves cost by efficiently utilizing hardware, sites, and spectrum. To achieve high spectral efficiency, resource sharing has to adapt to the instantaneous load and channel state. However, before operators accept such dynamic scheduling policies, a solid understanding of their reliability and performance is required. In this paper, we present a consistent theoretical framework for Multi-Operator Scheduling (MOS). This formulation allows to analyze sharing guarantees and spectral efficiency for a large number of parameters and covers various fixed and dynamic resource sharing policies as special cases. Extending our study to the complete knowledge of the future channel state allows us to study the complete performance region of MOS. The proofs, analysis and simulation results in this work provide a profound understanding of the fundamental tradeoff between sharing guarantees and performance in multi-operator sharing. This insight will enable reliable yet efficient infrastructure sharing in 5G Radio Access Networks.

Enhanced Throughput of Cognitive Radio Networks by Imperfect Spectrum Prediction

Spectrum sensing is used to detect spectrum holes and find active primary users while randomly selecting channel for sensing lead to secondary user's low throughput in high traffic cognitive radio networks. Spectrum prediction forecasts future channel states on the basis of historical information. A new frame structure is proposed in this letter for the imperfect spectrum prediction, resulting to select channels for sensing only from the channels predicted to be idle. Simulation results show that secondary user's throughput is significantly enhanced by imperfect spectrum prediction. The impacts of traffic intensity, prediction errors, and channel number on the throughput are also investigated in this study.

Energy efficient HARQ scheme for wireless communication over block fading channel

Energy efficiency (EE) can be enhanced by retransmissions and combining in hybrid automatic repeat request (HARQ) system. However, it is difficult to optimize the transmit power of each retransmission when the accurate retransmission number and future channel state information (CSI) cannot be obtained. This paper proposes a simple energy efficient HARQ scheme for point-to-point wireless communication. In the proposed scheme, the conditional word error rate (WER) of each retransmission is fixed and the transmit power is adapted Correspondingly. Three performance metrics are analyzed including average transmission number, throughput and EE. Compared with the conventional equal power HARQ scheme, the proposed scheme can significantly improve the EE and other two metrics under the same constraint of average transmit power or average energy consumption. Furthermore, it is found that, selecting a conditional WER which is slightly smaller than the optimal one is sufficient for practical implementation.

An Alternating Variable Step-Size Adaptive Long-Range Prediction of LMS Fading Signals

We propose a linear alternating variable step-size adaptive long-range prediction (AVSS-ALRP) scheme to predict fading signals which is especially suitable for a versatile two-state land mobile satellite (LMS) channel model at S-band. A three-step design procedure is presented to optimize the prediction performance. Firstly, we establish the Gilbert-Elliot channel model based on first-order Markov chain for satellite communication downlink and take advantage of smoothing average to obtain channel observed values. At a second stage, eigenvalue decomposition method is applied to predict future long-range channel state instead of weighted prediction. Finally, combining variable step-size least mean squares and adaptive long-range prediction, we introduce the VSS-ALRP algorithm to predict LMS channel fading signals in the case of “ good” state, and the obtained prediction results would be revised based on the linear prediction of error when shadowing condition is in the “ bad” state. Simulation results show that the proposed scheme can not only offer an accurate prediction for long-range channel state and fading signals over the two-state Gilbert-Elliot channel model and greatly enhance the fading signals’ autocorrelation, but also have considerably better performance than long-range prediction (LRP) algorithm from the results of mean square error (MSE) and correlation coefficient.

Adaptive Long-Range Prediction of Three-State LMS Channel Model

In order to provide the accurate real-time knowledge of future channel state information for the adaptive transmission of narrowband land mobile satellite (LMS) communication systems, an adaptive long-range prediction (ALRP) method, suitable for a three-state LMS channel model at S-band, is proposed. The very slow shadowing conditions' variations of satellite propagation channel can be described as a three-state Gilbert---Elliot channel model based on Markov chain. The channel state of future long-range is predicted based on weighting prediction while the parameters of linear auto-regression model are updated using an iterative adaptive tracking algorithm. The future channel fading series are predicted by the latest observations within current channel state. Simulation results show that the proposed method can be used to predict the future long-range channel state and fading series of three-state Gilbert---Elliot channel model accurately; and it is observed from the mean square error results that the prediction performance of the ALRP method is much more accurate than that of long-range prediction method. Moreover, the method has advantage of real-time and low-complexity which will make the adaptive transmission techniques feasible for LMS channel models.

Wireless Spectrum Occupancy Prediction Based on Partial Periodic Pattern Mining

Cognitive radio appears as a promising technology to allocate wireless spectrum between licensed and unlicensed users in an efficient way. When unlicensed users opportunistically utilize spectrum holes, prediction models that infer the availability of spectrum holes can help to improve the spectrum extraction rate and reduce the collision rate. In this paper, a spectrum occupancy prediction model based on Partial Periodic Pattern Mining (PPPM) is introduced. The mining aims at identifying frequent spectrum occupancy patterns that are hidden in the spectrum usage of a channel. The mined frequent patterns are then used to predict future channel states (i.e., busy or idle). Based on the prediction, unlicensed users are able to utilize spectrum holes aggressively without introducing significant interference to licensed users. PPPM outperforms traditional Frequent Pattern Mining (FPM) by considering real patterns that do not repeat perfectly due to noise, sensing errors, and irregular behaviors. Using real-world Wi-Fi and personal communication service (PCS) activities, we show a significant reduction on miss rate in channel state prediction. With the proposed prediction mechanism, the performance of Dynamic Spectrum Access (DSA) is substantially improved. Further, we extend the three-state PPPM to an N-state PPPM to predict the duration of high/low utilization in a channel. The frequent patterns of channel utilization duration are critical in optimizing channel switch strategies. The high prediction accuracy is validated with data collected in the paging bands.

Adaptive Resource Allocation with Strict Delay Constraints in OFDMA System

We consider the adaptive resource allocation problem in downlink Orthogonal Frequency Division Multiple Access (OFDMA) system with strict packet delay constraints in the range of . In this range of delay constraints, resource optimization has to be simultaneously performed over multiple time slots. Thus optimal allocation decisions require future Channel State Information (CSI) and packet arrival rate information. The causal nature of CSI combined with the increase in the number of optimization variables makes it a very challenging problem. We propose a two-step solution by separating scheduling from subcarrier and power allocation. Our proposed causal scheduler ensures delay guarantees by deriving a minimum data rate out of the user queues while minimizing transmit power in every time slot. The output rates are fed to the resource allocation block and the problem is formulated as a convex optimization problem. The subcarrier and power allocation decisions are made in order to satisfy the demanded rates within the peak power constraint. We address the feasibility of the physical layer resource allocation problem and develop efficient algorithms. When the problem is infeasible we devise a strategy which incurs minimum deviation from the proposed rates for maximum number of users. We show by simulations that our proposed scheme can efficiently utilize time variations as well as multiuser diversity in the system.

Maximizing Lifetime in Relay Cooperation Through Energy-Aware Power Allocation

We study the problem of optimal power allocation among relays for lifetime maximization in a dual-hop cooperative network operated by amplify-and-forward relays with battery limitation. We first formulate the optimization problem for global noncausal power allocation and present a solution based on dual decomposition. In the special case of static channels, we provide a closed-form solution for lifetime maximization, which simply requires equally distributing energy over time for each participating relay. Based on this, we then develop a perceived lifetime (PLT) power allocation strategy, which can be viewed as a causal implementation of the noncausal solution by considering only the current channel state information. We also present a minimum weighted total power (MWTP) strategy that does not depend on the prediction of future channel state. PLT and MWTP are compared through analysis and simulation, and it is demonstrated that both result in lifetime performance close to that of the noncausal optimal solution, and that they significantly outperform the conventional strategy of minimizing the total power per transmission, especially when the link conditions are asymmetric or initial energy levels nonuniform among relays. We further extend the proposed power allocation strategies to relay cooperation with multiple sources and discuss how different network configurations affect relay power sharing among the sources.