Electrostatic discharge (ESD) power clamps are critical for ensuring the reliability of modern integrated circuits (ICs). This paper presents an intelligent and automated design methodology that features application-driven topology selection tailored to diverse performance priorities and full-chain automation, from design specifications to silicon validation. The proposed approach begins with selecting representative circuit topologies, each tailored to specific application scenarios with distinct performance priorities. These topologies form a set of curated candidates defined by formalized design rules and established ESD protection principles. Subsequently, an optimization algorithm refines the dimensions of the device according to given specifications, while a professional floorplan and routing ensure high-current handling layout implementation. Compared with conventional manual design, the proposed methodology achieves superior design flexibility, comprehensive automation, and optimized performance, delivering balanced trade-offs among performance, power, and area (PPA). Post-layout simulations and silicon measurements in a 40-nm CMOS process validate the methodology, demonstrating the effectiveness of the intelligent algorithm in ESD protection design.

Phase noise (PN) significantly degrades the performance of millimeter-wave (mmWave) wireless communication systems. To mitigate this challenge, we propose the amplitude-phase-sphere block modulation (APSBM) scheme, which integrates amplitude-shift keying, phase-shift keying, and sphere modulation. Exploiting the Wiener random walk characteristic of PN, APSBM carries information on the relative amplitude and phase between consecutive symbols, thereby counteracting the impact of common PN. Immunity to residual PN in the scheme is further enhanced through a three-dimensional spherical constellation and modulation configurations. We also propose flexible detection strategies (joint, parallel, and cascaded) at the receiver to accommodate diverse application scenarios, minimizing the influence of PN while maintaining a low computational burden. Simulation and experimental results demonstrate that in the presence of PN, APSBM outperforms quadrature amplitude modulation (QAM), circular QAM, and spiral modulation, achieving a lower peak-to-average power ratio, a reduced bit error rate, and a higher achievable information rate. The proposed modulation and demodulation scheme is particularly effective under high PN conditions, offering a robust solution for PN mitigation in mmWave wireless communication systems.

In OFDM wireless communications, power amplifier nonlinearity generates significant out-of-band (OOB) emissions and in-band distortion, conventionally requiring large power back-offs to address both issues simultaneously. We propose a forward distortion compensation (FDC) scheme using amplitude-phase-frequency block modulation (APFBM) that decouples OOB emission suppression from in-band distortion management, enabling reliable transmission even under severe power amplifier nonlinearity. At the transmitter, in-band distortion is intentionally introduced to facilitate aggressive OOB emission suppression, while APFBM manages bit mapping and imposes a power-sum constraint on information-carrying subcarriers. At the receiver, this power-sum constraint guides the compensation for in-band distortion, ensuring reliable information recovery. This compensation strategy also relaxes digital pre-distortion (DPD) accuracy requirements, permitting a simpler Sigmoid-based static DPD focused solely on OOB suppression and eliminating feedback circuits. The experimental results show a relative gain of 4.4 dB with a power back-off of 5.2 dB compared to nonlinear transmission based on quadrature amplitude modulation with equivalent processing at 4.9 GHz, while a relative gain of 2.6 dB is achieved at 26.5 GHz. This approach achieves superior energy efficiency and spectral efficiency trade-offs, enhancing coverage and performance in wireless communications.

Operating power amplifiers (PAs) at lower input back-off (IBO) levels is an effective way to improve PA efficiency, but often introduces severe nonlinear distortion that degrades transmission performance. Amplitude-phase-time block modulation (APTBM) has recently emerged as an effective solution to this problem. The intrinsic amplitude and phase constraints of each APTBM block can be leveraged to mitigate PA-induced nonlinear distortion via constraint-guided signal reconstruction. However, existing reconstruction methods apply these constraints only heuristically and statistically, limiting the achievable IBO reduction and PA efficiency improvement. This paper addresses this limitation by decomposing the nonlinear distortion into dominant and residual components, and accordingly develops a novel two-stage signal reconstruction algorithm consisting of coarse and fine reconstruction stages. The coarse reconstruction stage eliminates the dominant distortion by jointly exploiting the APTBM block structure and PA nonlinear characteristics. Subsequently, the fine reconstruction stage minimizes the residual distortion by casting it as a nonconvex optimization problem subject to explicit APTBM constraints, for which a closed-form solution is derived. The proposed algorithm is validated through comprehensive numerical simulations and testbed experiments. Results show that, without compromising transmission quality, the proposed algorithm enables an additional IBO reduction of approximately 5 dB in simulations and 2 dB in experiments over baseline methods, yielding relative PA efficiency improvements of 77.8% and 30.9%, respectively.

Reconfigurable intelligent surface (RIS)-enabled systems have been widely considered as one of the revolutionary technologies for the new generation of communications. In particular, RIS-assisted multiple-input multiple-output (MIMO) communications have come at the forefront of research, yet there is still lack of supportive channel measurements and modeling in real environments. Against this background, this paper conducts multi-frequency and multi-scenario channel measurements and channel modeling for RIS-assisted MIMO communication systems. Utilizing a temporal autocorrelation-based channel sounder and the fabricated RISs, the virtual RIS-assisted MIMO channels are realized by successively moving the transceiver antennas on movable rotary tables. The channel realizations are collected in indoor hotspot (InH) and urban microcellular (UMi) scenarios at sub-6 GHz and millimeter-wave (mmWave) frequency bands, respectively, where various communication states, different coding schemes of the RIS, as well as with and without RIS deployment are fully considered. Based on the measured channel realizations, critical channel metrics including the signal power, effective rank, spectral efficiency (SE), root-mean-square delay-spread (RMS DS), RiceanK-factor (KF), spatial correlation, etc., are illustrated and compared under different coding schemes, communication states, deployment scenarios, and frequency bands. The measurement results indicate that the coding scheme of the RIS significantly impacts the channel performance, while the capability of RIS to customize the channel is strongly related to the power intensity it provides. Deploying an RIS with energy-focused coding can provide significant signal power gains for non-line-of-sight (NLoS) links and spatial multiplexing gains for line-of-sight (LoS) and obstructed-line-of-sight (OLoS) links, thereby greatly improving the SE. Moreover, it is found that under different communication states, such as RIS-assisted NLoS/LoS/OLoS links, the influence imposed by RIS on the channel metrics could be completely opposite, especially for the KF, RMS DS, and effective rank. In addition, after the RIS deployment with a beamforming mode, the spatial correlation in the NLoS and LoS MIMO channels significantly increases and decreases, respectively. Furthermore, it is verified that the Weichselberger model can provide a satisfactory prediction accuracy on the SE of RIS-assisted MIMO channels, while the Kronecker model underestimates it.

This paper presents channel measurement campaigns and spatial channel characterization of reconfigurable intelligent surface (RIS)-assisted millimeter-wave multiple-input multiple-output (MIMO) systems. Utilizing a channel sounder and an RIS, the RIS-assisted MIMO channels are constructed in an indoor non-line-of-sight scenario. By rotating a narrow-beam directional antenna (DA) in the azimuth angle domain, the spatial signal distributions are captured. Meanwhile, multiple comparative experiments, including: wideband vs narrowband (NB) and DA vs omnidirectional antenna, are conducted. The influence of RIS deployment and different coding schemes is considered. Based on such channel realizations, spatial channel metrics, including the power azimuth spectrum (PAS), root mean square angular spread (RMS AS), root mean square delay spread (RMS DS), spatial correlation coefficient (SCC), effective rank, and etc., are thoroughly investigated. Measurement results show that, using the DA, NB signal, and beamforming provided by the RIS contributes to a lower RMS DS, a higher SCC, and a lower effective rank. A truncated Laplacian function is used to describe the PAS, indicating prominent signal strength improvements in both the primary direction facing RIS and the opposite direction to RIS. The RMS ASs are well-fitted by a generalized extreme value distribution, which manifest a distance-dependent variation in the space domain.

Reconfigurable intelligent surfaces (RISs) are widely recognized as a cutting-edge technology for the sixth-generation mobile communication systems (6G). In this paper, we investigate the effective channel gain and spatial multiplexing gain of multi-RIS-assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) channels. A channel model for multi-RIS-assisted MIMO system is introduced. We propose two discrete phase design methods for multi-RIS-assisted MIMO systems. The factors influencing spatial multiplexing gain, including the power difference and spatial similarity between different virtual line-of-sight (VLOS) paths, are thoroughly analyzed. Numerical simulations and measurement experiments are conducted in three scenarios, analyzing the impact of the RIS unit number as well as the positions of the RISs and antennas. The results indicate that jointly considering the position information of multiple RISs in discrete phase design leads to a higher effective channel gain, whereas relying only on the position of each single RIS exhibits instability. These findings highlight the positive role of sufficient location information and joint phase design in multi-RIS-assisted systems. Moreover, the results demonstrate that reduced power difference and spatial similarity among VLOS paths enhance the effective rank of the cascaded mmWave MIMO channel.

Cloud-edge collaboration has become a pivotal approach in edge computing, especially for applications needing real-time inference. This holds particular significance in scenarios where continuous and concurrent inference tasks have stringent latency demands. This paper considers a scenario where dynamically arriving tasks can be partially offloaded to a server with parallel computing for inference. We build a dynamic model for each task's inference process and formulate the problem of minimizing the long-term delay violation rate. To address the strategy problem of joint splitting and batch scheduling for multiple users, we adopt a sequential decision-making approach which effectively lowers the computational complexity. Meanwhile, we innovatively devise a wireless resource pool and a batch scheduling linked list to characterize the system's overall computing and communication resources. On this basis, we can evaluate different task splitting and batch scheduling strategies to make more effective decisions. Simulation results demonstrate a significant improvement in latency guarantees with millisecondlevel execution efficiency and robust scalability across diverse user scales, outperforming baseline methods under heterogeneous workloads.

Reconfigurable intelligent surface (RIS) is regarded as one of the promising technologies for the sixth-generation mobile communication systems (6G) due to its ability to reshape the wireless channel and improve the communication transmission rate. In this paper, we investigate the spatial characteristic of an RIS-assisted millimeter wave (mmWave) multiple-input multipleoutput (MIMO) channel through channel measurement in practical environment. The channel measurement campaigns are carried out in an indoor hotspot (InH) non-line-of-sight (NLOS) scenario at 35 GHz. We compare and analyze the channel parameters in three propagation modes, including intelligent reflection with RIS (IRWR), specular reflection with RIS (SRWR), and without RIS (WR). Different types of receiver (RX) antenna are compared, including the horn antenna and the omni-directional antenna. Signals with bandwidths of 300 MHz and 10 MHz are transmitted to compare the spatial characteristics of wideband and narrowband signals. The measured channel frequency response (CFR) reveals that the RIS coding scheme, antenna type, and signal bandwidth are significant factors influencing the spatial correlation of the wireless channel. The measured power azimuth spectrum (PAS), fitted by truncated Laplacian functions, demonstrates that the deployment of RIS with energy-focused phase configuration results in an amplification of the power in the primary signal propagation direction. Moreover, the analysis of the root mean square (RMS) angular spread shows that it increases with the growing distance between the RX and RIS.

A large proportion of the carbon emissions associated with wireless communications stem from electricity consumption during operation. Spectral efficiency (SE) and energy efficiency (EE) are fundamental considerations in wireless communications. However, nonlinear amplification results in a trade-off between these factors. Various techniques have been developed to address this issue, including the frequently-used amplifier linearization. Nonetheless, these approaches have limitations in terms of versatility and complexity, making them impractical for modern broadband multiantenna wireless communications. Here, an amplitude-phase-time block modulation (APTBM) scheme and a corresponding demodulation scheme for resisting amplifier nonlinearity are proposed, establishing a new paradigm for balancing the SE and EE. At the transmitter, the symbol block, consisting of two time-domain consecutive symbols, is used to carry information. Simultaneously, specific amplitude and phase constraints are imposed on the symbols within a block. At the receiver, nonlinearly distorted symbols can be effectively demodulated by utilizing these constraints. Numerical and experimental results show that the proposed APTBM demonstrates excellent nonlinear transmission characteristics compared with conventional offset quadrature amplitude modulation.

Passing-object detection is a basic function in an intelligent environment. However, as one of the main functions in an integrated sensing and communication system, it is still challenging to achieve due to the dense multipath propagation in typical indoor environments. First, a Fresnel zone model and a diffraction model are constructed from indoor radio wave propagation characteristics to estimate the first Fresnel zone maximum (FFZM) and phase difference (PD), both of which enable the utilization of antenna pairs to determine the existence and passing direction of an object. Next, using a mixture of the FFZM and PD, a joint detection algorithm (JDA) for passing objects is proposed for a multiantenna system, in which the dynamic time warping method is applied to obtain the signal similarity between antennas. In the preprocessing stage of the proposed JDA, the minimum delay sequence is used to extract the passing period, and an improved trilinear parallel factor decomposition method is used to remove multipath interference. For experimental demonstration, the proposed JDA is implemented using a software radio platform and applied to WLAN sensing. The measurement results show that the proposed JDA can achieve very low missing alarm and direction error rates for both single-passing and multipassing scenarios.

Multiple direction-of-arrival estimation is one of the core functions in array signal processing and has many engineering applications. It is proposed that it can be realized using a two-stage strategy which consists of multiclass classification for region segmentation and successive cancellation-based fine estimation in subregions. In the first stage, the deep convolutional network (DCN) is introduced to classify the 2D angles into discrete subregions of the arrival plane. To enhance the DCN performance, the K-means clustering algorithm is applied to label 2D angles. The DCN is trained using the dataset with only one signal which can considerably reduce training complexity and dataset size. The trained DCN can be generalized for multisignal scenarios. In the second stage, the orthogonal matching pursuit algorithm is utilized to estimate the 2D angles in each subregion. To estimate multiple signals, orthogonal projection transformation is employed to successively cancel the estimated signals. Numerical results demonstrate that the proposed DCN-assisted multiple direction-of-arrival estimation method has lower complexity with better performance than the reduced-dimension MUSIC algorithm.

Since the data rates in advanced chips dramatically increase, the electrostatic-discharge (ESD) protection circuit at I/O ports causes significant ultrawideband challenges. To address these challenges, an arbitrarily tapped coil (AT-coil) structure with multitaps that enhance bandwidths is proposed; moreover, this structure can also fit into any rectangular layout area to save footprint. A microwave network-assisted analysis approach, which can better consider the nonideal factors in practical applications and thus converge to a better solution than traditional methods, is proposed with regard to the AT-coil. Further, machine learning-assisted optimization (MLAO) is introduced for the designing of AT-coil layouts for the first time, and prior human knowledge of high-speed circuits is maximally utilized to speed up MLAO processes. Thus, the converging stability is significantly improved to escape from a local optimum. Additionally, an automatic synthesis example for an on-chip spiral AT-coil proves the capability. Numerical results show that the proposed approach can achieve the best-high-frequency performance and guarantee ESD protective robustness.

The statistical gap between existing correlation-based stochastic channel models (CBSMs) and practical propagation is a longstanding issue, especially for massive multiple-input multiple-output (MIMO) transmission. To address this problem, a pervasively correlated channel model (PCCM) applicable to MIMO channels with arbitrary antenna configurations and scenarios is proposed. Unlike the conventional jointly correlated channel model (JCCM) based on an independently and identically distributed (i.i.d.) random matrix, a new random matrix whose elements are independent and nonidentically distributed (i.n.d.) generalized Gamma complex Gaussian mixture (GGCGM) variables with correlated envelopes is used to approximate the real channel statistics better. Moreover, the PCCM can be simplified via the Rayleigh fading assumption for rapid evaluation of channel performance and supports backward compatibility with existing CBSMs under specific assumptions. Demonstrative numerical experiments for MIMO channels are conducted based on the 3GPP TR 38.901 model considering various scenarios, frequencies, and antenna configurations. The channel capacity distributions are obtained based on random samples generated using the geometry-based stochastic channel model (GBSM), the JCCM, and the proposed PCCM. The numerical results show that the PCCM is more flexible than the JCCM with respect to high-order statistics, enabling more accurate estimation of massive MIMO transmission channel performance.

The angle of arrival (AoA) is a vital parameter for geometry-based 3-D indoor positioning. Considering a wireless local area network (WLAN) as an example, a conventional antenna array designed for multiple-input–multiple-output (MIMO) communication is not suitable for AoA estimation due to the angle ambiguity introduced by the large interelement spacing (IES). Consequently, the integration of positioning and communication functions using the same radio frequency (RF) frontend and the array would be highly valuable. A nonuniform array (NUA) is proposed to solve this challenging problem. The NUA is optimized to achieve unambiguous angle estimation while maintaining high-capacity WLAN MIMO communication. Moreover, based on the estimated MIMO channel state information (CSI), an improved trilinear parallel factor (TPF) decomposition method is proposed to accurately estimate AoAs with low complexity. Simulated and experimental results show that the proposed NUA-based scheme can achieve decimeter-level 3-D positioning accuracy.

An inductor is one of the basic passive circuit elements that make up integrated circuits. A hybrid knowledge-guided and data-driven technique (HKDT) that combines prior knowledge of on-chip inductors with a machine learning approach is proposed for designing a millimeter-wave on-chip deformable spiral inductor (DSI). First, a DSI, whose shape is no longer limited to a regular polygon and can be adapted to any rectangular region, is presented. Next, an expanded Wheeler formula is proposed to estimate the inductance of the DSI. Then, two approximate expressions with fitting parameters are deduced for the frequency responses of the inductance and quality factor. After that, two prior knowledge-guided Gaussian process regression (GPR) surrogate models are presented for the inductance and quality factor, with which the feature dimensions of the training data can be significantly reduced. Both models can achieve higher prediction accuracies in the frequency range of interest with lower computational complexity than the traditional GPR model with frequency-domain features when they are used for machine learning-assisted optimization (MLAO). Finally, an automatic inductor synthesis method is implemented by using a multibranch MLAO algorithm, and two examples are presented to validate the proposed synthesis method and illustrate its great capabilities. Ultimately, the HKDT can be extended to and applied for the efficient synthesis of other circuit elements with high accuracy.

Beamforming can flexibly change the beamwidth and beam direction to achieve the best system performance in the sixth generation (6G) mobile communication. The asymmetric communication systems based on all-digital beamforming have huge advantages, such as more user access in uplink and better signal-to-noise ratio (SNR) in downlink. For asymmetric communication systems, accurate channel models are especially important. In this paper, we apply the space-time-frequency non-stationary channel model to the asymmetric channels by generating correlated parameters in the uplink and downlink with different antenna patterns. Then we investigate the scattering distributions and statistical properties with different antenna array patterns. The simulation results indicate that the scattering distributions and correlation functions are greatly affected by antenna patterns.

As the requirements related to resolution or the data rate increase, most angle-of-arrival (AoA) estimation problems can be assumed to be under a broadband signal model. In this paper, AoA estimation using a nonuniform planar array (NUPA), which aims to resolve more signals and reduce mutual coupling, is considered under a broadband signal model. The analysis is conducted in both the space and frequency domains, and a broadband co-array is proposed to improve the performance of the AoA estimation. The broadband co-array is generated as follows: First, the received broadband signal is decomposed into several narrowband signals, and the samples from different frequency bins are transformed to space-domain samples to generate a virtual array. Then, the second-order statistics of the virtual array are used to generate the broadband co-array. The virtual array can decrease the number of element pairs with small interelement spacing to reduce mutual coupling in AoA estimations. With the help of the virtual array, the following broadband co-array can greatly increase the degrees of freedom, resulting in more resolvable signals and lower Cramer-Rao bounds of AoA estimation. An optimization method based on sparse recovery is proposed to locate the array. The simulation results confirm the AoA estimation performance achieved by the designed NUPA.

Orthogonal time frequency space (OTFS) as an emerging modulation technique in the 5G and beyond era exploits full time-frequency diversity and is robust against doubly-selective channels in high mobility scenarios. In this work, we consider an OTFS modulation based automotive joint radar-communication system and focus on the design of low-complexity parameter estimation algorithm for radar targets. It is well known that target parameter estimation in OTFS radar is computationally much more expensive than the orthogonal frequency division multiplex based platform, which hampers low-cost and real-time implementation. In this context, an efficient Bayesian learning scheme is proposed for OTFS automotive radars, which leverages the structural sparsity of radar channel in the delay-Doppler domain. We also reduce the dimension of the measurement matrix by incorporating the prior knowledge on the motion parameter limit of the true targets. Numerical simulation results are presented to demonstrate the superior performance of the proposed method in comparison with the state-of-the-art.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

In this paper, a novel three-dimensional (3D) space-time-frequency (STF) non-stationary geometry-based stochastic model (GBSM) is proposed for the sixth generation (6G) terahertz (THz) wireless communication systems. The proposed THz channel model is very general having the capability to capture different channel characteristics in multiple THz application scenarios such as indoor scenarios, device-to-device (D2D) communications, ultra-massive multiple-input multiple-output (MIMO) communications, and long traveling paths of users. Also, the generality of the proposed channel model is demonstrated by the fact that it can easily be reduced to different simplified channel models to fit specific scenarios by properly adjusting model parameters. The proposed general channel model takes into consideration the non-stationarities in space, time, and frequency domains caused by ultra-massive MIMO, long traveling paths, and large bandwidths of THz communications, respectively. Statistical properties of the proposed general THz channel model are investigated. The accuracy and generality of the proposed channel model are verified by comparing the simulation results of the relative angle spread and root mean square (RMS) delay spread with corresponding channel measurements.

The deployment of millimeter-wave (mmWave) cellular systems in dense urban environments with an acceptable coverage and cost-efficient transmission scheme is essential for the rollout of fifth-generation and beyond technology. In this paper, cluster-based analysis of mmWave channel characteristics in two typical dense urban environments is performed. First, radio propagation measurement campaigns are conducted in two identified mmWave bands of 28 and 39 GHz in a central business district and a dense residential area. The custom-designed channel sounder supports high-efficiency directional scanning sounding, which helps collect sufficient data for statistical channel modeling. Next, using an improved auto-clustering algorithm, multipath clusters and their scattering sources are identified. An appropriate measure for inter- and intra-cluster characteristics is provided, which includes the cluster number, the Ricean K -factor, root-mean-squared (RMS) delay spread, RMS angular spread, and their correlations. Comparisons of these parameters across two mmWave bands for both line-of-sight (LoS) and non-light-of-sight (NLoS) links are given. To shed light on the blockage effects, detailed analysis of the propagation mechanisms corresponding to each NLoS cluster is provided, including reflection from exterior walls and diffraction over building corners and rooftops. Finally, the results show that the cluster-based analysis takes full advantage of mmWave beamspace channel characteristics and has further implications for the design and deployment of mmWave wireless networks.

The clustered delay line channel model, in which each cluster consists of many rays, is widely used for link-level evaluations in mobile communications. Multiple parameters of each ray, including the delay, amplitude, cross-polarization ratio (XPR), initial phases of four polarization combinations, and the azimuth and elevation angles of arrival and departure, must be known and are measured using a channel sounder. The number of rays in every cluster is usually greater than the number of elements in the antenna array of the channel sounder, which represents a challenging issue in multipolarized channel measurements. Based on the broadband extended array response of an electromagnetic vector antenna array, a new subspace estimation method is proposed to resolve a large number of rays. The inter-element spacing of the array can be greater than half the carrier wavelength, which reduces inter-element coupling and simplifies the array design, especially for millimeter-wave bands. The delay of each cluster is first estimated using the reference antenna element. Next, two-dimensional angles of every ray are estimated using the classic rank-deficient multiple signal classification algorithm. Finally, the initial phases, XPR, and amplitude of every ray are estimated. Simulation results validate the proposed method.

Terahertz (THz) communication is now being considered as one of possible technologies for the sixth generation (6G) communication systems. In this paper, a novel three-dimensional (3D) space-time-frequency non-stationary massive multiple-input multiple-output (MIMO) channel model for 6G THz indoor communication systems is proposed. In this geometry-based stochastic model (GBSM), the initialization and evolution of parameters in time, space, and frequency domains are developed to generate the complete channel transfer function (CTF). Based on the proposed model, the correlation functions including time auto-correlation function (ACF), spatial cross-correlation function (CCF), and frequency correlation function (FCF) are investigated. The results show that the statistical properties of the simulation model match well with those of the theoretical model. The stationary intervals at different frequencies are simulated. The non-stationarity in time, space, and frequency domains is verified by theoretical derivations and simulations.

The deployment of millimeter-wave (mmWave) wireless communication systems in urban macrocell scenarios with large coverage and cost-efficient transmission schemes is investigated with a focus on space-time propagation characteristics. First, channel measurement campaigns are conducted at two 5G main candidate frequency bands of 28 GHz and 39 GHz in a central business district and a residential area, based on our new-designed time domain channel sounder, which can support directional scanning sounding with less time consumption. Next, using a reasonable data preprocessing method, small-scale channel characteristics across line-of-sight (LoS) and non-LoS (NLoS) links are analyzed via power delay angular profiles, root mean square delay spread, and azimuth and elevation angular spread. Measurement and analysis results show that space-time propagation parameters are layout-related and have further implications on the mmWave system design.

Spectral efficiency (SE) and energy efficiency (EE) are both essential in future wireless communications. To improve the system performance on these two metrics, in this paper we consider the weighted sum rate maximization (WSRMax) problem subject to per-cell EE constraints and per-BS transmit power constraints in multicell multiuser downlink systems. This problem is difficult in its original form due to the introduction of new EE constraints. We first reveal that the original problem can be transformed into an equivalent parameterized polynomial form by introducing some auxiliary variables. By exploiting the concavity property with respect to each variable in the equivalent problem, an efficient block coordinate ascent algorithm is then proposed with guaranteed convergence property. Numerical results show that compared with the conventional WSRMax algorithm, in addition to fulfilling the EE requirement of each cell, our algorithm achieves a better system EE performance at the cost of a slight sum rate performance loss at a certain region and offers a new insight on the SE-EE tradeoff in wireless communication systems.

Energy-efficient communications has attracted much interest in the research of 5G cellular systems. In this paper, we study energy-efficient coordinated beamforming design for heterogeneous multicell multiuser downlink systems. The considered problem is formulated as maximizing the weighted sum per-cell energy efficiencies (WSPEEMax) subject to predefined per-user target rate demands, maximum leakage interference power constraints, and per-BS transmit power constraints. This formulation is more general than the conventional EE optimization problem and provides a unified way to consider the EE of heterogeneous networks. However, it is hard to tackle due to the weighted sum-of-ratios form of the objective function and the non-convex nature of per-user target rate constraints. To address it, we propose to first transform the original problem into a polynomial form optimization by introducing some auxiliary variables and then further reveal their equivalence in finding the solution. Then, an efficient block coordinate ascent optimization algorithm is developed to solve the equivalent problem by exploiting the concave nature of the considered problem with respect to each optimization variable. To further improve the network EE, we also develop an energy-efficient transmission method for each small-cell network. Finally, extensive numerical results are provided to verify the effectiveness of the proposed schemes and show that both the EE and spectral efficiency (SE) of heterogeneous network can be significantly improved by energy-efficient coordinated multiple-input multiple-output (MIMO) transmission.

Capacity optimization of line-of-sight (LoS) 3×2 multiple-input-multiple-output (MIMO) channels in indoor environments is investigated in this paper. First, the capacity fluctuation number (CFN) which reflects the change of capacity is proposed. Next, the expression of capacity against the CFN is derived. The CFN is used as a criterion for optimization of capacity by changing inter-element spacings of transmit and receive antenna arrays. The capacity sensitivity to the orientation is studied and compared with that of 2×2 MIMO channel. A small sensitivity of 3 × 2 MIMO channel is achieved and verified by both simulation and measurement results. Furthermore, the CFN can also be used as a criterion for optimization of average capacity and the proposed optimization method is validated through numerical results.

In this paper, a new analytical expression of the max-min SINR duality between the multi-cell downlink and the virtual uplink subject to per-BS power constraints is firstly given. Based on that, a hierarchical iterative scheme is proposed to solve the virtual uplink optimization problem. The uplink solution is then converted to achieve the solution to the multi-cell downlink beamforming problem. Simulation results show that, in contrast to existing multi-cell beamforming schemes, the proposed scheme achieves better performance in terms of both the worst-user rate and the rate per energy.

In this paper, a novel unified channel model framework is proposed for cooperative multiple-input multiple-output (MIMO) wireless channels. The proposed model framework is generic and adaptable to multiple cooperative MIMO scenarios by simply adjusting key model parameters. Based on the proposed model framework and using a typical cooperative MIMO communication environment as an example, we derive a novel geometry-based stochastic model (GBSM) applicable to multiple wireless propagation scenarios. The proposed GBSM is the first cooperative MIMO channel model that has the ability to investigate the impact of the local scattering density (LSD) on channel characteristics. From the derived GBSM, the corresponding multi-link spatial correlation functions are derived and numerically analyzed in detail.

In this paper, we address the preamble-based channel estimation problem for orthogonal frequency-division multiplexing (OFDM) systems using multiple amplify-and-forward (AF) relays. We first establish a linear minimum mean-square-error (LMMSE) channel estimator which directly estimates the overall channels from the source to the destination. Then, based on the assumption that the channel correlations between the relays are unknown at the source terminal, a suboptimal training sequence and precoding matrices in closed form are designed to approach the optimal estimation performance at low-complexity.

In practical orthogonal frequency division multiplexing (OFDM) systems, some virtual subcarriers are reserved for easing the requirements on the filter. In this case, the conventional discrete Fourier transform (DFT)-based filtering in frequency-domain suffers from a spectral leakage, which results in an irreducible error floor. On the other hand, the DFT-based filtering or interpolation in time-domain also has performance degradation for system with high Doppler frequency. In order to solve these two problems, we propose a two dimensional discrete cosine transform (2D DCT)-based channel estimator for OFDM systems with virtual subcarriers and high Doppler frequency. Simulation results show that the performance of the proposed method can well approach the 2D minimum mean square error (MMSE) channel estimation.

The performance of linear receivers for detection of the single carrier frequency division multiple access transmission over frequency-selective fading channels is investigated in this work. Firstly, the cumulative distribution functions (CDF) of output signal to interference plus noise ratios (SINR) with zero forcing frequency domain equalization (ZF-FDE) and minimum mean squared error frequency domain equalization (MMSE-FDE) are derived by employing the numerical inversion of Laplace transforms. Next, based on the CDFs of the output SINRs, the approximate average bit error rate expressions as functions of the average receive signal to noise ratio for Gray-coded quaternary phase shift keying constellations are obtained. Finally, analytical and Monte Carlo simulated results are compared, and they perfectly agree for both ZF-FDE and MMSE-FDE.

Low complexity channel estimation for single-carrier block transmission systems over multiple-input multiple-output time varying frequency-selective channels is investigated. A time slot structure that uses Golay complementary sequences with perfect periodic autocorrelations as two-sided pilot blocks is presented. Employing this strucutre, optimal least square estimate in the minimum mean square error (MMSE) sense is achieved. Furthermore, a computationally efficient algorithm which is named as fast periodic Golay correlation is proposed based on the specific generator and the properties related to circulant matrices. Finally, the simulation results show the MMSE performance of the proposed scheme and algorithm.