<![CDATA[ IEEE Transactions on Wireless Communications - new TOC ]]>
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TOC Alert for Publication# 7693 2019April 22<![CDATA[Table of contents]]>184C1C4235<![CDATA[IEEE Transactions on Wireless Communications]]>184C2C284<![CDATA[A Message and Mission Statement From the New Editor-in-Chief]]>18420192020151<![CDATA[On the Uplink Max–Min SINR of Cell-Free Massive MIMO Systems]]>184202120362012<![CDATA[SDN-Enabled MIMO Heterogeneous Cooperative Networks With Flexible Cell Association]]>1842037205029993<![CDATA[Enhancing Performance of Random Caching in Large-Scale Wireless Networks With Multiple Receive Antennas]]>184205120651424<![CDATA[Distributed Cyclic Delay Diversity Systems With Spatially Distributed Interferers]]>184206620791665<![CDATA[Learning-Aided Multiple Time-Scale SON Function Coordination in Ultra-Dense Small-Cell Networks]]>${M}$ time-scale Markov decision process, where SON decisions made in each time-scale consider the impacts of SON decisions in other ${M}-{1}$ time scales on the network. Furthermore, in order to manage the network more autonomously and efficiently, a Q-learning algorithm for SON functions in the proposed MTCS scheme is proposed to achieve a stable control policy by learning from history experience. To improve energy efficiency, we then evaluate the proposed MTCS scheme with two functions of mobility load balancing and energy saving management with designed network utility. The simulation results show that the proposed SON coordination scheme significantly improves the network utility with different quality of experience requirements while guaranteeing stable operations in wireless networks.]]>184208020922451<![CDATA[Uplink Power Control and Ergodic Rate Characterization in FD Cellular Networks: A Stochastic Geometry Approach]]>184209321102417<![CDATA[Leakage Rate Analysis for Artificial Noise Assisted Massive MIMO With Non-Coherent Passive Eavesdropper in Block-Fading]]>184211121241098<![CDATA[Interference Management for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach]]>184212521401677<![CDATA[Energy Efficient Power Allocation With Demand Side Coordination for OFDMA Downlink Transmissions]]>184214121551940<![CDATA[3-D Energy Optimal Receiver Placement With Constraints on the LOS Delay and Angle]]>184215621692488<![CDATA[Expeditious Estimation of Angle-of-Arrival for Hybrid Butler Matrix Arrays]]>184217021854311<![CDATA[Low-Overhead Hierarchically-Sparse Channel Estimation for Multiuser Wideband Massive MIMO]]>184218621992140<![CDATA[Online Learning-Based Downlink Transmission Coordination in Ultra-Dense Millimeter Wave Heterogeneous Networks]]>184220022141683<![CDATA[Low-Complexity Coordinated Relay Beamforming Design for Multi-Cluster Relay Interference Networks]]>184221522281141<![CDATA[Wireless Channel Modeling Perspectives for Ultra-Reliable Communications]]>−9. In this paper, we analyze the tail of the cumulative distribution function of block fading channels in the regime of extremely rare events, i.e., the ultra-reliable (UR) regime of operation. Our main contribution consists of providing a unified framework for statistical description of wide range of practically important wireless channel models in the UR regime of operation. Specifically, we show that the wireless channel behavior in this regime can be approximated by a simple power law expression, whose exponent and offset depend on the actual channel model. The unification provides a channel-agnostic tool for analyzing and performance optimization of radio systems that operate in the UR regime. Furthermore, the unified model is particularly useful in the emerging measurement campaigns for empirical characterization of wireless channels in the regime of low outages. Finally, the asymptotic analysis can serve as an underlying building block for designing more elaborate, higher-layer technologies for URC. We showcase this by applying the power law results to analyze the performance of receiver diversity schemes and obtain a new simplified expression for maximum ratio combining.]]>184222922431498<![CDATA[An Online Optimization Framework for Distributed Fog Network Formation With Minimal Latency]]>184224422582115<![CDATA[OFDM-IM Based Dual-Hop System Using Fixed-Gain Amplify-and-Forward Relay With Pre-Processing Capability]]>184225922701652<![CDATA[Partial-Duplex Amplify-and-Forward Relaying: Spectral Efficiency Analysis Under Self-Interference]]>partial-duplex relaying mode encompasses half- and full-duplex as particular cases. By viewing the partial-duplex relay as a bandwidth-preserving linear periodically time-varying system, a spectral efficiency analysis under self-interference is developed. In contrast with previous works, self-interference is regarded as a useful information-bearing component rather than simply assimilated to noise. This approach reveals that previous results regarding the impact of self-interference on (full-duplex) relay performance are overly pessimistic. Based on a frequency-domain interpretation of the effect of self-interference, a number of suboptimal decoding architectures at the destination node are also discussed. It is found that the partial-duplex relaying mode may provide an attractive tradeoff between spectral efficiency and receiver complexity.]]>184227122851125<![CDATA[High-Resolution OFDM-Based Sensor Node Ranging Within In-Homogeneous Media of Human Body]]>184228622982629<![CDATA[Outage Analysis and Finite SNR Diversity-Multiplexing Tradeoff of Hybrid-Duplex Systems for Aeronautical Communications]]>184229923131169<![CDATA[Backscatter Data Collection With Unmanned Ground Vehicle: Mobility Management and Power Allocation]]>184231423281617<![CDATA[Energy Minimization for Wireless Communication With Rotary-Wing UAV]]>fly-hover-communicate design, where the UAV successively visits a set of hovering locations and communicates with one corresponding GN while hovering at each location. For this design, we propose an efficient algorithm to optimize the hovering locations and durations, as well as the flying trajectory connecting these hovering locations, by leveraging the travelling salesman problem with neighborhood and convex optimization techniques. Next, we consider the general case, where the UAV also communicates while flying. We propose a new path discretization method to transform the original problem into a discretized equivalent with a finite number of optimization variables, for which we obtain a high-quality suboptimal solution by applying the successive convex approximation technique. The numerical results show that the proposed designs significantly outperform the benchmark schemes.]]>184232923451764<![CDATA[Convex Relaxation Methods for Unified Near-Field and Far-Field TDOA-Based Localization]]>184234623602449<![CDATA[Modeling and Analysis of Differential CQI Feedback in 4G/5G OFDM Cellular Systems]]>184236123731158<![CDATA[Two-Phase Random Access Procedure for LTE-A Networks]]>184237423871761<![CDATA[Resource Allocation for Full-Duplex Systems With Imperfect Co-Channel Interference Estimation]]>184238824001248<![CDATA[User Scheduling for Millimeter Wave Hybrid Beamforming Systems With Low-Resolution ADCs]]>184240124141141<![CDATA[QoS-Aware User Association and Resource Allocation in LAA-LTE/WiFi Coexistence Systems]]>184241524303439<![CDATA[Joint Path Selection and Rate Allocation Framework for 5G Self-Backhauled mm-wave Networks]]>how to select the best multi-hop paths and how to allocate rates over these paths subject to latency constraints? In this regard, a new system design, which exploits multiple antenna diversity, mm-wave bandwidth, and traffic splitting techniques, is proposed to improve the downlink transmission. The studied problem is cast to as a network utility maximization, subject to the upper delay bound constraint, network stability, and network dynamics. By leveraging stochastic optimization, the problem is decoupled into: 1) path selection and 2) rate allocation sub-problems, whereby a framework which selects the best paths is proposed using reinforcement learning techniques. Moreover, the rate allocation is a non-convex program, which is converted into a convex one by using the successive convex approximation method. Via mathematical analysis, the comprehensive performance analysis and convergence proof are provided for the proposed solution. The numerical results show that the proposed approach ensures reliable communication with a guaranteed probability of up to 99.9999% and reduces latency by 50.64% and 92.9% as compared to baseline models. Furthermore, the results showcase the key tradeoff between latency and network arrival rate.]]>184243124452148<![CDATA[Corrections to “Outage Probability and Rate for <inline-formula> <tex-math notation="LaTeX">$kappa$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$mu$ </tex-math></inline-formula> Shadowed Fading in Interference Limited Scenario” [Dec 17 8289-8304]]]>$E_{D}$ term in [1, eqs. (6) and (27)] and the correct expression is given below:]]>18424462447151<![CDATA[Introducing IEEE Collabratec]]>184244824482131<![CDATA[IEEE Access]]>184244924491287<![CDATA[Member Get-A-Member (MGM) Program]]>184245024503451<![CDATA[IEEE Communications Society]]>184C3C375