<![CDATA[ IEEE Transactions on Wireless Communications - new TOC ]]>
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TOC Alert for Publication# 7693 2019February 14<![CDATA[Table of contents]]>182C1740238<![CDATA[IEEE Transactions on Wireless Communications]]>182C2C284<![CDATA[Spectrum Sensing Using a Uniform Uncalibrated Linear Antenna Array for Cognitive Radios]]>1827417522086<![CDATA[Spatial Configuration of Agile Wireless Networks With Drone-BSs and User-in-the-loop]]>1827537682244<![CDATA[Simultaneous Spectrum Sensing and Energy Harvesting]]>1827697791561<![CDATA[Robust Beamforming Design for Ultra-Dense User-Centric C-RAN in the Face of Realistic Pilot Contamination and Limited Feedback]]>1827807951950<![CDATA[Cross-Technology Communications for Heterogeneous IoT Devices Through Artificial Doppler Shifts]]>1827968062565<![CDATA[A Wireless Optical Backhaul Solution for Optical Attocell Networks]]>1828078232469<![CDATA[Experimental Evaluation of Techniques to Lower Spectrum Consumption in Wi-Red]]>1828248372070<![CDATA[Energy-Efficient Interactive Beam Alignment for Millimeter-Wave Networks]]>1828388511219<![CDATA[Symbol-Level Precoding for Low Complexity Transmitter Architectures in Large-Scale Antenna Array Systems]]>1828528632169<![CDATA[User-Centric Energy Efficiency Optimization for MISO Wireless Powered Communications]]>182864878985<![CDATA[Evaluating SIR in 3D Millimeter-Wave Deployments: Direct Modeling and Feasible Approximations]]>1828798963349<![CDATA[Blind Demixing for Low-Latency Communication]]>1828979111475<![CDATA[A Truthful Mechanism for Scheduling Delay-Constrained Wireless Transmissions in IoT-Based Healthcare Networks]]>1829129251804<![CDATA[Alamouti Coding for DFT Spreading-Based Low PAPR FBMC]]>1829269412321<![CDATA[Impact of Multiple Primaries and Partial CSI on Transmit Antenna Selection for Interference-Outage Constrained Underlay CR]]>1829429531225<![CDATA[Cooperative Authentication in Underwater Acoustic Sensor Networks]]>1829549681630<![CDATA[Downlink MU-MIMO With QoS Aware Transmission: Precoder Design and Performance Analysis]]>1829699821440<![CDATA[Estimation of Primary Channel Activity Statistics in Cognitive Radio Based on Periodic Spectrum Sensing Observations]]>1829839961980<![CDATA[Two-Tier Cellular Networks for Throughput Maximization of Static and Mobile Users]]>data rate of the typical static and mobile (i.e., moving) users, and the latter accounted for handoff outage periods. We consider also the average throughput of these two types of users defined as their average data rates divided by the mean total number of users co-served by the same base station. We find that if the density of a homogeneous network and/or the speed of MUs is high, it is advantageous to let the MUs connect only to some optimal fraction of BSs (i.e., an optimal random subset of BSs) to reduce the frequency of handoffs during which the connection is not assured. If a heterogeneous structure of the network is allowed, one can further jointly optimize the mean throughput of MUs and SUs. This joint optimization is done by appropriately tuning the powers of micro and macro BSs subject to some aggregate power constraint ensuring unchanged mean data rates of SUs via the network equivalence property.]]>18299710101394<![CDATA[Sensor Network-Based Rigid Body Localization via Semi-Definite Relaxation Using Arrival Time and Doppler Measurements]]>182101110251909<![CDATA[Precoding and Detection for Broadband Single Carrier Terahertz Massive MIMO Systems Using LSQR Algorithm]]>182102610402403<![CDATA[Beam Training and Allocation for Multiuser Millimeter Wave Massive MIMO Systems]]>182104110532024<![CDATA[Impact of Small Cells Overlapping on Mobility Management]]>182105410681720<![CDATA[Low-Complexity Truncated Polynomial Expansion DL Precoders and UL Receivers for Massive MIMO in Correlated Channels]]>Conjugate Beamforming (ConjBF) precoding method. On the other hand, it is well-known that in the regime of a large but finite number of antennas, the Regularized Zero-Forcing (RZF) precoding is generally much more effective than ConjBF. In order to close the gap between ConjBF and RZF, while meeting the latency constraint, truncated polynomial expansion (TPE) methods have been proposed. In this paper, we present a novel TPE method that outperforms previously proposed methods in the non-symmetric case of users with different channel correlations, subject to the condition that the covariance matrices of the user channel vectors can be approximated, for a large number of antennas, by a family of matrices with common eigenvectors. This condition is met, for example, by uniform linear and uniform planar arrays in far-field conditions. The proposed method is computationally simple and lends itself to classical power allocation optimization such as min-sum power and max-min rate. We provide a detailed analysis of the computation latency vs computation resources, specifically targeted to a highly parallel FPGA hardware architecture. We conclude that the proposed TPE method can effectively close the performance gap between ConjBF and RZF with computation latency of less than one LTE OFDM symbol, as assumed in Marzetta’s work on massive MIMO.]]>182106910841354<![CDATA[Uplink Performance Analysis in D2D-Enabled Millimeter-Wave Cellular Networks With Clustered Users]]>182108511001416<![CDATA[Secrecy Performance Analysis for Hybrid Wiretapping Systems Using Random Matrix Theory]]>182110111142499<![CDATA[VLC and D2D Heterogeneous Network Optimization: A Reinforcement Learning Approach Based on Equilibrium Problems With Equilibrium Constraints]]>182111511271520<![CDATA[Constructive Interference Optimization for Data-Aided Precoding in Multi-User MISO Systems]]>182112811413555<![CDATA[Novel Method for Multi-Dimensional Mapping of Higher Order Modulations for BICM-ID Over Rayleigh Fading Channels]]>−6. Our mappings also offer a lower error-floor compared to their well-known counterparts.]]>182114211542565<![CDATA[Intelligent MU-MIMO User Selection With Dynamic Link Adaptation in IEEE 802.11ax]]>182115511651528<![CDATA[Joint Sponsored and Edge Caching Content Service Market: A Game-Theoretic Approach]]>182116611812771<![CDATA[Weighted Sum-Rate Maximization for the Ultra-Dense User-Centric TDD C-RAN Downlink Relying on Imperfect CSI]]>182118211981749<![CDATA[Robust Joint Hybrid Transceiver Design for Millimeter Wave Full-Duplex MIMO Relay Systems]]>182119912151641<![CDATA[Physical-Layer Security in Full-Duplex Multi-Hop Multi-User Wireless Network With Relay Selection]]>182121612321345<![CDATA[Online Policies for Energy Harvesting Receivers With Time-Switching Architectures]]>18212331246741<![CDATA[Stochastic Control of Computation Offloading to a Helper With a Dynamically Loaded CPU]]>Internet-of-Things (IoT) devices (e.g., sensors and wearable computing devices) in proximity, thereby overcoming their limitations and lengthening their battery lives. However, unlike dedicated servers, the spare resources offered by edge helpers are random and intermittent. Thus, it is essential to intelligently control a user (IoT device) the amounts of data for offloading and local computing so as to ensure that a computation task can be finished in time-consuming minimum energy. In this paper, we design energy-efficient control policies in a computation offloading system with a random channel and a helper with a dynamically loaded CPU (due to the primary service). Specifically, the policy adopted by the helper aims at determining the sizes of offloaded and locally computed data for a given task in different slots such that the total energy consumption for transmission and local CPU is minimized under a task-deadline constraint. As the result, the polices endow an offloading user robustness against channel-and-helper randomness besides balancing offloading and local computing. By modeling the channel and helper CPU as Markov chains, the problem of offloading control is converted into a Markov decision process. Though dynamic programming (DP) for numerically solving the problem does not yield the optimal policies in closed form, we leverage the procedure to quantify the optimal policy structure and apply the result to design optimal or sub-optimal policies. For three cases ranging from zero, small to large helper buffers, the low complexity of the policies overcomes the “curse of dimensionality” -
n DP arising from joint consideration of channel, helper CPU, and buffer states.]]>182124712621413<![CDATA[Coverage and Handoff Analysis of 5G Fractal Small Cell Networks]]>e.g., 150m, increases obviously with the increase of the effect of anisotropic path loss in 5G fractal small cell networks. Moreover, it is observed that the anisotropic propagation environment is having a profound impact on the handoff performance. Meanwhile, we could conclude that the resulting heavy handoff overhead is emerging as a new challenge for 5G fractal small cell networks.]]>182126312762207<![CDATA[Hybrid LISA for Wideband Multiuser Millimeter-Wave Communication Systems Under Beam Squint]]>182127712881148<![CDATA[Decentralized Coded Caching Without File Splitting]]>182128913031425<![CDATA[Joint Channel Estimation and Tx/Rx I/Q Imbalance Compensation for GFDM Systems]]>182130413171655<![CDATA[Distributed Processing for Multi-Relay Assisted OFDM With Index Modulation]]>182131813311592<![CDATA[Network-Coded NOMA With Antenna Selection for the Support of Two Heterogeneous Groups of Users]]>182133213451870<![CDATA[Cellular UAV-to-X Communications: Design and Optimization for Multi-UAV Networks]]>182134613591601<![CDATA[Orchestrating Resource Management in LTE-Unlicensed Systems With Backhaul Link Constraints]]>182136013751465<![CDATA[Securing UAV Communications via Joint Trajectory and Power Control]]>182137613891573<![CDATA[Wireless Power Transfer by Beamspace Large-Scale MIMO With Lens Antenna Array]]>182139014031592<![CDATA[An Analysis of Two-User Uplink Asynchronous Non-orthogonal Multiple Access Systems]]>182140414181501<![CDATA[Pilot Decontamination in Noncooperative Massive MIMO Cellular Networks Based on Spatial Filtering]]>182141914331839<![CDATA[A Non-Stationary Online Learning Approach to Mobility Management]]>182143414462767<![CDATA[Resource Allocation for Wireless-Powered IoT Networks With Short Packet Communication]]>effective-throughput and effective-amount-of-information as the performance metrics to balance the transmission rate and the packet error rate, and then jointly optimize the transmission time and packet error rate of each user to maximize the total effective-throughput or minimize the total transmission time subject to the users’ individual effective-amount-of-information requirements. To overcome the non-convexity of the formulated problems, we develop efficient algorithms to find high-quality suboptimal solutions for them. The simulation results show that the proposed algorithms can achieve similar performances as that of the optimal solution via exhaustive search, and outperform the benchmark schemes.]]>182144714611679<![CDATA[Introducing IEEE Collabratec]]>182146214622138<![CDATA[IEEE Communications Society]]>182C3C3107<![CDATA[[Blank page]]]>182C4C44