<![CDATA[ IEEE Communications Letters - new TOC ]]>
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TOC Alert for Publication# 4234 2019April 22<![CDATA[Table of contents]]>234C1555287<![CDATA[IEEE Communications Society]]>234C2C288<![CDATA[An Analytical Model for Rank Distribution in Sparse Network Coding]]>234556559524<![CDATA[Error Performance Prediction of Randomly Shortened and Punctured LDPC Codes]]>234560563550<![CDATA[More Functions With Three-Valued Walsh Transform From Linear Combinations]]>234564567519<![CDATA[Reducing Search Complexity of Coded Caching by Shrinking Search Space]]>234568571556<![CDATA[Multi-Permutation Codes Correcting a Single Burst Unstable Erasure]]>$t$ -balanced multi-permutation into the sub-problems of a single permutation-invariant erasure (PIE) in $t$ permutations. Based on the rank demodulation method, we propose two classes of $t$ -balanced multi-permutation codes to correct a single BUE of length up to $t$ by interleaving $t$ single-PIE-correcting permutation codes. The decoding methods for the proposed codes are included in the proofs and demonstrated by examples.]]>234572575219<![CDATA[Cell-State-Distribution-Assisted Threshold Voltage Detector for NAND Flash Memory]]>234576579775<![CDATA[An Enhanced ARQ Scheme for A-MPDU Transmission Under Error-Prone WLANs]]>234580583562<![CDATA[Round Trip Time Prediction Using Recurrent Neural Networks With Minimal Gated Unit]]>234584587807<![CDATA[An Intelligent QoS Algorithm for Home Networks]]>234588591772<![CDATA[Closed-Form BER Expression for Fourier and Wavelet Transform-Based Pulse-Shaped Data in Downlink NOMA]]>234592595490<![CDATA[Pulse Position-Based Spatial Modulation for Molecular Communications]]>234596599618<![CDATA[Collaborative Distributed Q-Learning for RACH Congestion Minimization in Cellular IoT Networks]]>234600603718<![CDATA[Impact of Virtualization Technologies on Virtualized RAN Midhaul Latency Budget: A Quantitative Experimental Evaluation]]>2346046071132<![CDATA[Non-Alternating Globally Optimal MMSE Precoding for Multiuser VLC Downlinks]]>234608611634<![CDATA[Analysis of Policy-Based Security Management System in Software-Defined Networks]]>2346126151345<![CDATA[Modeling MPTCP Performance]]>234616619622<![CDATA[An Efficient Dynamic Anti-Collision Protocol for Mobile RFID Tags Identification]]>234620623821<![CDATA[NOMA-Based Irregular Repetition Slotted ALOHA for Satellite Networks]]>234624627682<![CDATA[AN-Aided Secure Beamforming Design for Correlated MISO Wiretap Channels]]>234628631322<![CDATA[Secure Multicast Communications in Cognitive Satellite-Terrestrial Networks]]>234632635368<![CDATA[Joint Trajectory and Communication Design for Secure UAV Networks]]>234636639432<![CDATA[Closed-Form Expression of Stochastic CRB for Mixed Near-Field and Far-Field Sources in Multipath Propagation Environments]]>2346406431524<![CDATA[A Widely Linear MMSE Anti-Collision Method for Multi-Antenna RFID Readers]]>234644647769<![CDATA[Compressive Multi-Timeslots Data Gathering With Total Variation Regularization for Wireless Sensor Networks]]>234648651802<![CDATA[Deep Learning-Based Channel Estimation]]>234652655649<![CDATA[Fast Maximum Likelihood Detection of the Generalized Spatially Modulated Signals Using Successive Sphere Decoding Algorithms]]>234656659521<![CDATA[A Novel Unitary PARAFAC Algorithm for Joint DOA and Frequency Estimation]]>234660663621<![CDATA[Middle Subarray Interference Covariance Matrix Reconstruction Approach for Robust Adaptive Beamforming With Mutual Coupling]]>234664667487<![CDATA[Iterative Channel Estimation Algorithm For Downlink MC-CDMA Systems With Two-Path Successive Relaying Transmission]]>a priori probabilities to refine the estimation process, without causing overhead to the demodulation execution. The simulation experiments are carried out to assess the effectiveness of the proposed estimator.]]>234668671431<![CDATA[Massive MIMO Channel Estimation Over the mmWave Systems Through Parameters Learning]]>234672675565<![CDATA[Analog Full-Duplex Amplify-and-Forward Relay for Power Line Communication Networks]]>234676679756<![CDATA[Performance Analysis of Time Reversal Communication Systems]]>234680683517<![CDATA[Joint Resource Allocation and Offloading Decision in Mobile Edge Computing]]>234684687375<![CDATA[Max–Min Fairness Optimal Rate-Energy Trade-Off of SWIPT for Massive MIMO Downlink]]>234688691476<![CDATA[Outage Probability for Cooperative NOMA Systems With Imperfect SIC in Cognitive Radio Networks]]>234692695483<![CDATA[A Leader Improves Things: Reducing Signaling and Complexity in Optimal IoT Storage]]>$O(s^{2})$ to $O(s)$ for $s$ remaining nodes, compared to using no leader for coordination.]]>234696699401<![CDATA[Proactive Caching Strategy With Content-Aware Weighted Feature Matrix Learning in Small Cell Network]]>2347007031508<![CDATA[Energy-Saving Computation Offloading by Joint Data Compression and Resource Allocation for Mobile-Edge Computing]]>234704707586<![CDATA[Energy-Efficient Sleep Strategy With Variant Sleep Depths for Open-Access Femtocell Networks]]>234708711483<![CDATA[Online Learning-Based Energy-Efficient Frame Aggregation in High Throughput WLANs]]>frame aggregation. However, different lengths of aggregated frames demand a variety of energy-budgets. Moreover, the carrier sense multiple access with collision avoidance is an energy-consuming channel access scheme in WLAN. In this letter, we propose an online learning-based frame aggregation, Intelligent Energy-Efficient Frame Aggregation (IE2FA), to design an energy-efficient MAC for HT-WLAN. The simulation analysis of IE2FA shows that it can improve network performance significantly compared to the other related works mentioned in the literature.]]>234712715734<![CDATA[Optimal Bin Width for Autonomous Coverage Estimation Using MDT Reports in the Presence of User Positioning Error]]>234716719740<![CDATA[Fair and Social-Aware Message Forwarding Method in Opportunistic Social Networks]]>234720723683<![CDATA[VariLoc: Path Loss Exponent Estimation and Localization Using Multi-Range Beaconing]]>sensitivity level of a receiver device for sensing the channel and the transmission power, instead of the more commonly used RSSI measurements, which can be very noisy. This letter shows that VariLoc is robust against multipath fading and can estimate the PLE accurately, even in the presence of very deep fading. This letter further introduces a centroid localization algorithm that utilizes VariLoc to obtain estimates for both the PLE and the position of a target node. The performance of the proposed approach is compared with the state-of-the-art localization algorithms and Cramer–Rao bound using simulation and real-world experimentation using Bluetooth beacons.]]>234724727876<![CDATA[Opportunistic Mobility Utilization in Flying Ad-Hoc Networks: A Dynamic Matching Approach]]>234728731706<![CDATA[Energy Efficiency–Delay Tradeoff for a Cooperative NOMA System]]>234732735487<![CDATA[A Homogeneous Multi-Radio Rendezvous Algorithm for Cognitive Radio Networks]]>234736739571<![CDATA[eMBB-URLLC Resource Slicing: A Risk-Sensitive Approach]]>i.e., puncturing the current eMBB transmission) and cannot be queued due to its hard latency requirements. In this letter, we propose a risk-sensitive based formulation to allocate resources to the incoming URLLC traffic, while minimizing the risk of the eMBB transmission (i.e., protecting the eMBB users with low data rate) and ensuring URLLC reliability. Specifically, the Conditional Value at Risk (CVaR) is introduced as a risk measure for eMBB transmission. Moreover, the reliability constraint of URLLC is formulated as a chance constraint and relaxed based on Markov’s inequality. We decompose the formulated problem into two subproblems in order to transform it into a convex form and then alternatively solve them until convergence. Simulation results show that the proposed approach allocates resources to the incoming URLLC traffic efficiently, while satisfying the reliability of both eMBB and URLLC.]]>234740743664<![CDATA[Characterization of Bacteria Signal Propagation With an Absorbing Wall]]>234744747516<![CDATA[A Low Complexity Signal Detection Scheme Based on Improved Newton Iteration for Massive MIMO Systems]]>234748751625<![CDATA[TDOA-/FDOA-Based Adaptive Active Target Localization Using Iterated Dual-EKF Algorithm]]>234752755620<![CDATA[SSK-Based SWIPT With AF Relay]]>234756759582<![CDATA[Performance Analysis of Relay Selection in Cooperative NOMA Networks]]>234760763481<![CDATA[Analysis of Massive MIMO With Low-Resolution ADC in Nakagami-<inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> Fading]]>$m$ fading. By means of moment matching, the signal-to-interference-plus-quantization-noise ratio at the output of maximum ratio combiner is approximated by a gamma random variable. Using this approximate probability density function, tight approximations for metrics, such as outage probability and rate, are derived. The gap between the simulation and the approximate result is negligible. In addition, for a large number of antennas, a decrease in the number of antennas should be accompanied by a proportionate decrease in the threshold to maintain the same outage probability.]]>234764767474<![CDATA[Low-Complexity Beam Selection Algorithms for Millimeter Wave Beamspace MIMO Systems]]>234768771439<![CDATA[Comment on “Bayesian MMSE Estimation of a Gaussian Source in the Presence of Bursty Impulsive Noise”]]>k is determined according to the maximum a posteriori (MAP) decision rule based on the whole sequence of observations. Then, given the hard decision on the noise state, the corresponding MMSE estimator (i.e., conditional mean) is employed to estimate the source signal at time epoch k. However, performing estimation based on the hard decision of the MAP detector is not optimal in terms of minimizing the mean-squared-error (MSE). In this comment, we show that the optimal MMSE estimator for the considered problem is obtained as a weighted average of the MMSE estimators corresponding to distinct states of the noise probable at time epoch k and the weights are equal to the a posteriori probabilities of the noise state computed based on the whole sequence of observations.]]>234772772171<![CDATA[IEEE Communications Society]]>234C3C377