<![CDATA[ IEEE Transactions on Wireless Communications - new TOC ]]>
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TOC Alert for Publication# 7693 2021October 18<![CDATA[Table of contents]]>2010C16239148<![CDATA[IEEE Transactions on Wireless Communications]]>2010C2C2123<![CDATA[Wideband Channel Estimation for IRS-Aided Systems in the Face of Beam Squint]]>2010624062531511<![CDATA[Performance Analysis of NOMA in Vehicular Communications Over i.n.i.d Nakagami-<italic>m</italic> Fading Channels]]>${m}$ fading channels. The performance analysis of NOMA vehicular networks is also extended for multiple-input multiple-output antenna configurations and evaluated in the presence of successive interference cancellation (SIC) error propagation. The obtained analytical results are validated by Monte Carlo simulations. Furthermore, the performance of NOMA is verified with conventional orthogonal multiple access (OMA) for fading parameter $m=1$ and $m=2$ with perfect channel knowledge and channel estimation. Numerical results show that NOMA outperforms the conventional OMA by approximately 20% and has high sum rate with i.n.i.d as well as i.i.d channel consideration. However, i.n.i.d consideration degrades the performance of NOMA and OMA as the diversity gain achieved with i.n.i.d consideration is less as compared to i.i.d consideration. The performance is further deteriorated with SIC er-
or and channel estimation.]]>2010625462682069<![CDATA[Decentralized Frequency Alignment for Collaborative Beamforming in Distributed Phased Arrays]]>2010626962812570<![CDATA[Joint Beam Training and Positioning for Intelligent Reflecting Surfaces Assisted Millimeter Wave Communications]]>2010628262972254<![CDATA[Throughput Analysis and User Barring Design for Uplink NOMA-Enabled Random Access]]>2010629863142129<![CDATA[Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems]]>2010631563281850<![CDATA[Joint Access Point Assignment and Power Allocation in Multi-Tier Hybrid RF/VLC HetNets]]>college admission model is first performed. Then, a distributed and low-complexity algorithm based on matching theory and an efficient heuristic PA scheme are proposed to obtain a good quality suboptimal solution for the joint problem. Simulation results highlight the robustness of the proposed solution and its significant gain in network sum-rate as compared to different benchmark schemes. The effect of various system parameters such as the minimum QoS and maximum illumination requirements on the performance of the proposed solution is studied. Finally, the theoretical analysis of convergence, stability, and complexity of the proposed technique is performed.]]>2010632963422172<![CDATA[Joint Resource Allocation and Trajectory Optimization With QoS in UAV-Based NOMA Wireless Networks]]>2010634363552122<![CDATA[Learning-Based Prediction, Rendering and Association Optimization for MEC-Enabled Wireless Virtual Reality (VR) Networks]]>2010635663702065<![CDATA[Millimeter Wave MIMO-OFDM With Index Modulation: A Pareto Paradigm on Spectral- Energy Efficiency Trade-Off]]>2010637163861749<![CDATA[URLLC With Massive MIMO: Analysis and Design at Finite Blocklength]]>$M$ of antennas grows to infinity, even under pilot contamination. However, numerical results for a practical URLLC network setup involving a base station with $M=100$ antennas, show that a target error probability of 10^{−5} can be achieved with MMSE processing, uniformly over each cell, only if orthogonal pilot sequences are assigned to all the users in the network. Maximum ratio processing does not suffice.]]>2010638764011430<![CDATA[Robust Secure UAV Communications With the Aid of Reconfigurable Intelligent Surfaces]]>$mathcal {S}$ -Procedure, and semidefinite relaxation (SDR) are applied to tackle these non-convex sub-problems. Numerical results demonstrate that the proposed algorithm can considerably improve the average secrecy rate compared with the benchmark algorithms, and also confirm the robustness of the proposed algorithm.]]>2010640264171444<![CDATA[Impact and Calibration of Nonlinear Reciprocity Mismatch in Massive MIMO Systems]]>2010641864351662<![CDATA[Power and Rate Adaptive Pushing Over Fading Channels]]>2010643664502057<![CDATA[Impact of Channel Aging on Cell-Free Massive MIMO Over Spatially Correlated Channels]]>2010645164662398<![CDATA[ALMS Loop Analyses With Higher-Order Statistics and Strategies for Joint Analog and Digital Self-Interference Cancellation]]>2010646764802275<![CDATA[User Scheduling and Beam Alignment in mmWave Networks With a Large Number of Mobile Users]]>$({1}+epsilon)$ approximation of the optimal solution, with smaller $epsilon $ at the cost of longer transmission interval of each user. Lastly, to deal with the case where the assumption on the rate function does not hold due to beam conflicts between the users, we consider a system model that accounts for an angular channel information. A new CMDP is formulated for the problem and a heuristic algorithm based on the age information is proposed.]]>2010648164921137<![CDATA[Bi-Directional Training Methods With Frequency-Division Duplexing]]>2010649365051599<![CDATA[Robust Computation Offloading in Fog Radio Access Network With Fronthaul Compression]]>2010650665211889<![CDATA[Phase Noise in Modular Millimeter Wave Massive MIMO]]>2010652265351897<![CDATA[Eavesdropping in Massive MIMO: New Vulnerabilities and Countermeasures]]>2010653665502441<![CDATA[Dynamic Computation Offloading in Ultra-Dense Networks Based on Mean Field Games]]>2010655165656593<![CDATA[Line-of-Sight Probability for mmWave-Based UAV Communications in 3D Urban Grid Deployments]]>2010656665792126<![CDATA[Caching Efficiency Maximization for Device-to-Device Communication Networks: A Recommend to Cache Approach]]>2010658065941503<![CDATA[Hybrid Interference Mitigation Using Analog Prewhitening]]>$M$ -input $M$ -output analog phase shifter network (PSN) between the receive antennas and the ADCs to spatially prewhiten the interferences, which requires no signal information but only an estimate of the covariance matrix. After interference mitigation by the PSN prewhitener, the preamble can be synchronized, the signal channel response can be estimated, and thus a minimum mean squared error (MMSE) beamformer can be applied in the digital domain to further mitigate the residual interferences. The simulation results verify that the HIMAP scheme can suppress interferences 80dB stronger than the signal by using off-the-shelf phase shifters (PS) of 6-bit resolution.]]>2010659566051464<![CDATA[Machine Learning-Based Resource Allocation in Satellite Networks Supporting Internet of Remote Things]]>2010660666212046<![CDATA[Random Multiple Access With Hierarchical Users]]>2010662266331287<![CDATA[Trajectory Optimization and Resource Allocation for OFDMA UAV Relay Networks]]>2010663466471831<![CDATA[Joint Deployment and Multiple Access Design for Intelligent Reflecting Surface Assisted Networks]]>time-selective nature of the IRS. Furthermore, for all three multiple access schemes, low-complexity suboptimal algorithms are developed by exploiting alternating optimization and successive convex approximation techniques, where a local region optimization method is applied for optimizing the IRS deployment location. Numerical results are provided to show that: 1) near-optimal performance can be achieved by the proposed suboptimal algorithms; 2) asymmetric and symmetric IRS deployment strategies are preferable for NOMA and FDMA/TDMA, respectively; 3) the performance gain achieved with IRS can be significantly improved by optimizing the deployment location.]]>2010664866641617<![CDATA[On-Request Wireless Charging and Partial Computation Offloading In Multi-Access Edge Computing Systems]]>2010666566791762<![CDATA[Adaptive Spatial Scattering Modulation]]>2010668066902154<![CDATA[Fractional Frequency Reuse in Random Hybrid FD/HD Small Cell Networks With Fractional Power Control]]>2010669167051630<![CDATA[A Joint Filter and Spectrum Shifting Architecture for Low Complexity Flexible UFMC in 5G]]>$2^{15}$ . Thus, any desired combination of FFT-length, number of subbands, subband size and filter-length is selected to generate the filter co-efficients for the individual subbands. Moreover, complex multiplication and addition operations are reduced in proposed architecture, quantitatively, about 58.81% reduction in filtering unit is achieved over the state-of-the-art architecture. Finally, hardware implementation output and XILINX post route simulation result matches perfectly with MATLAB simulations.]]>2010670667141524<![CDATA[SLIPT for Underwater Visible Light Communications: Performance Analysis and Optimization]]>2010671567282003<![CDATA[Spectrum-Agile Cognitive Radios Using Multi-Task Transfer Deep Reinforcement Learning]]>2010672967422276<![CDATA[Online Distributed Offloading and Computing Resource Management With Energy Harvesting for Heterogeneous MEC-Enabled IoT]]>2010674367571495<![CDATA[Towards Cost Minimization for Wireless Caching Networks With Recommendation and Uncharted Users’ Feature Information]]>2010675867711349<![CDATA[Theoretical Analysis of In-Band Full-Duplex Radios With Parallel Hammerstein Self-Interference Cancellers]]>2010677267862330<![CDATA[Application of Deep Learning to Sphere Decoding for Large MIMO Systems]]>$K$ -best SD (KSD, FDL-KSD) algorithms. Therein, the major application of DL is to generate a highly reliable initial candidate to accelerate the search in SD and KSD in conjunction with candidate/layer ordering and early rejection. Compared to existing DL-aided SD schemes, our proposed schemes are more advantageous in both offline training and online application phases. Specifically, unlike existing DL-aided SD schemes, they do not require performing the conventional SD in the training phase. For a $24 times 24$ MIMO system with QPSK, the proposed FDL-SD achieves a complexity reduction of more than 90% without any performance loss compared to conventional SD schemes. For a $32 times 32$ MIMO system with QPSK, the proposed FDL-KSD only requires $K = 32$ to attain the performance of the conventional KSD with $K=256$ , where $K$ is the number of survival paths in KSD. This implies a dramatic improvement in the performance–complexity tradeoff of the proposed FDL-KSD scheme.]]>2010678768032017<![CDATA[Dynamic Aggregation for Heterogeneous Quantization in Federated Learning]]>2010680468191957<![CDATA[Online Learning-Based Reconfigurable Antenna Mode Selection Exploiting Channel Correlation]]>2010682068341802<![CDATA[Time Scheduling and Energy Trading for Heterogeneous Wireless-Powered and Backscattering-Based IoT Networks]]>block coordinate descent and convex-concave procedure techniques to design two partitioning schemes (i.e., partial adjustment (PA) and joint adjustment (JA)) to find the optimal energy price and service time that constitute local SEs. Numerical results reveal that by jointly optimizing the energy trading and time allocation for IoT devices, one can achieve significant improvements in terms of the IoTSP’s profit compared with those of conventional transmission methods (up to 38.7 folds). Different tradeoffs between the ESP’s and IoTSP’s profits and complexities of the PA/JA schemes can also be numerically tuned. Simulations also show that the obtained local SEs approach the optimal social welfare when the benefit per transmitted bit exceeds a given threshold.]]>2010683568512132<![CDATA[Centralized Dynamic-Time Division Duplex Utilizing Interference Alignment]]>$N$ full duplex nodes. We maximize the performance of the proposed centralized D-TDD scheme utilizing IA in terms of rate region by optimizing the reception, transmission, simultaneous reception and transmission, and silence at each node in each time slot in addition to the choice of whether a node should treat the interference as noise or it should use IA. The problem of the rate region maximization of the wireless network is formulated as a non-convex optimization problem, whose optimal solution is found. The simulation results demonstrate that the proposed centralized D-TDD scheme utilizing IA achieves significant gains over the existing D-TDD schemes.]]>2010685268661161<![CDATA[Frequency-Domain Signal Processing for Spectrally-Enhanced CP-OFDM Waveforms in 5G New Radio]]>2010686768834018<![CDATA[Enhancing Ambient Backscatter Communication Utilizing Coherent and Non-Coherent Space-Time Codes]]>2010688468971466<![CDATA[DeepFake: Deep Dueling-Based Deception Strategy to Defeat Reactive Jammers]]>2010689869142030<![CDATA[wChain: A Fast Fault-Tolerant Blockchain Protocol for Multihop Wireless Networks]]>$mathit {wChain}$ , a blockchain protocol specifically designed for multihop wireless networks that deeply integrates wireless communication properties and blockchain technologies under the realistic SINR model. We adopt a hierarchical spanner as the communication backbone to address medium contention and achieve fast data aggregation within $O(log Nlog Gamma)$ slots where $N$ is the network size and $Gamma $ refers to the ratio of the maximum distance to the minimum distance between any two nodes. Besides, $mathit {wChain}$ employs data aggregation and reaggregation as well as node recovery mechanisms to ensure efficiency, fault tolerance, persistence, and liveness. The worst-case runtime of $mathit {wChain}$ is upper bounded by $O(flog Nlog Gamma)$ , where $f=lfloor frac {N}{2} rfloor $ is the upper bound of the number of faulty nodes. To validate our design, we conduct both theoretical analysis and simulation studies. The results not only demonstrate the nice properties of $mathit {wChain}$ , but also point to a large new space for the exploration of blockchain protocols in wireless networks.]]>2010691569261351<![CDATA[Mobility-Enhanced Simultaneous Lightwave Information and Power Transfer]]>2010692769394213<![CDATA[Capacity Characterization for Reconfigurable Intelligent Surfaces Assisted Multiple-Antenna Multicast]]>2010694069536034<![CDATA[Energy-Efficient mm-Wave Backhauling via Frame Aggregation in Wide Area Networks]]>2010695469702716<![CDATA[IEEE Communications Society]]>2010C3C3111