Cell-free massive MIMO (CF-mMIMO) has emerged as a promising technology for next generation wireless systems, combining the benefits of distributed antenna systems (DAS) and traditional MIMO technology. In this work, we present the first extensive channel measurements for CF-mMIMO in the mid-band (FR3, 6-24 GHz), using a virtual widely distributed antenna array comprising 512 elements in the urban Macrocell (UMa) environment. Based on the measurement data, this paper compares the channel capacity of CF-mMIMO and Conventional mMIMO under both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions across a range of signal-to-noise ratios (SNRs). We then analyze how channel capacity varies with Rx positions from the perspectives of the full array and of individual subarrays. Finally, we conclude that the 64-element array configuration yields the greatest advantage in channel capacity for CF-mMIMO in the measurement environment considered, with gains of 14.02\% under LOS and 24.61\% under NLOS conditions. This in-depth analysis of channel capacity in the FR3 band provides critical insights for optimizing CF-mMIMO systems in next generation wireless networks.

In this paper, we analyze the periodic transmission masks for MASked Modulation (MASM) in half-duplex integrated sensing and communication (ISAC), and derive their closed-form expected range-Doppler response $\mathbb{E}\{r(k,l,ν)\}$. We show that range sidelobes ($k\neq l$) are Doppler-invariant, extending the range-sidelobe optimality to the 2-D setting. For the range mainlobe ($k=l$), periodic masking yields sparse Doppler sidelobes: Cyclic difference sets (CDSs) (in particular Singer CDSs) are minimax-optimal in a moderately dynamic regime, while in a highly dynamic regime the Doppler-sidelobe energy is a concave function of the mask autocorrelation, revealing an inevitable tradeoff with mainlobe fluctuation.

Integrated sensing and communication (ISAC) enables numerous innovative wireless applications. Communication-centric design is a practical choice for the construction of the sixth generation (6G) ISAC networks. Continuous-wave-based ISAC systems, with orthogonal frequency-division multiplexing (OFDM) being a representative example, suffer from the self-interference (SI) problem, and hence are less suitable for long-range sensing. On the other hand, pulse-based half-duplex ISAC systems are free of SI, but are also less favourable for high-throughput communication scenarios. In this treatise, we propose MASked Modulation (MASM), a half-duplex ISAC waveform design scheme, which minimises a range blindness metric, termed as “mainlobe fluctuation”, given a duty cycle (proportional to communication throughput) constraint. In particular, MASM is capable of supporting high-throughput communication (∼50% duty cycle) under mild mainlobe fluctuation. Moreover, MASM can be flexibly adapted to frame-level waveform designs by operating on the slow-time scale. In terms of optimal transmit mask design, a set of masks is shown to beidealin the sense of sidelobe level and mainlobe fluctuation intensity.

Next generation cellular deployments are expected to exploit the 6–24 GHz frequency range 3 (FR3) and extremely large-scale multiple-input multiple-output (XL-MIMO) to enable ultra-high data rates and reliability. However, the significantly enlarged antenna apertures and higher carrier frequencies make the far-field and spatial stationarity assumptions in the existing 3rd generation partnership project (3GPP) channel models no longer valid, giving rise to new features such as near-field propagation and spatial non-stationarity (SNS). Despite extensive prior research, incorporating these new features within the standardized channel modeling framework remains an open issue. To address this, this paper presents a channel modeling framework for XL-MIMO systems that incorporates both near-field and SNS features, adopted by 3GPP. For the near-field propagation feature, the framework models the distances from the base station (BS) and user equipment to the spherical-wave sources associated with clusters. These distances are used to characterize element-wise variations of path parameters, such as nonlinear changes in phase and angle. To capture the effect of SNS at the BS side, a stochastic-based approach is proposed to model SNS caused by incomplete scattering, by establishing power attenuation factors from visibility probability and visibility region to characterize antenna element-wise path power variation. In addition, a physical blocker-based approach is introduced to model SNS effects caused by partial blockage. The near-field and SNS channel modeling approaches are validated against ray-tracing simulations. Finally, a simulation framework for near-field and SNS is developed based on the existing 3GPP channel model. Performance evaluations demonstrate that the near-field model captures higher channel capacity potential compablack to the far-field model. Coupling loss results indicate that SNS leads to more pronounced propagation fading relative to the spatial stationary model.

Accurate radar cross section (RCS) modeling is crucial for characterizing target scattering and improving the precision of Integrated Sensing and Communication (ISAC) channel modeling. Existing RCS models are typically designed for specific target types, leading to increased complexity and lack of generalization. This makes it difficult to standardize RCS models for 3GPP ISAC channels, which need to account for multiple typical target types simultaneously. Furthermore, 3GPP models must support both system-level and link-level simulations, requiring the integration of large-scale and small-scale scattering characteristics. To address these challenges, this paper proposes a unified RCS modeling framework that consolidates these two aspects. The model decomposes RCS into three components: (1) a large-scale power factor representing overall scattering strength, (2) a small-scale angular-dependent component describing directional scattering, and (3) a random component accounting for variations across target instances. We validate the model through mono-static RCS measurements for UAV, human, and vehicle targets across five frequency bands. The results demonstrate that the proposed model can effectively capture RCS variations for different target types. Finally, the model is incorporated into an ISAC channel simulation platform to assess the impact of target RCS characteristics on path loss, delay spread, and angular spread, providing valuable insights for future ISAC system design.

Terahertz (THz) extremely large-scale MIMO (XL-MIMO) is considered a key enabling technology for 6G and beyond due to its advantages such as wide bandwidth and high beam gain. As the frequency and array size increase, users are more likely to fall within the near-field (NF) region, where the far-field plane-wave assumption no longer holds. This also introduces spatial non-stationarity (SnS), as different antenna elements observe distinct multipath characteristics. Conventional far-field stationary models with fixed path parameters fail to capture these variations. Therefore, this paper proposes a THz XL-MIMO channel model that accounts for both NF propagation and SnS, validated using channel measurement data. In this work, we first conduct THz XL-MIMO channel measurements at 100 GHz and 132 GHz using 301- and 531-element ULAs in indoor environments, revealing pronounced NF effects characterized by nonlinear inter-element phase variations, as well as element-dependent delay and angle shifts. Moreover, the SnS phenomenon is observed, arising not only from blockage but also from inconsistent reflection or scattering. Based on these observations, a hybrid NF channel modeling approach combining the scatterer-excited point-source model and the specular reflection model is proposed to capture nonlinear phase variation of different types of non-line-of-sight (NLoS) paths. For SnS modeling, amplitude attenuation factors (AAFs) are introduced to characterize the continuous variation of path power across the array. By analyzing the statistical distribution and spatial autocorrelation properties of AAFs, a statistical rank-matching-based method is proposed for their generation. Finally, the model is validated using measured data. Evaluation across metrics such as entropy capacity, condition number, spatial correlation, channel gain, Rician K-factor, and RMS delay spread confirms that the proposed model closely aligns with measurements and effectively characterizes the essential features of THz XL-MIMO channels.

The digital twin channel (DTC) aims to map the physical scene into a real-time digital twin (DT) and enables artificial intelligence (AI)-driven channel prediction. To meet diverse channel acquisition requirements in mmWave systems, we propose TwinPAS, a DT-based hierarchical power angular spectrum (PAS) prediction method for multi-band coexistence communications. TwinPAS enables the base station to convert visual sensing information into spatial channel characteristics through two stages: DT construction and PAS prediction. Firstly, in the DT construction, static scene information is obtained through computer vision techniques, while dynamic environmental features are derived from the estimation of sub-6 GHz channel parameters. To fuse the two heterogeneous features to construct the DT of the physical scene, a novel channel representation method, called channel embedding, is presented. Then, in the PAS prediction, a hierarchical channel prediction network is developed to predict the mmWave PAS in descending order of path powers. For the network output, the embedded mmWave PAS is projected into the virtual distance matrix, thereby solving the problem of uncertain channel parameter dimensions that arises when the number of multipaths changes dynamically. Simulation results demonstrate the superiority and generalization of the TwinPAS approach for PAS prediction, showing its potential for physical layer applications, such as beam selection.

In this paper, we studyrobustbeamforming design fornear-fieldphysical-layer-security (PLS) systems, where a base station (BS) equipped with an extremely large-scale array (XL-array) serves multiple near-field legitimate users (Bobs) in the presence of multiple near-field eavesdroppers (Eves). Unlike existing works that mostly assume perfect channel state information (CSI) or location information of Eves, we consider a more practical and challenging scenario in this paper, where the locations of Bobs are perfectly known, while onlyimperfect location informationof Eves is available at the BS. We first formulate a robust optimization problem to maximize the sum-rate of Bobs while guaranteeing a worst-case limit on the eavesdropping rate under location uncertainty. By transforming Cartesian position errors into the polar domain, we reveal an important near-fieldangular-error amplification effect, i.e., under the same location error, the closer the Eve, the larger the angle error, which severely degrades the performance of conventional robust beamforming methods based on imperfect channel state information. To address this issue, we first establish the conditions for which the first-order Taylor approximation of the near-field channel steering vector under location uncertainty is largely accurate. Then, we propose atwo-stagerobust beamforming method, which first partitions the uncertainty region into multiple fan-shaped sub-regions, followed by the second stage to formulate and solve a refined linear-matrix-inequality (LMI)-based robust beamforming optimization problem. In addition, the proposed method is further extended to scenarios with multiple Bobs and multiple Eves. Finally, numerical results validate that the proposed method achieves a superior trade-off between rate performance and secrecy robustness, hence significantly outperforming existing benchmarks under Eve location uncertainty.

The distributed network architecture allows cell-free massive MIMO(mMIMO) with multiple ways to combine uplink signals. The decision feedback scheme uses the detected signal of the distributed combiner to estimate the equivalent channel at the central processing unit (CPU), and iteratively updates the symbols using the estimated channel state information (CSI). The performance of this algorithm greatly depends on the accuracy of initial signal detection. In this paper, we first propose a pilot-based signal combining (PC) scheme at the CPU to improve the estimation accuracy of the symbols. We use pilot signals instead of detected signal to estimate the equivalent channel at the CPU. The estimated data symbols is then used as the training input in the decision feedback scheme, enabling iterative refinement of the equivalent CSI. Simulation results show that the proposed PC scheme can significantly reduce the system bit error rate compared to the decision feedback scheme with lower computational complexity and less decoding latency, albeit with slightly increase fronthaul signaling load. Furthermore, continuing the iterative process with the decision feedback scheme after initial PC further reduces the bit error rate, albeit with added decoding latency and computational complexity similar to the standard decision feedback scheme.

With the full automation transformation of the railway industry and its close connection to the goals of high data rate, high reliability, and ultra-low latency communication, the demand for wireless communication technology in railway communication is increasing, especially in terms of accuracy in electromagnetic environment reconstruction. The accuracy of ray tracing (RT)-based electromagnetic environment reconstruction depends on the accuracy of electromagnetic (EM)) properties. Therefore, we propose an enhanced algorithm, which combines the advantages of logistic chaotic (LC) algorithm and simulated annealing (SA) algorithm. The proposed algorithm first utilizes the traversal property of LC algorithm for global search, and then uses SA algorithm for fine search for optimal solution. We validate the accuracy and applicability of algorithm in railway scenario by comparing the measurement results with the simulation results based on RT. After calibration, it will reduce the root mean square error between the measured parameters and the parameters of RT, and quickly converge to the minimum value.

Shore-based communication is an essential component of the Space-air-ground integrated network(SAGIN). However, due to complex marine environmental factors such as wave motion and tidal effects, it’s challenging to accurately model its channel characteristics. Therefore, this paper conducted a wideband shore-to-ship channel measurement experiment with 4 GHz frequency and 100 MHz bandwidth. To deeply analyze the impact of waves and tides on the channel, a ship attitude recorder was introduced for the first time to capture the real-time attitude information of the ship. Based on the measured data and combined with the Kirchhoff approximation theory, a path loss model in the nearshore area was established and proved better than the fitting effect of the two-ray model. Moreover, the delay spread of the channel was analyzed, showing that its 90% value is within 185.64 ns. The paper contributes to the development of a more realistic and detailed model of dynamic maritime channels.

In this paper, we investigate the visible light-based integrated sensing and communication (ISAC) channel characteristics in a vehicle-to-vehicle (V2V) scenario, focusing on monostatic sensing and bi-static sensing modes. The ISAC channel is divided into a target channel and a background channel. The channel impulse response (CIR) at different distances, as well as the fading characteristics, multipath correlation, and delay spread of the channel within the effective sensing distance in the two sensing modes are investigated by ray tracing simulation. The results show that with the increase of distance, the target channel received power ratio of mono-static sensing decreases by 30%, while the received power ratio of bi-static sensing is stable at about 1.06%, the target channel fading of mono-static sensing is faster than that of bi-static sensing. In addition, the linear fit coefficient of the root mean square (RMS) delay spread (DS) values for mono-static sensing is positive with a mean value of 2.95 which is greater than the mean value of 0.32 for the RMS DS values for bi-static sensing, while the linear fit coefficient of the RMS DS values for bi-static sensing is negative. Overall, the bi-static sensing system outperforms at longer distances, has a more stable target signal received power ratio, better handling target signals and background noise, reducing signal RMS DS, and improving transmission quality and anti-interference capabilities, indicating the advantages of bi-static sensing in V2V scenarios using visible light-based ISAC.

Integrated Sensing and Communication (ISAC), as a fundamental technology of 6G, empowers Vehicle-to-Everything (V2X) systems with enhanced sensing capabilities. One of its promising applications is the reliance on constructed maps for vehicle positioning. Traditional positioning methods primarily rely on Line-of-Sight (LOS), but in urban vehicular scenarios, obstructions often result in predominantly Non-Line-of-Sight (NLOS) conditions. Existing researched indicate that NLOS paths, characterized by one-bounce reflection on building wall with determined delay and angle, can support sensing and positioning. However, experimental validation remains insufficient. To address this gap, channel measurements are conducted in an urban street to explore the existence of strong reflected paths in the presence of a vehicle target. The results show significant power contribution from NLOS paths, with large Environmental Objects (EOs) playing a key role in shaping NLOS propagation. Then, a novel model for EO reflection is proposed to extend the Geometry-Based Stochastic Model (GBSM) for ISAC channel standardization. Simulation results validate the model's ability to capture EO's power and position characteristics, showing that higher EO-reflected power and closer distance to Rx reduce Delay Spread (DS), which is more favorable for positioning. This model provides theoretical guidance and empirical support for ISAC positioning algorithms and system design in vehicular scenarios.

Abstract With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, divided into three progressive stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies. We compare wireless network large models with conventional large language models (LLMs), and identify key design principles and components for building wireless network architectures. In the final stage, AI as a service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we define the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we analyze the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

With the rapid proliferation of Internet of Things (IoT) devices, overlapping spectrum signals (OSS) have become increasingly prevalent due to concurrent transmissions over the same frequency bands. Accurate recognition of OSS, including their categories, positions, and transmission powers, is crucial for spectrum management and interference mitigation in dense wireless environments. This paper proposes an integrated deep signal recognition (IDSR) architecture based on deep non-negative matrix factorization (Deep-NMF) for multi-parameter OSS estimation. To reduce computational complexity and accelerate iterative convergence, we further develop a collaborative multi-layer signal recognition (CMSR) architecture that decomposes OSS features through a three-layer structure, enabling successive estimation of category, position, and power. Additionally, we incorporate a Dempster-Shafer (D-S) based fusion strategy for signal category recognition and introduce a consensus-constrained NMF algorithm for collaborative localization across monitoring nodes. Particle swarm optimization (PSO) is employed to initialize the power and position estimation to enhance accuracy. Experimental results demonstrate that both IDSR and CMSR outperform traditional blind source separation methods (e.g., PCA, ICA, and standard NMF) and classical deep learning models (CNN and Transformer) in OSS recognition. Specifically, the proposed methods achieve up to 95.6% accuracy in signal classification under SNR = 4 dB, with comprehensive performance gains. Moreover, CMSR attains comparable accuracy to IDSR with lower computational complexity, indicating its potential for real-time edge deployment. The impact of monitoring node placement, signal power ratios, and deployment density is also analyzed, providing practical insights for spectrum monitoring in IoT networks.

Multiple-input-multiple-output (MIMO) has been a promising technology in wireless communication systems. Channel models are of great importance for the development and assessment of system. With the increase of carrier frequency and MIMO size, the channel model needs to consider near-field spherical wave and spatial non-stationary characteristics, which is different from conventional far-field planar-wave-based geometry-based stochastic model (GBSM) in the 3rd Generation Partnership Project (3GPP). This paper focuses on comparing the channel characteristics and modeling in the far- and near-field region. In this work, we design the measurement campaign in the 6 GHz band (5.9-6.1 GHz) involving the unlicensed spectrum. The uniform planar array (UPA) is adopted from far-field to near-field, where the communication distance is decreasing from 21 m to 6 m (Rayleigh distance is about 14.8 m). Compared to the far-field, the spatial non-stationary phenomenon of channel parameters can be more clearly observed along the array in the near-field region. Then, we propose the extension channel model based on the channel modeling of 3GPP TR 38.901. The array domain is introduced to characterize the spatial non-stationarity of channel parameters (e.g., power, delay, angle). Subsequently, the channel characteristic parameters along the array are analyzed in the near-field range, and the non-stationary model related to the antenna array is established, including power, path loss, delay spread, angular spread, and Ricean K-factor. Finally, the model validation and parametrization are presented in detail with the actual indoor near-field MIMO channel measurements in the 6 GHz band, such as power, angle, and so on. The design and scheme of antenna array spacing are given under the influence of spatial non-stationary characteristics. These work will be helpful for the development and operation of MIMO technology in unlicensed spectra for wireless communication systems.

Abstract The sixth-generation (6G) mobile network implements the social vision of digital twins and ubiquitous intelligence. Contrary to the fifth-generation (5G) mobile network that focuses only on communications, 6G mobile networks must natively support new capabilities such as sensing, computing, artificial intelligence (AI), big data, and security while facilitating Everything as a Service. Although 5G mobile network deployment has demonstrated that network automation and intelligence can simplify network operation and maintenance (O&M), the addition of external functionalities has resulted in low service efficiency and high operational costs. In this study, a technology framework for a 6G autonomous radio access network (RAN) is proposed to achieve a high-level network autonomy that embraces the design of native cloud, native AI, and network digital twin (NDT). First, a service-based architecture is proposed to re-architect the protocol stack of RAN, which flexibly orchestrates the services and functions on demand as well as customizes them into cloud-native services. Second, a native AI framework is structured to provide AI support for the diverse use cases of network O&M by orchestrating communications, AI models, data, and computing power demanded by AI use cases. Third, a digital twin network is developed as a virtual environment for the training, pre-validation, and tuning of AI algorithms and neural networks, avoiding possible unexpected losses of the network O&M caused by AI applications. The combination of native AI and NDT can facilitate network autonomy by building closed-loop management and optimization for RAN.

As the underlying foundation of a digital twin network (DTN), digital twin channel (DTC) can accurately depict the electromagnetic wave propagation in the air interface to support the DTN-based 6G wireless network. Since electromagnetic wave propagation is affected by the environment, constructing the relationship between the environment and radio wave propagation is the key to implementing DTC. In the existing methods, the environmental information inputted into the neural network has many dimensions, and the correlation between the environment and the channel is unclear, resulting in a highly complex relationship construction process. To solve this issue, we propose a unified construction method of radio environment knowledge (REK) inspired by the electromagnetic wave property to quantify the propagation contribution based on easily obtainable location information. An effective scatterer determination scheme based on random geometry is proposed which reduces redundancy by 90%, 87%, and 81% in scenarios with complete openness, impending blockage, and complete blockage, respectively. We also conduct a path loss prediction task based on a lightweight convolutional neural network (CNN) employing a simple two-layer convolutional structure to validate REK’s effectiveness. The results show that only 4 ms of testing time is needed with a prediction error of 0.3, effectively reducing the network complexity.

Advancements in emerging technologies, e.g., reconfigurable intelligent surfaces and holographic MIMO (HMIMO), facilitate unprecedented manipulation of electromagnetic (EM) waves, significantly enhancing the performance of wireless communication systems. To accurately characterize the achievable performance limits of these systems, it is crucial to develop a universal EM-compliant channel model. This paper addresses this necessity by proposing a comprehensive EM channel model tailored for realistic multi-path environments, accounting for the combined effects of antenna array configurations and propagation conditions in HMIMO communications. Both polarization phenomena and spatial correlation are incorporated into this probabilistic channel model. Additionally, physical constraints of antenna configurations, such as mutual coupling effects and energy consumption, are integrated into the channel modeling framework. Simulation results validate the effectiveness of the proposed probabilistic channel model, indicating that traditional Rician and Rayleigh fading models cannot accurately depict the channel characteristics and underestimate the channel capacity. More importantly, the proposed channel model outperforms free-space Green’s functions in accurately depicting both near-field gain and multi-path effects in radiative near-field regions. These gains are much more evident in tri-polarized systems, highlighting the necessity of polarization interference elimination techniques. Moreover, the theoretical analysis accurately verifies that capacity decreases with expanding communication regions of two-user communications.

With the development of sixth generation (6G) networks toward digitalization and intelligentization of communications, rapid and precise channel prediction is crucial for the network potential release. Interestingly, a dynamic ray tracing (DRT) approach for channel prediction has recently been proposed, which utilizes the results of traditional RT to extrapolate the multipath geometry evolution. However, both the priori environmental data and the regularity in multipath evolution can be further utilized. In this work, an enhanced-dynamic ray tracing (E-DRT) algorithm architecture based on multipath bidirectional extrapolation has been proposed. In terms of accuracy, all available environment information is utilized to predict the birth and death processes of multipath components (MPCs) through bidirectional geometry extrapolation. In terms of efficiency, bidirectional electric field extrapolation is employed based on the evolution regularity of the MPCs’ electric field. The results in a Vehicle-to-Vehicle (V2V) scenario show that E-DRT improves the accuracy of the channel prediction from 68.3% to 94.8% while reducing the runtime by 7.2% compared to DRT.

Due to the fact that the scale and construction of 6G communication systems are progressing toward unprecedentedly large and complex, accuracy and robustness of channel modeling method are vital to ensure that communication systems can work efficiently. Ray-Tracing (RT), as a precise deterministic modeling method, can meet most of these requirements. Shooting and Bouncing Rays (SBR) is a forward RT algorithm with high efficiency and wide usage. Traditional SBR utilizes environmental prior information, like distribution of scatters, only in path-finding. Channel parameters like quantity of rays are determined artificially, which affects SBR’s performance especially in 6G systems. In order to enable SBR to conduct 6G modeling stably and efficiently, we propose an advanced SBR algorithm that utilizes environmental prior information globally for both path-finding progress and parameter-determination. Space-division, face-classification are some environmental-information-assisted processes of the proposed algorithm, which help to calculate channel parameters quantitatively. Additionally, our new algorithm accelerates path-finding progress by decreasing unnecessary intersections through space-division. Simulation reveals our new algorithm reduces computational complexity by 34.53% in comparison of traditional SBR. Furthermore, with Image Method (IM) as a standard, new algorithm has smaller Normalized Mean Squared Error (NMSE) of 78.09% less in LOS-path ignoring power-receiving, compared to traditional SBR. Other metrics such as Power-Delay Profile (PDP) are also demonstrated in our work.

Smart factory scenarios in the Industrial Internet of Things (IIoT) introduce novel challenges to wireless communication, characterized by large-scale device interaction, strong reflection, and dense scattering, diverging from typical factories. To ascertain the channel disparities between smart factories and typical factories, we analyze channel characteristics across sub-6 GHz (e.g., 6 GHz) and mmWave frequencies (e.g., 28 and 39 GHz) in smart factory scenarios, establishing a multi-frequency path loss model. Furthermore, the comparison between the large-scale fading characteristics, including path loss and root mean square delay spread (RMS DS), observed in smart factory scenarios and those in the typical indoor factory (InF) scenarios defined by the 3rd-Generation Partnership Project (3GPP). Additionally, channel characteristics of the metal and non-metal scatterer areas in the smart factories are extracted to analyze the impact of metal scatterers on angle spread (AS) and RMS DS.

Traditional path loss methods typically employ statistical or empirical models, without fully considering the dynamic propagation environment. In this paper, we introduce a method called Visual Sensing-Based Path Loss Prediction (VSB-PLM), which predicts path loss using visual data obtained from multi-view sensing cameras. Specifically, we deploy multi-view cameras in real-world scenarios. Then, a Convolutional Neural Network (CNN) is designed to integrate environmental image features, the existence of the Line-Of-Sight (LOS) path, and the distance between the Transmitter (Tx) and Receiver (Rx) for path loss prediction. Finally, optimal path loss prediction results are obtained utilizing a multi-view selection algorithm. Simulation results demonstrate that the proposed algorithm has successfully improved path loss prediction accuracy by 9% compared to single-view sensing, achieving a Root Mean Squared Error (RMSE) of 3.66 dB.

Extremely large-scale massive multiple-input multiple-output (XL-MIMO) is a potential enabling technology for 6th-generation (6G) communication. The XL-MIMO channel research will be important for XL-MIMO system development. In this paper, the measurement of XL-MIMO channels from 32 to 512 transmitting antenna elements in the 6 GHz band is carried out in an indoor corridor scenario. The delay spread, angular spread, and channel capacity are investigated. The results are compared with the indoor channel model in the Third Generation Partnership Project (3GPP) TR 38.901. We find that the number of antenna elements has a small impact on the delay spread and angular spread. So the spatial non-stationary effect does not need to be considered specifically in the far-field range in this scenario. In addition, the special structure of the corridor leads to a difference in the comparison of the angular spread with the 3GPP model in each dimension. The closed environment of the corridor also results in a significant gap in channel capacity performance from the i.i.d. channel. This work can provide insights into XL-MIMO applications in the 6G era.

Extremely Large-Scale Multiple-Input-Multiple-Output (XL-MIMO) communication, is recognized as a potential enabling technology for sixth-generation (6G) communication. Due to the large antenna aperture of XL-MIMO, spatial non-stationary (SnS) phenomena may occur on the array domain during deployment. This paper, relying on Ray-tracing (RT) simulations, analyzes the SnS phenomena from various perspectives of channel characteristics. Meanwhile, to accurately model the SnS phenomenon, this paper proposes a method for stationary sub-interval partitioning based on channel characteristics. It is assumed that the channel is stationary within each sub-interval, while it is non-stationary across different intervals. The method comprehensively considers factors such as channel correlation, delay spread (DS), azimuth angle spread of departure (ASD), and multipath components (MPCs) birth-death for sub-interval partitioning. By analyzing the independence of sub-intervals, this paper demonstrates that the proposed method performs better in sub-interval partitioning compared to the traditional averaging approach.

Massive multiple-input multiple-output (MIMO) is envisioned as a promising technology in 5G-Advanced and sixth-generation (6G) communication. Channel characteristics are important for the development and performance assessment of massive MIMO systems. Besides, the 6 GHz band (5925- 7125 MHz) has rapidly become the focus band of industry and academia at present. Therefore, this paper conducts the massive MIMO channel measurements in 6 GHz band in typical urban scenarios, and focuses on the massive MIMO channel characteristics in 6 GHz band. The important channel characteristic parameters such as power delay profile (PDP), delay spread, angular spread, channel capacity and data transmission rate are obtained and investigated, which are statistically different from Third Generation Partnership Project (3GPP) TR 38.901 model parameters. Besides, the impact of bandwidth on the system is important, and the great bandwidth allocation has the ability to provide higher transmission rates for future communication systems. Based on deeply exploration of the channel characteristics in 6 GHz band, this work hopes to provide some reference for the design and optimization of 6G communication systems.

Significant challenges remain for realizing precise positioning and velocity estimation in practical perceptive vehicular networks (PVN) that rely on the emerging integrated sensing and communication (ISAC) technology. Firstly, complicated wireless propagation environment generates undesired clutter, which degrades the vehicular sensing performance and increases the computational complexity. Secondly, in practical PVN, multiple types of parameters individually estimated are not well associated with specific vehicles, which may cause error propagation in multiple-vehicle positioning. Thirdly, radio transceivers in a PVN are naturally asynchronous, which causes strong range and velocity ambiguity in vehicular sensing. To overcome these challenges, in this paper 1) we introduce a moving target indication (MTI) based joint clutter suppression and sensing algorithm, and analyze its clutter-suppression performance and the Cramér-Rao lower bound (CRLB) of the paired range-velocity estimation upon using the proposed clutter suppression algorithm; 2) we design an algorithm (and its low-complexity versions) for associating individual direction-of-arrival (DOA) estimates with the paired range-velocity estimates based on “domain transformation”; 3) we propose the first viable carrier frequency offset (CFO) and time offset (TO) estimation algorithm that supports passive vehicular sensing in non-line-of-sight (NLOS) environments. This algorithm treats the delay-Doppler spectrum of the signals reflected by static objects as an environment-specific “fingerprint spectrum”, which is shown to exhibit a circular shift property upon changing the CFO and/or TO. Then, the CFO and TO are efficiently estimated by acquiring the number of circular shifts, and we also analyse the mean squared error (MSE) performance of the proposed time-frequency synchronization algorithm. Finally, simulation results demonstrate the performance advantages of our algorithms under diverse configurations, while corroborating the theoretical analysis.

Extremely-large-scale multiple-input multiple-output (XL-MIMO) technology, offering vast spatial degrees of freedom by deploying a huge number of antennas, is a promising enabling technology to empower sixth-generation mobile networks (6G). The XL-MIMO channel model is a prerequisite of XL-MIMO technology optimization, system design, and performance evaluation. In this paper, we provide an overview of challenges and ongoing research in XL-MIMO channel measurement, characterization, and modeling. In particular, characterizing and modeling near-field effects and spatial non-stationarity (SnS) are discussed. Also, the channel modeling methods that can describe these new channel characteristics are surveyed. Furthermore, open issues in XL-MIMO channel measurement, characterization, and modeling are presented to give insights into future XL-MIMO channel research.

Abstract Near-field technology is increasingly recognized due to its transformative potential in communication systems, establishing it as a critical enabler for sixth-generation (6G) telecommunication development. This paper presents a comprehensive survey of recent advancements in near-field technology research. First, we explore the near-field propagation fundamentals by detailing definitions, transmission characteristics, and performance analysis. Next, we investigate various near-field channel models—deterministic, stochastic, and electromagnetic information theory based models, and review the latest progress in near-field channel testing, highlighting practical performance and limitations. With evolving channel models, traditional mechanisms such as channel estimation, beamtraining, and codebook design require redesign and optimization to align with near-field propagation characteristics. We then introduce innovative beam designs enabled by near-field technologies, focusing on non-diffractive beams (such as Bessel and Airy) and orbital angular momentum (OAM) beams, addressing both hardware architectures and signal processing frameworks, showcasing their revolutionary potential in near-field communication systems. Additionally, we highlight progress in both engineering and standardization, covering the primary 6G spectrum allocation, enabling technologies for near-field propagation, and network deployment strategies. Finally, we conclude by identifying promising future research directions for near-field technology development that could significantly impact system design. This comprehensive review provides a detailed understanding of the current state and potential of near-field technologies.

传统无线通信系统广泛利用了远场空间资源。随着6G网络的出现, 近场资源的探索和利用势在必行。这些资源为无线通信系统引入了新的物理空间维度。通过利用更高频段并结合智能超表面(RIS)、超大规模多入多出(XL-MIMO)和无蜂窝网络等技术, 近场通信将成为6G网络的关键推动因素。这种范式转变挑战了传统的远场平面波假设, 需要重新评估空间资源管理策略。 尽管传统系统已有效利用远场空间资源, 但在6G网络中采用近场空间资源为重新定义无线通信系统提供了机会。这种向近场通信的转变促进了对创新技术范式的研究。近场通信有可能显著提高频谱效率、数据传输速率和空间分辨率, 从而在增强现实、高精度定位、通感一体化以及安全无线能量传输等领域实现先进应用。影响近场通信开发和应用的关键因素包括近场传播和信道建模、提高空间资源利用率、硬件挑战以及工程实践与标准化。这些领域强调了近场通信的多面性, 反映了在标准化工作的同时, 对建模、技术、硬件开发和工程实践进步的需求。近场通信具有推动无线技术发展的变革潜力, 为消费者、工业和安全等领域的应用提供了新的可能。 在此背景下, 中国工程院院刊《信息与电子工程前沿(英文)》邀请张平院士担任主编, 赵亚军总工、戴凌龙教授、Marco di Renzo教授担任执行主编, 组织出版了“近场通信理论与应用”专刊。专刊收录12篇文章, 包括2篇综述、5篇研究、5篇通讯, 内容涵盖近场传播基本原理、信道模型的发展、传统机制在近场环境中面临的限制等, 此外包含XL-MIMO信道研究、同时无线信息和能量传输(SWIPT)系统以及RIS的最新研究进展, 以及它们在增强通信系统中的应用。

The upcoming sixth generation (6G) driven Internetof- Things (IoT) will face the great challenges of extremely low power demand, high transmission reliability and massive connectivities. To meet these requirements, we propose a novel hybrid ambient backscatter communication (ABCom) or relay communication (RCom) assisted non-orthogonal multiple access (NOMA) network, which simultaneously enables traditional relay networks and ABCom-assisted IoT networks. Specifically, we investigate the reliability and the finite signal-to-noise ratio (SNR) diversity-multiplexing trade-off (f-DMT) of the proposed system to characterize the outage performance of the proposed system in the non-asymptotic SNR region. We derive the outage probability (OP) and the finite SNR diversity gain when two sources aim to communicate through either ABCom or RCom. On the basis that the results of Monte Carlo simulation and analysis are in perfect agreement, we discover that in the high SNR regime, the OP for ABCom tends to be a constant, leading to a zero diversity gain and an error floor, while the OP for RCom is monotone decreasing with respect to the SNR. Also, compared with the imperfect successive interference cancellation (ipSIC) mode, the reliability of the system under the ideal condition is significantly improved; Moreover, in the lower multiplexing gain regime, for both ABCom and RCom, the higher finite SNR diversity gain results in better system reliability, which provides good opportunities for ABCom to adapt f-DMT and improve relevant performance metrics by adapting the reflection parameter.

Massive Multiple Input Multiple Output (MIMO) at millimeter-Wave (mmWave) frequencies is envisioned as a key technology for beyond 5G communication. Accurate channel modeling is crucial for the design and evaluation of such systems. Ray-Tracing (RT) can be used to accurately simulate the propagation channel. However, state-of-the-art RT for multi-antenna systems typically employs plane-wave extension under far-field conditions, failing to capture Near-Field (NF) and Spatial non-Stationary (SnS) properties that are observed in real-world mmWave massive MIMO channel measurements. This work aims at accurate and efficient RT simulations for massive MIMO systems, with a proposed coarse-refinement strategy capable of capturing NF and SnS. The channel is simulated using RT on a few sparsely located array elements and then interpolated onto other elements using spherical/astigmatic-wave approximation and the Uniform Theory of Diffraction, thus significantly reducing simulation complexity while maintaining accuracy. The proposed strategy is demonstrated to offer almost the same simulation accuracy as the brute-force method, with a dramatic reduction in complexity through experimental validation. The significance, novelty, effectiveness, and simplicity of the proposed framework make it highly valuable for massive MIMO channel research.

To reduce the downlink beam sweep overhead of mmWave systems, we propose a deep reinforcement learning based dynamic beam selection (DRL-DBS) method. A new learning motivation is presented by analyzing the dynamic change laws of high- and low-frequency channels in the spatial domain: to learn the index offset between the optimal beam of mmWave and sub-6 GHz spatial spectrum. In the DRL-DBS method, we propose a novel action space where actions can dynamically adjust the size of the beam sweep subset according to the high-and low-frequency channel propagation laws. Hence, the DRL-DBS method can predict a mmWave downlink beam sweep subset with dynamic size, and the optimal beamforming index is from beam sweep results on the subset. A dual-input dueling Q-network with noisy networks and prioritized experience replay is designed to select the optimal action. The DRL-DBS method can achieve a dynamic trade-off between mmWave beam selection quality and beam sweep overhead based on the reward function. Simulation results demonstrate the superior performance of the DRL-DBS method compared with the existing strategies. Especially, the DRL-DBS method outperforms the exhaustive search algorithm in achievable rate because the overhead of mmWave beam sweep is considered.

Terahertz (THz) communication technology is considered as one of the key technologies of sixth-generation (6G) communications. The study of THz channel characteristics is the basis for evaluating, deploying, and optimizing THz communication systems. In this paper, we conduct measurements in line-of-sight (LOS) and non-LOS (NLOS) scenarios in an indoor-office scenario at 100 GHz. Based on measured data, path loss (PL) and root-mean-squared (RMS) delay spread (DS) are analyzed and modeled. First, the differences between omnidirectional and directional PL are analyzed. And by analyzing the distance dependence of RMS DS, it is found that RMS DS in the NLOS scenario is negatively proportional to propagation distance while the trend is opposite in the LOS scenario. Second, based on the statistical modeling method, the model parameters of PL and RMS DS are obtained. We compare the obtained model to the 3GPP channel model and analyze their differences. Third, the bit-error-rate (BER) performance of OFDM systems at 100 GHz is simulated based on measured power delay profiles (PDPs). We find that the scenario and propagation distance have an apparent influence on the BER performance. These results can give insight into the design of THz communication systems in the indoor-office scenario.

Visible light positioning (VLP) systems provide higher accuracy and lower cost, more suitable for indoor positioning compared to their radio-frequency (RF) counterparts. And the VLP system based on received signal strength (RSS) is widely used due to the lowest hardware cost. However, existing RSS-VLP systems are usually sensitive to reflections and shadows. In this paper, we propose a VLP framework with an adaptive algorithm module, a diversity module, and a combination module. Simulations show that in the presence of reflections and shadows, this positioning framework can achieve an average error below 15 cm in a 5 m × 5 m area. Moreover, in case of severe blocking, its positioning error is even an order of magnitude lower than that of the typical RSS-VLP method.

In the 5G-Advanced and beyond systems, multi-frequency cooperative networking will become an inevitable development trend. However, the channels have not been fully investigated at multi-frequency bands and in multi-scenarios by using the same channel sounder, especially for the sub-6 GHz to millimeter-wave (mmWave) bands. In this paper, we carry out channel measurements at four frequency bands (3.3, 6.5, 15, and 28 GHz) in two scenarios including Urban Micro (UMi) and Outdoor-to-Indoor (O2I) with the same channel sounder. The channel characteristics are extracted and modeled, including path loss (PL), shadow fading, frequency dependence of cluster features, root mean square (RMS) delay spread (DS), Ricean K-factor, and the correlation properties. We mainly focus on the analysis of large-scale parameters and more consideration of the link budget coverage problem. We present the frequency dependence model of the channel characteristics. Among them, in the non-line-of-sight (NLoS) condition, it is found that except for theoretical value brought by higher frequency, additional path loss increment will be generated. Based on these channel characteristics, the impact on performance of the wireless system is analyzed including cell coverage radius, data rate and bit error rate (BER). The results can give an insight into the spectrum selection and optimization in 5G-Advanced and beyond multi-frequency communication systems.

太赫兹信道传播特性对太赫兹通信系统的设计、评估和优化至关重要。此外, 反射在信道传播中起着重要作用。本文基于大量的信道测量工作, 对太赫兹通道的反射系数进行研究。首先, 建立从220 GHz到320 GHz的太赫兹信道测深平台, 入射角范围从10°到80°。根据实测的传播损耗, 分别计算玻璃、瓷砖、木板、石膏板和铝合金五种建筑材料的频率和入射角的反射系数。研究发现, 由于缺乏与太赫兹相关的参数, 导致非金属材料的菲涅耳模型无法成功地拟合实测数据。因此, 通过改进菲涅耳模型与洛伦兹和德鲁德模型, 提出一个频角二维反射系数模型。该模型表征了反射系数的频率和入射角, 与实测数据的均方根误差较小。总的来说, 这些结果对于太赫兹通道的建模做出贡献。

Abstract Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have been attracting significant attention in both academia and industry for their advantages of achieving 360° coverage and enhanced degrees-of-freedom. This article first identifies the fundamentals of STAR-RIS, by discussing the hardware models, channel models, and signal models. Then, three representative categorizing approaches for STAR-RISs are introduced from the phase-shift, directional, and energy consumption perspectives. Furthermore, the beamforming design of STAR-RISs is investigated for both independent and coupled phase-shift cases. As a recent advance, a general optimization framework, which has high compatibility and provable optimality regardless of the application scenarios, is proposed. As a further advance, several promising applications are discussed to demonstrate the potential benefits of applying STAR-RISs in sixth-generation wireless communication. Lastly, a few future directions and research opportunities are highlighted.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) is a promising passive device that contributes to full-space coverage via transmitting and reflecting the incident signal simultaneously. As a new paradigm in wireless communications, how to analyze the coverage and capacity performance of STAR-RISs becomes essential but challenging. To solve the coverage and capacity optimization (CCO) problem in STAR-RIS-assisted networks, a multi-objective proximal policy optimization (MO-PPO) algorithm is proposed to handle long-term effects. To strike a balance between each objective, the MO-PPO algorithm provides a set of optimal solutions to approach a Pareto front (PF), where the solution on the approximate PF is regarded as an optimal result. Moreover, in order to improve the performance of the MO-PPO algorithm, two update strategies, i.e., action-value-based update strategy (AVUS) and loss function-based update strategy (LFUS), are investigated. For the AVUS, the improved point is to integrate the action values of both coverage and capacity and then update the loss function. For the LFUS, the improved point is only to assign dynamic weights for both loss functions of coverage and capacity, while the weights are calculated by a min-norm solver at every update. The numerical results demonstrated that the investigated update strategies outperform the fixed weights MO optimization algorithms in different cases, which include a different number of sample grids, the number of STAR-RISs, the number of elements in the STAR-RISs, and the size of STAR-RISs. Additionally, the STAR-RIS-assisted networks achieve better performance than conventional wireless networks without STAR-RISs. Moreover, with the same bandwidth, a millimetre wave is able to provide higher capacity than sub-6 GHz, but at a cost of smaller coverage.

Visual information, captured for example by cameras, can effectively reflect the sizes and locations of the environmental scattering objects, and thereby can be used to infer communications parameters like propagation directions, receiver powers, as well as the blockage status. In this paper, we propose a novel beam alignment framework that leverages images taken by cameras installed at the mobile user. Specifically, we utilize 3D object detection techniques to extract the size and location information of the dynamic vehicles around the mobile user, and design a deep neural network (DNN) to infer the optimal beam pair for transceivers without any pilot signal overhead. Moreover, to avoid performing beam alignment too frequently or too slowly, a beam coherence time (BCT) prediction method is developed based on the vision information. This can effectively improve the transmission rate compared with the beam alignment approach with the fixed BCT. Simulation results show that the proposed vision based beam alignment methods outperform the existing LIDAR and vision based solutions, and demand for much lower hardware cost and communication overhead.

In visible light communications, adding direct current (DC) bias is the most common solution to the bipolar problem of signal, so that the precoding and optical orthogonal frequency division multiplexing (O-OFDM) can be performed. Moreover, it will be easier to achieve dimming control. In this paper, we analyze the impact of DC bias on optical power signal in the view of distribution, capacity and nonlinear distortion. Firstly, we classify the impacts of DC bias into two types depends on the total power, and derive their maximum entropy distributions. Then we investigate the capacity of two different channels with DC bias. Finally, we study the nonlinear distortion of power signal. Derivation and simulation results show that the capacity would decrease as the DC bias increases in most cases. The conclusions will help the consideration of DC bias in future system design.

The over-the-air (OTA) testing based on multi-probe anechoic chamber (MPAC) is an efficient solution to evaluate the performance of 5G multiple-input multiple-output (MIMO) capable devices, which can reconstruct the wireless channel within the lab in a controlled manner. For different test requirements, the probe layouts of the MPAC may be varied, bringing a lot of additional hardware overhead. In this paper, a novel design of the MPAC test system is proposed and constructed to meet various mainstream 5G OTA test solutions. By adopting switch matrixes and the three-dimensional (3D) quick probe selection algorithm, a 3D MPAC can be easily adjusted to different probe layouts. The solution can save at most 3/4 of the hardware port resources while ensuring the channel emulation accuracy. Channel validation and 5G terminals performance testing are carried out in the new system. The result comparison demonstrates the effectiveness of the probe simplification scheme and the system design.

The sensitivity of millimeter-wave (mmWave) to blockage and the requirement for the communication system to support mobility scenarios makes mmWave blockage and beam prediction necessary. In this paper, the effect of diffraction characteristics on improving blockage and beam prediction is investigated. A dataset with diffraction is created, and a recurrent neural network (RNN) is designed to capture the diffraction characteristics in the dataset. Further, the generalization ability of the RNN for different scenarios is researched. The results show that the accuracy of both blockage and beam prediction is further improved by utilizing diffraction characteristics. In addition, the blockage and beam prediction accuracy of the designed RNN is increased by 1.5% and 9.7% compared with a reference deep neural networks (DNN). Finally, applying the RNN model trained in the outdoor scenario to the prediction in the indoor scenario, the indoor blockage and beam prediction accuracy respectively reach up to 99.8% and 98.3% of the outdoor’s.

The loss characteristics caused by human bodies blockage have a significant impact on millimeter-wave (mmWave) band. In this paper, the influence of the number of human bodies on blockage loss is firstly analyzed in both corridor and classical indoor scenarios at the mmWave band. Then, in order to represent the blockage effect caused by different numbers of human bodies, the line-of-sight (LOS) outage probability is introduced. Finally, a multi-person blockage loss model is proposed based on the LOS outage probability and human model. The model demonstrates that the multi-person blockage loss can be modeled as Gaussian by the LOS outage probability. These results try to give some insights into the modeling of human blockage, especially in multi-person cases.

Beamforming is an essential part of the fifth-generation (5G) mobile communication technology. And it will continue to play an indispensable role in the sixth-generation (6G) system. Due to unequal numbers of radio frequency (RF) chains between the base station (BS) and the user equipment (UE), asymmetric beam patterns exist between BS and UE. In this case, characteristics of downlink (DL) channel and uplink (UL) channel need to be explored. In this paper, channel measurements have been performed in an indoor office at 39 GHz. In the measurement campaign, a directional antenna and an omnidirectional antenna are used to simulate BS and UE respectively. In the line-of-sight (LOS) case, we find that path loss (PL) exponent of UL is 0.3 smaller than DL and intercept of UL is 5 dB larger than DL. The measured PL of UL is higher than DL in most cases. And the difference in measured PL between DL and UL decreases with increasing distance. Meanwhile, the root-mean-square (RMS) delay spread (DS) value of UL is larger than DL under most circumstances and the mean value of the difference is 0.9 ns. In the non-line-of-sight (NLOS) case, PL exponent of UL is 0.2 smaller than DL, and the intercept of UL is 2 dB larger than DL. The difference in measured PL and RMS DS between DL and UL becomes narrowed due to blockage effect. The results of the paper are conductive to get a further understanding of asymmetric channel in millimeter-wave.

Acquisition and feedback of accurate downlink (DL) channel state information (CSI) bring up high overhead. So a convolutional neural network (CNN) based multi-input-multi-output (MIMO) channel prediction method is proposed in this paper. Based on the space correlation, we design the proposed prediction method that predicts the MIMO channel concurrently. The proposed CNN method extracts the feature of UL-CSI through convolutional layers and predicts DL-CSI by the feature through deconvolutional layers. Simulation results demonstrate that the proposed channel prediction method leads to higher accuracy than existing methods. The bit error rate (BER) in orthogonal frequency division multiplexing (OFDM) system of proposed method is 82.4% and 26.2% lower than linear minimum mean squared error (LMMSE) method in two different simulated datasets. And the proposed method is tested under different user moving speeds and different antenna scale.

Antenna selection has attracted more and more attention in massive multiple input multiple output (MIMO) systems to balance the performance and computational complexity. In this paper, we design a top-N transmit antenna selection algorithm for massive MIMO systems. This algorithm can reach the optimization rather the sub-optimization which traditional algorithms usually achieve. Specifically, we build a top $-N -$ output ResNet which selects the top-N optimal schemes to improve the accuracy compared to the single output network. And then we design a N-to-one selection algorithm that acquires the optimal scheme from the top-N optimization. With the help of the top-N antenna selection algorithm, our proposed algorithm reaches 95% accuracy with only 2.23% complexity compared to exhaustive-search-based antenna selection.

Millimeter-wave channel measurements for a meeting room, lecture room and open plan office floor were used to analyse and model the spatial consistency of channel clusters. Particularly, how the angles of arrival and delays of extracted multi-path components in each cluster varied with small changes in receiver location. We extended the KPowerMeans algorithm, for classifying captured multi-path components into clusters from the delay/angular domains, to include the location domain to allow the spatial consistency of the clusters between locations to be analysed. The observed spatial consistency in measurements was then used to validate whether the 3GPP spatial consistency procedure (3GPP SC-I) reflects real world spatial consistency. The 3GPP spatial consistency procedure for ensuring spatial consistency of cluster parameters during mobile user simulations was then applied for each environment, allowing for comparison between the ‘predicted’ cluster parameters and the measured cluster parameters. The 3GPP model/procedure showed a good fit for all environments but highlighted the impact of the environment on the amount of accuracy of the procedure.

Millimeter-wave technology is the key technology to support the connection and throughput of the Industrial Internet of Things (IIoT). And the channel model is the basis for the evaluation, optimization, and deployment of the wireless communication system. To model the millimeter-wave channel characteristics in the IIoT scenario, we conduct measurements in a factory-like scenario at 28 GHz. In measurements, antenna heights are set to be higher or lower than most of the metal machines, separately. Based on the collected data, we analyze the large-scale fading and small-scale fading characteristics, including path loss, delay spread (DS), and angular spread (AS). These channel characteristics are statistically modeled. The effects of antenna heights on these channel characteristics are also investigated. Furthermore, we study the distance dependence of the DS and AS.

Environmental modeling errors have significant influence on the accuracy of ray tracing (RT) based channel modeling method. The aim of this paper is to provide reference for determining the appropriate accuracy of indoor databases to achieve prediction results within tolerance. Therefore in this paper, we discuss the influence of modeling errors on the RT-based channel modeling accuracy for millimeter-wave frequency bands. Different from the previous work, our discussion mainly foucus on the indoor environments and try to analyze the relationship between the channel parameters including received power, delay spread (DS), angle spreads (AS) and the types of scatterers. Simulation results show the RT results are more sensitive to the geometric errors caused by the room boundary in the line-of-sight (LOS) case while indoor scatterers have a greater impact in the non-LOS (NLOS) case. Additionally, AS is more sensitive to geometric errors of environment than DS. These results have guiding significance for channel modeling and simulation work using RT.

Industrial Internet of Things (IIoT) is an emerging area that fifth-generation (5G) mobile communication system penetrates industrial manufacturing applications. The indoor factory has larger space and there are a lot of metal machine tools distributed in it, which makes its radio propagation characteristics and corresponding channel models significantly different from those of the indoor office and indoor hotspot. To support the design and the evaluation of the IIoT techniques, the 3rd Generation Partnership Project (3GPP) released the first 5G IIoT standard model in October 2019. In this article, we give a detailed explanation of this IIoT model and compare it with other indoor models. First, we introduce the standardization of 3GPP IIoT channel model and its motivation. Second, four IIoT subscenarios, which are classified according to the clutter density and antenna height, are described. Third, the potential frequency bands of 5G IIoT are summarized. Fourth, the models of channel parameters, including the path loss and the line-of-sight (LOS) probability, the root mean-square (RMS) delay spread, and the angular spread, are given. Among them, the models of path loss and LOS probability take the antenna height and clutter density into consideration. The model of RMS delay spread changes from frequency-dependent to volume-dependent in order to catch the size variation of factories. Finally, two newly added key channel characteristics, dual mobility and absolute time of arrival, which help to describe the robot movement and positioning, are also presented.

This paper presents an empirical study of the uplink and downlink azimuth angle of arrival (AoA) in an urban micro (UMi) scenario at 28 GHz. At present, most UMi measurements are conducted in the downlink and then the uplink situation is inferred assuming channel reciprocity. Although the channel correlation coefficient of the uplink and downlink can be as high as 0.8, this does not mean that they are the same. Only a real uplink measurement can accurately describe its channel conditions, and this is what this study does. A receiver equipped with a rotatable horn antenna is mounted at the base station and the user terminal, respectively, in simulating the uplink and downlink. To improve the angular resolution, we extract the multipath components (MPCs) using the space-alternating generalized expectation-maximization algorithm. Also, a spatial lobe approach is used to cluster the MPCs in the power angular spectrum. By matching MPCs with objects in the environment, we find that direct propagation and first-order reflections are dominant in line-of-sight and non-line-of-sight cases. By comparing our measurement with those in standard channel models, we verify that the AoA of clusters follows a Gaussian distribution in the uplink and downlink. In addition, a two-dimensional Gaussian distribution for ray AoA and power is established to reflect their correlation.

Channel covariance matrix (CCM) is one critical parameter for designing the communications systems. In this paper, a novel framework of the deep learning (DL) based CCM estimation is proposed that exploits the perception of the transmission environment without any channel sample or the pilot signals. Specifically, as CCM is affected by the user’s movement, we design a deep neural network (DNN) to predict CCM from user location and user speed, and the corresponding estimation method is named as ULCCME. A location denoising method is further developed to reduce the positioning error and improve the robustness of ULCCME. For cases when user location information is not available, we propose an interesting way that uses the environmental 3D images to predict the CCM, and the corresponding estimation method is named as SICCME. Simulation results show that both the proposed methods are effective and will benefit the subsequent channel estimation.

Previous studies have shown that introducing real principal component analysis (PCA) into wireless MIMO channel modeling can effectively improve its accuracy. However, wireless channel data is mostly in the form of complex-valued channel impulse response (CIR) and thus real PCA is very difficult to recover the phase of channel. Under such a motivation, this paper proposes a novel complex PCA-based MIMO channel modeling methodology. First, we prove that PCA can be used to analyze the measured complex-valued CIR data with the principle of maximizing the converted channel power. Then the representative channel features are extracted, which are utilized to reconstruct the CIR finally. An indoor measurement with 32 × 56 antennas is performed for validation. The results clearly show that the proposed channel model is more accurate compared to the real PCA-based model, and is tightly close to the measured channel. Moreover, the proposed methodology is robust with the increase of antennas, which provides insights into massive MIMO channel modeling in the future.

With the commercialization of fifth generation networks worldwide, research into sixth generation (6G) networks has been launched to meet the demands for high data rates and low latency for future services. A wireless propagation channel is the transmission medium to transfer information between the transmitter and the receiver. Moreover, channel properties determine the ultimate performance limit of wireless communication systems. Thus, conducting channel research is a prerequisite to designing 6G wireless communication systems. In this paper, we first introduce several emerging technologies and applications for 6G, such as terahertz communication, industrial Internet of Things, space-air-ground integrated network, and machine learning, and point out the developing trends of 6G channel models. Then, we give a review of channel measurements and models for the technologies and applications. Finally, the outlook for 6G channel measurements and models is discussed.

In this paper, the Gaussian mixture model (GMM) is introduced to implement channel multipath clustering. The GMM incorporates the covariance structure and the mean information of the channel multipaths, thus it can effectively reveal the similarity of the channel multipaths. First, the expectation-maximization (EM) algorithm is utilized to search for the posterior estimation of the GMM parameters. Then, the variational Bayesian (VB) algorithm is employed to optimize the GMM parameters to enhance the searching ability of EM and further to determine the optimal number of Gaussian distributions without resorting to cross-validation. Finally, a compact index (CI) is proposed to validate the clustering results reasonably. Thanks to the proposed CI, it is possible to find a close relationship among the GMM clustering mechanism, the multipath propagation characteristics and the CI evaluation index. Experiments with synthetic data and outdoor-to-indoor (O2I) channel data are presented to demonstrate the effectiveness of the proposed method.

Identifying Line-of-Sight (LOS) and Non-LOS (NLOS) channel conditions is a big concern in many works. In this paper, we aim to achieve channel classification for millimeter wave (mmWave) systems. The results of mmWave measurement conducted at 28 GHz in typical shopping mall scenario are presented. Specifically, we analyzed the identification performance of single- valued feature extracted from channel impulse response (CIR), and confirmed that rise-time is an attractive features for identification, particularly precise for wideband mmWave signal. Furthermore, a classification algorithm Gradient Boosting Decision Tree (GBDT) is used for LOS/NLOS identification. Experimental results demonstrate an overall detection accuracy of 97.9\%, which shows that our method has a better classification performance than the single feature classifier. Moreover, extensive experimental evaluation considering various propagation distances and obstruction diversity have validated the feasibility of this method.

The Industrial Internet of Things (IIoT), as the typical application of the fifth generation mobile communication (5G) has its complex and special electromagnetic propagation characteristics in harsh industrial environments. The metal obstacles such as machines and mechanical arms in industrial scenarios will have an impact on the path loss of radio waves. The large metal equipment can generate strong mirror reflection and scattering waves which produce additional strong multipath components. Furthermore, the propagation channel will become change with the time due to the mobility of the automated guided vehicle, working people, etc. in industrial space. In this paper, based on the measurement data obtained in a typical industrial factory, the propagation channel is characterized. The path loss exponent and channel envelope in line of sight (LoS) scenarios and a special case that the sensor is in metallic box are respectively investigated. Additionally, the level passing rate (LCR) and the spatial correlation characteristics of time varying channels caused by the moving transport vehicle are investigated at different frequency bands. These results provide the basis for the practical deployment of wireless networks for the IIoT systems.

The Industrial Internet of Things (IIOT) can help the manufacturing enterprises improve the production efficiency, reduce costs and achieve intelligent factory production. In this harsh scenario, however, the electromagnetic noise performs a severe impact on IIOT which uses low-power wireless sensor devices. In this paper, we aim to characterize electromagnetic noise for IIoT systems based on the realistic measurement data collected in an automobile factory. The measurements were conducted in time and frequency domain, respectively. By using the frequency-domain measurement data, we can extract the frequency occupation and power amplitude information of electromagnetic noise between 300 MHz to 3 GHz band. By using the time-domain measurement data, the noise amplitude distribution (NAD) of electromagnetic noise at 315MHz, 779MHz and 916MHz bands are extracted. Finally, a continuous hidden Markov model (CHMM) is used to characterize the time-varying property of electromagnetic noise. These results are beneficial and informative when designing wireless networks for the IIoT.

Based on outdoor microcellular measurements at 26 and 32 GHz, the path loss models are modified by a proposed dynamic rain model to see the difference of the path loss models between clear and rainy air with respect to rainfall intensities and transceiver distances. Moreover, a dynamic rain cell model for a multi‐user system is developed to investigate the total rain attenuation. The results show that the parameters for floating‐intercept (FI) model have a bigger change with different rainfall intensities than close‐in (CI) model. When considering the dynamic rain model with a different radius and moving speeds, the larger rain cell radius, the larger path loss exponents in CI and FI models, the smaller path loss intercept in FI model and system capacity will be achieved. For a fixed rain cell radius, the larger moving speed of the rain cell, the shorter effective time on the path loss and system capacity will be achieved. In addition, the rain cell has a bigger effect on the path loss exponent, intercept and system capacity at higher frequency band with respect to its size, moving speed, and rainfall intensity.

Millimeter-wave channel models are fundamental for evaluating the performance of technologies for 5G. In this paper, we present two kinds of millimeter-wave channel measurements, fixed point measurements and virtual measurements, in an indoor open office. A correlation sounder with a bandwidth of 600 MHz is used to carry out these measurements. Then, channel parameters, e.g., the path loss, shadow fading, root mean square (RMS) delay spread (DS), K-factor, delay-cluster number and angular spread, are extracted and modelled. These extracted models will enable the generation of an impulse response for the indoor hotspot environment. Differences between our measurement-based channel model parameters and the International Telecommunication Union (ITU) channel model are discussed. The impact of differences on capacity are investigated by simulations. We find that the ITU channel model is optimistic about capacity. These results can help researchers to use millimeter-wave channel models for indoor office scenarios when evaluating 5G performance.

In this article, we introduce a statistics-based clustering framework that is able to model the clustering problem corresponding to the channel propagation characteristics and evaluate the clustering results effectively. In the framework a Gaussian mixture model (GMM) is employed to model the channel multipaths. Then, we optimize the GMM parameters with the expectation-maximization (EM) algorithm. To evaluate the clustering results effectively, a compact index (CI) is devised, in which both the mean and variance of the clusters are considered. In the simulation, outdoor-to-indoor (O2I) channel measurement data is presented to demonstrate the effectiveness of the proposed framework.

Multiuser multiple input multiple output (MU‐MIMO) wireless communication system provides substantial downlink throughput in millimeter wave (mmWave) communication by allowing multiple users to communicate at the same frequency and time slots. However, the design of the optimum beam‐vector for each user to minimise interference from other users is challenging. In this paper, based on the concept of signal‐to‐leakage plus noise ratio (SLNR), we analyze the ergodic sum‐rate capacity using statistical Eigen‐mode (SE) and zero‐forcing (ZF) models with Ricean fading channel. In the analysis, the orthogonality of channel vectors between users is assumed to guarantee interference cancelation from other cochannel users. The impact of the number of antenna elements on the achievable sum‐rate capacity obtained by dirty paper coding (DPC) method considered as a nonlinear scheme for approximating average system capacity is studied. A power iterative precoding scheme that iteratively finds the most dominant eigenvector (optimum weight vector) for minimising cochannel interference (CCI), that is, maximising the SLNR for all users simultaneously, is designed resulting in enhancement of average system capacity. The average system capacities achieved by the proposed power iterative technique in this study compared with the singular value decomposition (SVD) method are in the ranges of 5–11 bps/Hz and 1–6 bps/Hz, respectively. Therefore, the proposed power iterative method achieves higher performance than the SVD regarding achievable sum‐rate capacity.

This paper attempts to identify the requirement and the development of machine learning‐based mobile big data (MBD) analysis through discussing the insights of challenges in the mobile big data. Furthermore, it reviews the state‐of‐the‐art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently applied data analysis methods are reviewed. Three typical applications of MBD analysis, namely, wireless channel modeling, human online and offline behavior analysis, and speech recognition in the Internet of Vehicles, are introduced, respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis.

Results of recent measurement campaigns demonstrate a significant increase in the degrees of freedom introduced by the elevation domain, and consequently, a large capacity gap as predicted by 3D and 2D MIMO channel models. To gain further insight into the effects of the elevation domain on MIMO system performance, we derive new spatial correlation (SC) expressions for 3D and 2D channel models, given rectangular arrays of cross-polarized antennas. The main result relates 3D SC to the 2D counterpart, allowing us to quantify the effects of the elevation domain on SC. Furthermore, our result allows us to derive the following three simple relationships between 2D and 3D correlations. We show that for a small elevation angle spread, the difference between 2D and 3D SCs grows quadratically in both vertical separation and angle spread. For the case of small vertical spacings, we derive a bound on the above correlation gap. Furthermore, we show that the elevation effects on correlation can be accurately decoupled, allowing us to express the 3D SC matrix as a Hadamard product of a 2D SC matrix and an elevation component. Collectively, our results demonstrate the need for accurate channel modeling when evaluating the performance of 3D MIMO systems.

Massive multi-input and multi-output (MIMO), which uses large-scale antenna arrays to improve spatial efficiency, strengthen performance robustness, etc., is one of primary technologies of 5G wireless communication. To gain further insights into the massive MIMO channel, measurements with the virtual antenna array of the same structure (256 antennas at transmitter and 16 antennas at receiver) are performed in an urban macrocell scenario (UMa) at different frequencies (3.5 & 6 GHz) with different bandwidths (100 & 200 MHz). The enormous collected channel impulse responses enable us to present comparative results including the power delay profile, delay spread and extract angular parameters via SAGE algorithm. Furthermore, angle spread values are illustrated as a direct reflection of massive MIMO channel propagation, then we present comparative channel capacity results. Collectively, these results will shed light on the design of massive MIMO system in UMa environments.

This paper presents the wideband channel characteristics of 28 GHz in urban microcell (UMi) scenario based the measurement in Beijing. Both the omnidirectional-antenna-based measurement and the horn-antenna-based measurement are performed with bandwidth up to 800 MHz. The standard deviation of shadow fading (σS ) is studied and is compared with that value in standard protocol. Unlike traditional method, the σS is calculated with data captured in each single route in this paper. It is found that in a specific environment, the standard model obtained by traditional method overestimate the σS of this frequency band to a large extent both in line of sight (LOS) environment and non line of sight (NLOS) environment. In addition, the 3-Dimensional angle information is estimated with the space alternating generalized expectation-maximization (SAGE) algorithm. The angle spread of arrival (ASA) and the angle spread of departure (ASD) are investigated and the cluster characteristics at different rotation measurement sites are analysed. It is found that dimension of parameter space that used in clustering algorithm has a great impact on the clustering result of the recognizable multipaths.

The millimeter-wave communication is a key component in future broadband cellular networks. In this paper,a virtual multi-antenna measurement was conducted in an indoor line-of-sight scenario using a broadband channel sounder with 400 MHz bandwidth at 28 GHz. In the measurement,with an omni- directional antenna located in a fixed position at transmitter, horn antenna at receiver is rotated 360° in azimuth with the step of 2°, 3°, 5°, 8° and 10°, respectively. Then, the space- alternating generalized expectation-maximization (SAGE) algorithm was utilized to extract channel parameters from the measured results, including number of multipath components (MPCs) in azimuth domain, power azimuth spectrum (PAS) and root mean square azimuth spread of arrival angles (ASA). Finally, we compare these estimated parameters. This work aims to reveal the effects of the rotating step on the estimated results using SAGE algorithm in the virtual multi- antenna measurement. It is found that the number of estimated MPCs in azimuth domain decreases obviously as the rotating step rises, which further affects PAS and ASA. When with the step of 5°, the estimated ASA value is closest to the reference value of 3GPP TR 38.900.

The range of candidate bands considered for 5G wireless systems extends from 6-100 GHz. In this paper we examine the impact of key parameters of millimeter-wave (mmWave) and microwave channel models on various system performance metrics, e.g., spectral efficiency, eigenvalue structure and convergence to massive multiple-input multiple-output (MIMO) properties. We consider 6 spatial channel model (SCM) based scenarios, including 3GPP SCM at 2.6 GHz and those derived from recent measurement campaigns at 6 GHz, 28 GHz and 73 GHz. We define and evaluate an effective degrees of freedom metric (EDOF), demonstrating a dramatic EDOF reduction in mmWave bands compared to microwave, unless serving multiple users, where the antenna separation increases the effective rank of the composite channel. Furthermore, we analytically derive the covariance matrix of two SCM structures showing the impact of modelling choice on the channel correlations.

It is necessary to study the spatial evolution of clusters in massive multiple-input multiple-output (MIMO) mobile channel, which is one of the important features in cluster-based models. In this paper, we analyze the data collected from the massive MIMO mobile measurement campaign, with a 256-element virtual array at 3.5 GHz in line of sight (LoS) and non line of sight (NLoS) conditions, respectively. Firstly, the cluster-level angular power spectrums (APS), azimuth of departure (AoD) and azimuth of arrival (AoA), are shown to display the variation of clusters along with the measurement route. Secondly, the evolution of cluster number is presented, showing the changes of appearance and disappearance of the clusters, and the corresponding model based on birth-death process is built. Finally, the cumulative density functions (CDF) of radii of visibility regions (VR) are calculated, which are used to study the variations of VRs along with the measurement route, and lognormal distribution fits the CDFs well.

By taking advantage of the elevation domain, three-dimensional (3-D) multiple input and multiple output (MIMO) with massive antenna elements is considered as a promising and practical technique for the fifth Generation mobile communication system. So far, 3-D MIMO is mostly studied by simulation and a few field trials have been launched recently. It still remains unknown how much does the 3-D MIMO meet our expectations in versatile scenarios. In this paper, we answer this based on measurements with 56 × 32 antenna elements at 3.5 GHz with 100-MHz bandwidth in three typical deployment scenarios, including outdoor to indoor (O2I), urban microcell (UMi), and urban macrocell (UMa). Each scenario contains two different site locations and 2-5 test routes under the same configuration. Based on the measured data, both elevation and azimuth angles are extracted and their stochastic behaviors are investigated. Then, we reconstruct two dimensional and 3-D MIMO channels based on the measured data, and compare the capacity and eigenvalues distribution. It is observed that 3-D MIMO channel which fully utilizes the elevation domain does improve capacity and also enhance the contributing eigenvalue number. However, this gain varies from scenario to scenario in reality, O2I is the most beneficial scenario, then followed by UMi and UMa scenarios. More results of multiuser capacity varying with the scenario, antenna number and user number can provide the experimental insights for the efficient utilization of 3-D MIMO in future.

In order to meet the increasing demand on mobile data service, the fifth generation mobile communication systems (5G) expects to operate in high frequency band up to 100 GHz where the spectrum is available for wide bandwidth. Therefore, the channel characteristics and model for high frequency band up to 100 GHz are of great importance for technology evaluation and network deployment of 5G system. In this paper, wideband channel measurements were conducted in open office and shopping mall scenarios at the center frequency of 28 GHz with 800 MHz bandwidth. Channel impulse responses are obtained for directional and omni-directional channel by using horn antennas and biconical antennas, respectively. Delay characteristics are analyzed based on the measurement data. The measured delay spread is found to obey the log-normal distribution in the two indoor scenarios. The delay spread at 28 GHz in LoS case is smaller compared with that of indoor hotspot scenario in ITU-R M.2135 for below 6 GHz. Furthermore, it is found that the delay parameters are much smaller in directional channel than those in omni-directional channel.

With the fast development of high-speed trains (HST) globally, some related technical issues of HST communications should be addressed, e.g. time- variant (TV) radio channel modeling and channel estimation. In this paper, we propose a more generic TV channel model based on Qian Liu's research. Moreover, a generalized subspace-based channel estimation algorithm with the aid of an improved basic expansion model (BEM) is proposed for orthogonal frequency-division multiplexing (OFDM) systems in high-speed mobile environments. The proposed channel model is based on the sum-of- sinusoids (SoS) method and accounts for the fast fading characteristics of HST channels due to the terminal's movement. The statistical analysis shows that the proposed model can be considered as a more general channel for TV-HST scenarios. Additionally, to accurately estimate the TV-HST channels, the improved BEM is applied to reconstruct the model by using the subspace projection method. Finally, numerical simulation illustrates that the accuracy of our channel estimation algorithm.

Having the unique feature of wide continuous bandwidth, millimeter-wave communication has attracted great attention from both industry and academics. However, due to the large path loss and diffraction loss, human body shadowing (HBS) has a huge impact on millimeter- wave communication and requires intensive research. In this paper, we conduct HBS measurements in three different cases at 28 GHz by using a pair of horn antennas. Based on our measurement results, we propose a simple HBS model, and then use the double-edge and the multiple-edge diffraction method to study the characteristics of HBS. The gain of HBS is generally 10 dB or greater for the 16 m LOS link. It grows with the distance between TX and RX, and a relative small gain will be obtained when people are close to the antennas and LOS path. Furthermore, human body's direction will have great impact on the effective height, which finally causes 4.63 dB loss.

The millimeter-wave band will be one of the most key components in the next generation wireless communication system. In this paper, a radio channel measurement was conducted in an indoor office environment at 28 GHz with 500 MHz bandwidth. The channel sounder with the clock synchronization was used to measure in both line-of-sight (LoS) and none- line-of-sight (NLoS) scenarios. The channel impulse responses (CIRs) are recorded with an omnidirectional antenna at TX and a horizontal-rotating horn antenna fixed at the same height at RX as references. The space-alternating generalized expectation-maximization (SAGE) algorithm was applied to extract the channel characteristics of multipath components (MPCs) from the synthesized CIRs, and then the direct synthesized CIRs and the CIRs constructed from SAGE results were compared in terms of power delay profiles (PDPs) and power angular spread (PAS). It is found that the reconstructed results closely approximate real results. In addition, the cluster numbers and the inner-cluster root mean square (RMS) angle spreads are drawn after the clustering analysis and they cohere with the changes of the corresponding surrounding environment of the point, regardless of LoS or NLoS. The wireless channel propagation at 28 GHz is heavily dependent on the environment because the linear and reflective propagation are the main mode of transmission.

In this paper, the mobile channel propagation measurements at 3.5 GHz and 6 GHz have been performed in urban micro-cell scenario in the city of China. The measured cases include line-of-sight (LOS) and non- line-of-sight (NLOS) propagation. Statistical results and comparative analysis are presented in this paper, including power delay profiles (PDPs), path loss (PL), coverage, and root mean square (rms) delay spread (DS). It can be found that PDPs measured from campaign keep similarity in these two bands. The result based on PDPs demonstrates that, in mobile condition, the PL exponent of 6 GHz is a little greater than that of 3.5 GHz in both LOS and NLOS propagation. Moreover, as the frequency increases, the path loss will increase and the coverage will remarkably degrade. Last, the rms DS of 3.5 GHz and 6 GHz are similar in LOS case, while that of 3.5 GHz is higher in NLOS case.

In this paper, the channel propagation measurements have been performed in indoor scenario at 3.5, 6, 14, 23, 26 and 28 GHz. The measured cases include line-of- sight (LOS) and non-line-of-sight (NLOS) propagation. Statistical results and comparative analysis are presented in this paper, including path loss (PL) and root mean square (RMS) delay spread (DS). Both floating-intercept (FI) path loss model and alpha-beta- gamma (ABG) path loss model are provided. For the LOS propagation, path loss exponents at different bands are less than or closed to 2. PL exponents range from 2.7 to 4.2 in NLOS case. By fitting ABG PL model for each frequency band, the frequency dependent factors are observed as 2.21 and 2.33 in LOS and NLOS environments respectively. However, no tendency is found between the RMS DS and frequency in indoor scenarios. RMS DS seem not decreases with the increment of frequency bands. We also find that the RMS DS at any given location increases with separation distance and fluctuates seriously at larger distance.

This paper considers the uplink of a single-cell large-scale multiuser multiple-input multiple-output (MU-MIMO) system. The transmitted data is detected by linear receivers at the base station (BS), that is, maximum ratio combining (MRC), zero-forcing (ZF) and minimum mean square error (MMSE). New non-asymptotic outage expressions that are valid for any number of BS antennas are derived. It is shown that the MMSE scheme always achieves the best outage performance, and the ZF scheme outperforms the MRC scheme in the high signal-to-noise-ratio (SNR) regime, while the opposite holds in the low SNR regime. We also demonstrate that when the number of BS antennas is very large and the number of users is fixed, the asymptotic outage probability tends to zero, providing that the channel state information (CSI) is perfect. However, if both the number of BS antennas and the number of users grow large, it would be a different matter. Extensive numerical results are presented to verify the theoretical analysis.

In this paper, we investigate the statistical characteristics of the three dimensional (3D) multiple- input multiple-output (MIMO) channels and their dependency on the dynamic range in channel impulse responses (CIRs). Our study is based on empirical results obtained from the 3D MIMO channel measurement campaigns at 6 GHz in a typical urban macrocell outdoor-to-indoor scenario. The space-alternating generalized expectation-maximization (SAGE) algorithm is employed to estimate parameters of multipath components from the measured data. The measured data is divided into groups based on the dynamic range in the measured CIRs. The second-order statistics in time and spatial domains are calculated for each group. It is found that the channel statistics shows strong dependency on the dynamic range in the measured CIRs.

In the last decade, Nakagami fading shows good performance in matching measurement data under many scenarios, i.e., vehicle-to-vehicle (V2V), mobile-to-mobile (M2M) and indoor. However, little research has been done for generating of spatial-temporal correlated MIMO Nakagami fading. In this paper, we propose a generalized and accurate algorithm to generate Nakagami-m envelope channels with arbitrary temporal autocorrelation and spatial cross-correlation. Specifically the temporal autocorrelation of Nakagami channels is derived using rank matching technology, then spatial cross-correlation is introduced by Sim's algorithm. Finally, the proposed algorithm is validated with theoretical data. This algorithm is valuable for communication system simulation and emulation.

In this paper, we present the small scale characteristics of indoor office scenario based on the channel measurement at 14 GHz with 250 MHz bandwidth. The measurements were operated at more than 100 receiver locations including LOS and NLOS cases over distances up to 55 meters by utilizing a sliding correlator sounder with 125 mega-chip per second (Mcps). Besides, highly directional horn antennas and dipole antennas were used, respectively, to get the channel characteristics including power delay profile (PDP), path number and root-mean-square (rms) delay spread. The results of path number and rms delay spread measured by dipole antennas and horn antennas are different in LOS case. Compared with the results measured by horn antennas, the path number and rms delay spread value obtained by dipole antennas is bigger in LOS case, where rms delay spread values are 30 ns and 52 ns, respectively. It is reasonable that some multipath information is lost when using horn antenna to measure. In NLOS case, path number and rms delay spread only measured by dipole antennas are given.

We present the results of a 3 dimensional (3D) multiple-input multiple-output (MIMO) channel impulse response (CIR) measurement conducted in China and New Zealand (NZ) for an outdoor-to-indoor (O2I) urban macro environment. Key channel parameters are computed from the measured data using the space-alternating generalized expectation-maximization (SAGE) algorithm, and are used to re-construct the 16 × 16 3D MIMO CIR according to the guidelines given in the 3GPP 3D MIMO channel model. The CIR is then used to evaluate the channel capacity for four different antenna configurations. These different topologies, along with different antenna spacings, allow us to study the characteristics of the MIMO channel with different spatial correlation structures.

In this paper, the channel measurements for 5th generation mobile networks (5G) potential spectrum at 3.35 GHz, 4.9 GHz and 5.4 GHz have been performed in the urban macro cell scenarios in China. The measured cases include line-of-sight (LOS) and non-line-of-sight (NLOS) propagation. After acquiring the channel impulse response from the measurement data, the empirical log-distance path loss models are derived. Free-space and ITU-R models are analysed as references. To explore the relation between path loss and carrier frequency, we revise the proposed path loss models by introducing the frequency dependence coefficient (FDC). Furthermore, when FDC is applied, a more accurate description of path loss differences among three frequencies can be obtained in NLOS case. Measurements can be implemented in more scenarios to acquire optimised channel models in the future.

In this paper, we perform 3-Dimensional (3D) clustering based on the Outdoor-to-Indoor (O2I) wideband 3D multiple-input-multiple-output (MIMO) channel measurement at 3.5 GHz. Clusters are identified by KPowerMeans algorithm. Based on analysis on clustering results, we modified the definition of Multiple component distance (MCD) to split the bounding of azimuth and elevation, which can obtain larger number of clusters and the clusters are more intra- compact and inter-separated. Then, Calinski-Harabasz (CH) and Davies-Bouldin (DB) indices are used to further validate the proposed MCD. Finally, intra cluster and inter cluster statistics are both provided, which provides insights in 3D MIMO channel modeling.

This paper addresses the energy-efficient resource allocation problem in downlink Orthogonal Frequency Division Multiple Access (OFDMA) networks with multiple users. The joint optimization of power and subcarrier allocation to maximize bits-per-Joule is formulated constrained by the maximum transmit power and the minimum required system data rate. Due to the prohibitive computational complexity, this paper proposes an iteration-based sub-optimal scheme with superlinear convergence based on the Dinkelbach method by relaxing the constraints and exploiting the properties of non-linear fractional programming. The analytical solutions for the power and subcar-rier allocation in each iteration are derived by the subgradient method according to Karush-Kuhn-Tucker (KKT) conditions and Lagrange multipliers. Finally, the simulation results validate the convergence and energy efficiency performance of the proposed scheme.

In this paper, an implementable channel quantization scheme that can effectively exploit both spatial and temporal channel correlation for massive MIMO systems is proposed. In limited feedback systems, differential quantization conducted by applying skewing and rotations on a differential codebook is effective on improving the overhead efficiency practically. To apply these techniques in massive MIMO systems, we adopt noncoherent trellis coded quantization (NTCQ) for Rayleigh channel as a foundation. The inherent codebook of NTCQ is defined and investigated thoroughly. Then we propose a scheme that can produce a differential inherent codebook, which makes adaptive skewing and rotations applicable. In numerical simulations, compared to previous approaches on differential NTCQ, superiority of the proposed scheme is significant. It needs no prior statistical knowledge of channel correlation, while high overhead efficiency can also be achieved. The results reveal that in massive MIMO systems, if ideal channel state information at user terminals is assumed to be available, precisely feeding them back to base stations is practical within affordable overhead and computations.

This paper analyzes the performance of MIMO downlink broadcast channel employing precoding techniques, with imperfect channel state information at the transmitter (CSIT) due to limited feedback. We focus on two optimization issues that related to power control (PC) in precoding. Firstly we employ a distributed and competitive PC scheme for the interference-limited network, and derive two sufficient conditions of Nash Equilibrium, which are the upper bounds for achievable SINRs. Secondly, maximum sum capacity (SC) is investigated to show the maximal ability of PC in precoding. From Monte-Carlo simulation, it reveals that the capacity boost of non-linear Tomlinson-Harashima precoding (THP) with PC is significant as compared to its alternatives. Specially, the more users are served, the higher capacity gain is achieved. Maximally about 0.9 bps/Hz capacity gain can be achieved for THP, which is about 10.8% better than equal power allocation (EP).

In this paper, we present a cluster parameter based analysis of an outdoor-to-indoor (O2I) Multiple-Input-Multiple-Output (MIMO) measurement campaign carried out at 3.5 GHz in an office building. The measurement was performed using a wideband multi-antenna radio channel sounder. Besides azimuth angle of arrival (AoA), azimuth angle of departure (AoD) and delay domains included in traditional 2 dimensional (2D) MIMO channels, 3 dimensional (3D) MIMO channels also consider elevation angle of arrival (EoA), elevation angle of departure (EoD) when describing clusters. By introducing the elevation domain, we observe that multipath components (MPCs) within one cluster in 2D MIMO channels can be divided into several clusters in 3D MIMO channels and each cluster has MPCs with smaller power weighed multipath component distance (MCD). We also analyze the effect of height difference between transmitting and receiving ends on the clustering performance. There is a tendency that clusters show smaller parameter values when the height difference is smaller. After that, we describe all the cluster parameters by a set of probability density functions (pdfs) and give typical values in this scenario.

We consider a multipair two-way relay network where multiple communication pairs simultaneously exchange information with the help of a single relay. Each terminal has only a single antenna, while the relay is equipped with a very large antenna array. We further assume that channel state information is available at the relay node. We investigate the power efficiency of this network when very simple signal processing, i.e., maximum ratio combining (MRC) or zero-forcing (ZF), is used at the relay. When the number of relay antennas grows infinite, the transmit power of each terminal or relay (or both) can be made inversely proportional to the number of relay antennas while maintaining a given quality-of-service. We show that with very large antenna arrays, the two-way relaying scheme outperforms both the orthogonal scheme and the one-way relaying scheme.

It is well known that relay is able to neutralize some interferences in destinations. In this paper we use not only relay to neutralize interferences but also interference alignment to reduce the number of antennas needed in destinations. To this end, a scheme of cognitive relay-aided interference neutralization and alignment is proposed. To neutralize and align interferences, the relay needs to retransmit the signals using proper transmitting vectors. We demonstrate that the 3-user MIMO interference channels with cognitive relay can achieve 2M degrees of freedom (DoF) when each node has M antennas. It is a big improvement compared to k-user system using interference alignment which is able to obtain 3M/2 DoF. The transmitting vectors in sources and relay are carefully designed to not only satisfy the neutralization and alignment constraints but also achieve higher sum-rate, which can be proved by simulation results.

This paper reports the methodology and results from our latest field measurement campaign for characterizing the 3-dimensional (3D) MIMO channel, particularly in the elevation domains, in a typical urban macro (UMa) environments in Beijing, China. Stochastic channel model parameters are obtained based on the high-resolution multi-path parameter estimates. In addition, a distance dependent elevation angular spread model is proposed based on field observations. These works enables the realistic evaluation of 3D MIMO system performance and extends the applicability of the ITU SCM models in 3D MIMO system study.

Detection of wideband communication signals is critical for cognitive radio (CR) as it enables secondary users to dynamically access the unoccupied bands. However, accurate and fast spectrum sensing is still a challenge in low signal to noise ratio (SNR) environment. To encounter this problem, a feature detector based on compressed sensing (CS) and wavelet transform (WT) (CS-WT feature detector) is proposed. Feature detector is chosen for its accuracy under low SNR, and CS is introduced to alleviate the sampling bottleneck of wideband sensing. Moreover, noise caused by the CS process is analyzed, and a traditional noise reduction method-two dimensional wavelet transform is utilized to cope with it by treating the spectral correlation function (SCF) as a grey image. It is verified by simulation that WT can effectively reduce the noise introduced by CS, and the proposed detector can achieve 90% detection probability under -10dB, making cyclostationary detection based on CS applicable.

The adaptive AF relay combined with the active antenna system (AAS) and angle estimation strategy is studied in this paper. To improve the system performance, we introduce the AAS scheme into AF relay. Meanwhile, we also propose a joint AAS relay (JAR) angle estimation strategy to obtain the useful horizontal angle information. Results show that, in the temporary cover or emergency communication scene, the adaptive AF relay with AAS helps to improve the system performance gain effectively and also enhance the robustness stably.

Based on the channel measurement in a typical indoor environment, a simple mode selection criterion is originally proposed for closed-loop multiplexing transmissions using LTE codebook. The fitting result of the capacity loss (CL) due to the limited size codebook is presented. High signal-to-noise ratio (SNR) analysis is also given to provide a deep insight for the addressed mode selection problem. Furthermore, the effect of the codebook design and dimension on the capacity loss is also investigated. Though based on a specific environment, the generality of the proposed algorithm is validated using ITU-R M.2135 channel model in four scenarios. The results are important for the adaptive technique development in modern communication systems.

Considering three dimensional (3D) channel with elevation effect, this paper addresses pilot spacing design in frequency, time and spatial domains for orthogonal frequency division multiplexing (OFDM) systems with multiple antennas. Employing the channel spatial correlation between transmit antennas, pilot overhead can be reduced substantially without estimation accuracy loss. On the other hand, the accuracy of pilot aided channel estimation (PACE) can be improved. This paper also verifies the performance of cascaded lower-dimensional Wiener filters in mean square error (MSE) sense. Numerical results show that the channel estimation in frequency-space domain is undecomposable.

In the absence of a control channel, the idle channel utilization (ICU) is dictated by both the spectrum sensing strategy and the packet access protocol in a distributed cognitive radio (CR) network. In this paper, we investigate a distributed multi-channel random access CR network under imperfect spectrum sensing, where each secondary user (SU) randomly selects a fixed number of channels for sensing and transmits on arbitrary detected idle channels. Based on this model, we first develop a slotted opportunistic access aloha protocol for multiuser random access. Then, the distribution of the successful packet transmissions is derived, according to which we obtain the expression of the average ICU. Finally, the analytical results are validated by substantial simulations. We show that their impacts on ICU by adjusting the number of the sensed channels and packet transmission probability, along with the distance from SUs to primary users.

In this paper, the antenna modeling method in the International Mobile Telecommunications-Advanced (IMTAdvanced) channel model is validated by field channel measurements in the indoor scenario at 2.35 GHz. First, the 2x2 MIMO channel impulse responses (CIRs) are recorded with practical antennas as references. Second, the CIRs are reconstructed from the available IMT-Advanced channel model with field patterns of the practical antennas and updated spatial parameters extracted from the similar scenario measurements. Then comparisons between the field CIRs and the reconstructed CIRs are made from coherent bandwidth, eigenvalue dispersion, outage capacity, and ergodic channel capacity. It is found that the reconstructed results closely approximate real results in the coherent bandwidth and correctly describe the statistical characteristics in frequency domain. Compared to the field CIRs, the spatial correlation of the reconstructed CIRs with either type of antenna have a wider range, that causes the underestimation of the 5% channel outage capacity. Due to the negligence of the coupling among the antennas and the near field effect of antenna, this modeling method will have a great impact on the characteristic of radio channel, especially on the spatial characteristics.

It is known that channel model of propagation characteristics are crucial in the research and evaluation of three-dimensional multiple-input multiple-output (3D-MIMO) technique, especially the elevation angle (EA) model. In this paper, the results of 3D-MIMO channel measurement in the urban macro-cell (UMA) scenario at 3.5 GHz are presented. Based on the measurement data, it is find that the mean value of elevation angle has a offset angle to line-of-sight (LOS) angle especially in not-line-of- sight (NLOS) situation. And the offset angle is depend on the azimuth distance between BS and MS which can be modeled as a function that use distance as a parameter. A novel way to model mean angle of elevation angle is proposed at the end of this paper. The relation between Circular angular spread (CAS) and distance is also studied, the measurement result shows that the effects caused by distance can be neglected.

Cooperative multi-relay networks are asynchronous both in time and frequency inherently due to their spatial distributed nature. This paper addresses channel estimation for amplify-and-forward relay networks using training sequences in the presence of both time and frequency offsets. A practical training scheme is provided to reduce the complexity at the relay nodes and to combat the effects of time and frequency offsets at the destination. By exploiting the training scheme, a piecewise channel estimator with respect to signal-to-noise ratio (SNR) is proposed to reduce the computational complexity of the minimum mean square error (MMSE) method. Simulation results show that our method approaches the MMSE estimator and outperforms the linear least-square (LS) estimator.

In this paper, we focus on detecting, estimating and tracking spatial multi-paths in radio propagation, based on elaborated channel sounding. An algorithm based on least-square criterion is proposed, and the multi-paths are estimated by an iterative method. The particle swarm optimizer(PSO) is used to guarantee both global optimum and computational efficiency in each iteration. Realistic system influence, such as system response, non-isotropic array gain, are considered and mitigated. The precision is theoretical analysed and experimental verified. It is shown that in this scheme, the spatial multipath separating and tracking ability can be significantly improved, and reliable results can be obtained. Moreover, the computational efficiency is significantly raised, and the trade- off of precision and complexity can be flexible.

High-speed railway (HSR), as an important deployment scenario for both the present and the future mobile wideband radio communication systems, has attracted more and more attention all over the world with the rapid increasing demand of the high data rate communication service on traveling. For the purpose of capturing the wideband channel characteristics of HSR, a channel measurement campaign was conducted at the center frequency of 2.35 GHz with 50 MHz bandwith in the U-shape cutting scenario of Zhengzhou--Xian HSR line in China. Based on the field measured data, we analyze the small scale characteristics in detail, which maily include path number, root mean square delay spread (rms DS), and doppler shift. It is found that the distribution of the path number is well fitted by a Gamma distribution. The statistics of rms DS in the U- shape cutting scenario are larger than the results in other scenario of HSR. In addition, an increasing tendency of rms DS against the transmitter-to- receiver distance is observed and can be modeled by a linear function. Finally, the doppler frequency shift is verified and meets the theoretical value.

Green radios have drawn much attention in recent years. This paper derives the energy per good-bit expressions for semi-blind relay networks with and without maximum ratio combining under Rayleigh fading channels considering the energy consumed per bit as well as the link reliability and retransmission probabilities. The optimal constellation size is investigated constrained by a given bit error rate. The energy consumed in the transmission, reception and idle modes are all involved. Numerical simulations demonstrate the energy efficiency performance of semi-blind relay networks in several cases with the direct transmission as the benchmark. Finally some practical implications can be made from these observations. The analysis and results will be a good reference for the real green cellular communication design.

This paper deals with timing and frequency synchronization in multi-relay cooperative networks operating with both large and small carrier frequency offset (CFO) over frequency-selective channels. A novel preamble based on constant amplitude zero auto-correlation (CAZAC) sequence is proposed, and a corresponding practical multistage scheme is presented. Joint timing and integral frequency synchronization is involved to resist multi-relay interference (MRI). Then, fractional frequency estimation is carried out, and fine timing estimation completes the synchronization scheme. The performance is evaluated in terms of the mean square error (MSE). Simulation results show that the method is robust under both flat-fading and multipath fading channels, and provides accurate estimation results in the presence of both large and small multiple CFO values.

Based on the channel measurement in a typical indoor environment, wideband multi-input multi-output (MIMO) channel capacity analysis result is reported in this paper. A series of non line-of-sight (NLOS) and LOS measurement positions are planned for capacity comparison in different propagation conditions. For fixed received signal-to-noise ratio (SNR), slight average capacity loss in LOS cases is observed compared to NLOS cases. However, the capacity in presence of LOS component appears more position-sensitive than that in NLOS cases. When considering the large scale path loss difference introduced by the receiver movement, the capacity in LOS cases greatly exceeds the NLOS cases. A key finding is that the measured maximum channel eigenvalue fits t Location-Scale distribution rather than Gamma distribution in indoor scenario. Finally, Ricean K-factor and spatial correlation results are presented.

Green radios have drawn much attention in recent years. This paper derives the energy per good-bit expressions for MQAM and MPSK in DF relay networks under AWGN and Rayleigh fading channels. The energy per good-bit is defined as the energy per correctly received bit, which indicates that the energy consumed per bit as well as the link reliability and retransmission probabilities are considered. An energy efficient mode switching transmission is proposed. Constrained by a given probability of bit error, the optimal constellation size of MQAM and MPSK to achieve energy efficiency is obtained. Finally, computer simulations are also carried out to analyze the energy efficiency performance with respect to system parameters such as relay position and transmission distance.

In conventional amplify-and-forward relay networks, relays retransmit signals with predetermined transmit power and amplification factors assuming perfect channel knowledge. This paper proposes a relay selection scheme with amplification factor optimization according to the minimum mean square error criterion when the channel state information is imperfect due to the channel estimation error. Relays can adaptively adjust their amplification factor and transmit power according to the quality of the channels. The relay selection includes both single and multiple relay selection schemes. Finally, numerical results prove the advantage of our proposed scheme compared with the relay selection on the basis of the normalized amplification factor. The impact of channel estimation error is analyzed. We also investigate how the number of selected relays has an impact on the system performance.

SUMMARY Although the demand for battery capacity on mobile devices has grown with the increase in high data rate applications, battery technology has not kept up with this demand. Therefore, the growth in energy demand coupled with global warming provide a new trend in wireless communication known as energy‐efficient transmission. In this paper, the energy‐efficient resource allocation for a two‐hop uplink multiuser relay‐based system is studied. We adopt the orthogonal frequency division multiplexing as the physical layer modulation technique. Assuming that the base station has all the channel state information, an energy efficiency optimization problem by joint subcarrier assignment, bit and power allocation is formulated. We first develop a near‐optimal resource allocation scheme to maximize the overall energy efficiency; then, an efficient resource allocation algorithm is provided to solve the problem with relatively low computational complexity. Furthermore, fairness constraint among users is imposed on the system to guarantee each user's QoS. Our joint resource allocation algorithm is proved to achieve a considerable improvement in terms of energy‐saving and simultaneously decreases outage probability by simulation results. Copyright © 2012 John Wiley & Sons, Ltd.

In this study, the authors address the problem of transceiver design in an amplify‐and‐forward multiple‐input multiple‐output relay system with full or partial channel state information (CSI), where linear processing is applied to maximise the average received signal‐to‐noise ratio (SNR) at source, relay and destination, respectively. This joint design problem is a non‐convex optimisation problem, so the authors convert it into a scalar problem and derive the optimal unitary decoder under average power constraint. The precoder at source and relay can be obtained by employing generalised Rayleigh quotient method. Specifically, two CSI assumptions are considered: (i) the authors first derive the precoder and decoder with full CSI; (ii) assumimg the statistical independence between the channel estimated value and channel estimation errors, the problem with partial CSI can be solved as the same way as full CSI case. Numerical results show a great improvement on the average received SNR by applying the proposed scheme. On the other hand, the authors also illustrate the impact on received SNR arose by the channel estimation errors.

In this paper, we investigate a novel slotted ALOHA-based distributed cognitive network in which a secondary user (SU) selects a random subset of channels for sensing, detects an idle (unused by licensed users) subset therein, and transmits in any one of those detected idle channels. First, we derive a range for the number of channels to be sensed per SU. Based on that, the analytical average system throughput is derived in both saturation and non-saturation networks. Second, the relationship between the average system throughput and the number of sensing channels is attained. We show that the optimal number of sensed channel in a given number of SUs is dependent on the number of licensed channels, the number of idle channels, and the transmission probability of each SU. Finally, the analytical results are validated by substantial simulations.

In this paper, we address a two-way relay network (TWRN) where two source nodes exchange their information through a relay node (RN) in a bi-directional manner and propose a receiver design method at RN and source nodes under variable-gain amplify-and-forward (VG-AF) relay system. A two-period transmission protocol is employed: in the training period, source node and RN will send their own training signals to obtain channel state information (CSI); in the transmission period, two source nodes exchange their information based on the CSI obtained in the training period. We develop the Maximum-Likelihood (ML) estimator and Linear-Minimum-Mean-Square-Errors (LMMSE) estimator and derive the expression of channel estimation errors. Numerical results show that VG relay system outperforms fixed gain (FG) system in terms of mean square errors (MSE) of channel estimation and bit error rate (BER) performance. Besides, in contrast to the high Peak-to-Average-Power-Ratio (PAPR) in FG relay system, simulation results show that VG relay system has a more stable transmission power.

This paper investigates the cluster dynamic behaviors in an outdoor propagation environment to facilitate the outdoor channel modeling and simulation. To identify the relevant clusters from measured channel data in joint spatial-temporal domain, an automatic cluster identification algorithm has been employed. By introducing a closed loop scheme and a multi-dimension filter, significant improvements on the algorithm performance can be obtained. Furthermore, based on analysis on the cluster identification results, this paper proposes two stochastic models for cluster time-variant characteristics including cluster lifespan and cluster spatial-temporal evolution. Empirical examples reveal the applicability and accuracy of the models of which can be made use in related simulations to imitate the continuous evolution of realistic channels.

In this paper, the energy-efficient resource allocation in relay-aided uplink multiuser system is studied. We adopt the orthogonal frequency division multiplexing (OFDM) as the physical layer modulation technique. Assuming that the BS has all the channel state information (CSI), an energy-efficiency optimization problem through joint subcarrier assignment, bit and power allocation is formulated. Due to the computational complexity restriction, a suboptimal solution based on decomposition is presented to solve the problem with low complexity. Considering the fairness constraint, a joint resource allocation algorithm is further proposed, which is proved to achieve a considerable improvement in terms of energy-saving and simultaneously decreases rate outage probability by simulation results.

This paper addresses joint source-relay precoder and decoder designs for a single data flow transmission in amplify-and-forward (AF) multiple-input-multiple-output (MIMO) relay networks with imperfect channel state information (CSI). First, the precoder is obtained by improving the lower bound of the received SNR under power constraints at source and relay. Then, we derive the decoder to maximize the average received signal-to-noise ratio (SNR). Numerical results show a great improvement on the received SNR by applying our proposed schemes. Besides, we also make a discussion about the impact which brought by the channel correlation coefficients based on our derived received SNR expression and the numerical results.

In this paper, we investigate a novel slotted ALOHA-based distributed access cognitive network in which a secondary user (SU) selects a random subset of channels for sensing, detects an idle (unused by licensed users) subset therein, and transmits in any one of those detected idle channels. First, we derive a range for the number of channels to be sensed per SU access. Then, the analytical average system throughput is attained for cases where the number of idle channels is a random variable. Based on that, a relationship between the average system throughput and the number of sensing channels is attained. Subsequently, a joint optimization problem is formulated in order to maximize average system throughput. The analytical results are validated by substantial simulations.

Distributed antenna system (DAS) is a very promising technology to enhance the capacity of indoor communication system due to the inherent macro and micro diversity. With its natural characteristics of reducing the access distance and increasing the probability of line-of-sight (LoS) coverage, DAS can greatly enhance the power efficiency, which is in accordance with the goal of green communication. In this paper, the downlink performance of an indoor DAS is investigated based on a realistic wideband multiple input multiple output (MIMO) channel model constructed with measured data at 5.25 GHz. Different transmission schemes and antenna configurations are compared from the capacity point of view. It is demonstrated that with the same total transmit power DAS with selection transmission (ST) can increase the capacity significantly compared with centralized antenna system (CAS) and DAS with equal-power transmission. The results also show that MIMO technique can improve the system performance significantly even under LoS propagation conditions for indoor scenarios, which is counterintuitive to the common sense that MIMO will suffer from great performance degradation under LoS conditions.

Coordinated multi-points (CoMP) transmission has been widely proved to be effective to combat co-channel interference by a number of theoretical literatures. However, a common assumption is made that the optical fiber required by coordination between base stations (BSs) is always ready to be used which is quite impractical in the real world. Therefore, Intra-Site CoMP, which only involves coordination between sector antennas, is much more easily implemented since the sector antennas are located together. In contrast with the previous theoretical researches, a measurement at 2.35 GHz, with a bandwidth of 50 MHz, was carried out to evaluate its spectral efficiency performance. A novel measuring method is put forward to avoid multi-BS synchronization as well as other challenges in the implementation of CoMP. It is found that Intra-Site CoMP can greatly improve the throughput of the system under the measured environment.

In this paper, we consider a four-node relay-based OFDMA system. The expression of the upper bound for the achievable rate in Successive Relaying (SUR) protocol is derived by using the cut-set theorem for half-duplex systems. Based on this expression, the near-optimal solution of the achievable rate is obtained in the joint power and subcarrier allocation constraint, according to the dual problem decomposition approach and the subgradient method. Moreover, we make a comparison on the achievable rate between SUR and Simultaneous Relaying (SIR) in the pathloss model accepted by the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) Advanced system. The simulation results demonstrate the enhancement of the achievable rate in SUR protocol is expanded in high signal-to-noise ratio (SNR) compared with the achievable rate in SIR protocol under both the symmetric and asymmetric cases. And for SUR protocol , the achievable rate in symmetric case performs better in high SNR.

In this paper, we consider cooperative beamforming in an amplify-and-forward (AF) cooperative network with multiple relay nodes. Specially, only "appropriate" relay nodes are selected to perform cooperative beamforming. Source node can determine the relay nodes with just mean channel gain, which will reduce the complexity of obtaining instantaneous channel state information (CSI). This scheme guarantees that the energy is allocated to those "appropriate" relay nodes, and accordingly improves the energy efficiency. Therefore, it is able to provide superior diversity over the conventional cooperative beamforming. We also prove that power allocation (PA) between source and selected relay nodes is a convex problem, which can be resolved with lower computational complexity. Simulation results demonstrate that our scheme achieves an essential improvement in terms of outage performance, as well as high energy-efficiency for energy-constrained networks.

In broadband orthogonal frequency division multiplexing (OFDM) systems, accurate channel state information (CSI) is required to support coherent detection and diversity combining. However, it is indeed challenging for channel estimation in real systems since some tones are nulled to ease the implementation of spectral masking filters. We propose a novel channel estimation scheme which can solve the leakage caused by virtual tones (VTs) via effective energy compensation and partial refinement with no extra computational complexity. Additionally, the proposed scheme works well with different allocations of various VTs, and hence applicable for hardware realization. Simulation results validate that the proposed method is efficient to combat the leakage, and also robust to the existence of VTs.

Cooperative communication has been recently proposed as a way to improve the performance of wireless communication. However, due to the half duplex constraint, most of the relay networks face a fundamental challenge in terms of multiplexing loss. In this paper, we present an adaptive cooperation via relay selection (ACRS) protocol that improves spectral efficiency. This protocol lets the source transmit most of the time and always allows source transmission to be forwarded only by the best transmitter available (maybe source itself) if needed. ACRS has the feature of adapting the system to the fluctuation of the wireless channel and trying to make a packet transmission finished in just one time slot. The diversity-multiplexing tradeoff (DMT) is used to show a remarkable improvement over previous relaying schemes. Simulation results are presented to verify our analysis.

In order to obtain a more accurate assessment of relay performance in real-world outdoor propagation environment and to provide guidelines for the relay-based system deployment in the IMT-Advanced frequency band, the performance of a variety of relay schemes is investigated based on wideband measurements. The measurements were conducted at 2.35 GHz with 50 MHz bandwidth, which is within the frequency bands allocated to the IMT-Advanced system. We pay attention to two aspects: 1) the performance evaluation of a variety of transmission schemes in real propagation environment, and 2) the impact of propagation environment on the relay performance. Based on the measured channel transfer matrix, the achieved signal-to-noise ratio (SNR), spatial diversity and capacity of different transmission schemes are analyzed and compared. The measurement results reveal that in the NLOS region of the base station (BS) or in the region far away from the BS, the decode-and-forward (DF) relaying can significantly enhance the system performance.When the quality of the link between the BS and the relay station (RS) is good, the DF can provide larger performance improvement than the amplify-and-forward relaying. It is also found that propagation condition in the link between the RS and MS has major impact on the SNR, but minor impact on the spatial diversity.

For multiple-relay cooperative networks with multiple antennas deployed at source and destination nodes, we investigate the outage performance of selection based semi-blind amplify-and-forward (AF) relaying, where transmit beamforming (TB) is conducted at source transmission and maximum ratio combining (MRC) at destination reception. Based on the Kolmogorov-Smirnov test, we analyze the impact of the configuration of destination antennas on the outage performance under arbitrary Nakagami- m fading channels. Results reveal that increasing the number of destination antennas is not necessary for an improvement of outage performance with any Nakagami- m parameter. Inspired by this fact, an approximation is proposed for the optimal selection. Simulation results show that the approximation is an efficient selection method.

This comment points out several errors in the above letter. The correct frequency offset estimator is then proposed. Monte Carlo simulation results validate our analytical observations.

In this paper, statistical analysis of clusters are presented based on the indoor hotspot wideband multiple-input multiple-output (MIMO) channel measurements at 2.35 GHz. Clusters are identified with an automatic cluster identification algorithm according to the power weighted multiple component distance (MCD) jointly on the azimuth of arrival(AoA), azimuth of departure(AoD), elevation of departure(EoD), elevation of arrival(EoA), and delay domains, instead of traditional delay-AoAAoD domains. A new method at the initial step of KpowerMeans algorithm is proposed, which yields more intra-compact and inter-separated clusters. After that, the intra cluster parameters including delay spread (DS), azimuth spread of arrival (ASA), azimuth spread of departure (ASD), elevation spread of arrival (ESA), and elevation spread of departure (ESD) of the clusters, and inter cluster parameter of the cluster number distribution are presented in both line-of-sight (LOS) and none-line-of-sight (NLOS) scenarios. These results show concrete information about cluster propagation in indoor scenario which are very useful to the design and evaluation of IMT-Advanced systems.

In this paper we present single antenna relay channel measurements conducted in an urban environment at 2.35 GHz. Three types of links, i.e. base station to mobile station (BS-MS), relay station to mobile station (RS-MS) and base station to relay station (BS-RS), were measured at two sites. Our investigation focuses on the characteristics of large scale parameters (LSP) of the RS-MS link, which is characterized by the low antenna height at RS and short RS-MS distance. Measurement results show that the current BS-MS path loss model cannot perfectly predict the propagation loss of RS-MS link. The distance dependent property and the distribution of Ricean K-factor are analyzed. The RS-MS link is found to exhibit lower Ricean K-factor compared to the BS-MS link. We also investigate the capacity gain provided by the relay link when the MS is located in the shadowing area of BS. Furthermore, it is observed that the capacity gap between decode-and-forward (DF) and the fixed gain amplify-and-forward (AF) relay schemes vanishes, provided the large K-factor and high SNR of BS-RS link. This gap becomes larger as the K-factor of BS-RS link decreases.

In this paper, we propose a novel modeling method of indoor broadband fixed wireless access (BFWA) MIMO Ricean channel based on empirical results obtained from stationary channel measurement at 4.9 GHz in both indoor line of sight (LOS) and non-LOS (NLOS) scenarios. The phenomena that K-factors decay versus delay linearly in both the LOS and NLOS scenarios are reported. Extended Saleh-Valenzuela cluster structure is proposed to model Ricean components and has been validated by empirical results. Channel model of IEEE 802.11n is employed as a baseline model and both ergodic and outage mutual information (I) are used as metrics to evaluate the performance of novel method. Simulation results show that, compared to the original way, the performance of novel method on ergodic I and outage I is closer to empirical results in respectively LOS and NLOS scenarios.

In this paper, we consider a two-hop orthogonal frequency division multiplexing (OFDM) relay link, and investigate the system capacity in hybrid forward (HF) relay through the power allocation (PA). The expression of the system capacity in HF relay is provided. Two situations about the power constraint are concerned, and under each situation, the selection criterion among different forwarding schemes in HF relay is proposed by means of capacity comparison on the subcarrier pair. From the selection criterions, the source or relay can adaptively choose the best scheme for every subcarrier pair according to the channel condition. Then the optimal solutions of the relay PA and the joint PA are derived to maximize the capacity. In the simulation, the advantage of the HF relay is shown in terms of capacity improvement compared with the conventional forward relay.

Polarized antennas equipped in multi-input multioutput (MIMO) systems are anticipated as space-effective alternatives. In this situation, the impacts of polarization configurations on fading correlation should be considered. A novel model with flexible polarization orientation is proposed for calculating spatial correlation. The model demonstrates that introducing of polarization reduces the spatial correlation of antennas. To maximize the performance at mobile station (MS), tradeoff between diversity gain and mean effective gain is investigated. The proposed evaluation model proves that ±45° polarization at MS is always optimal, as traditionally believed. However, polarized antennas at base station (BS) also have an significant effects on system performance, especially in environment with high crosspolarization ratio (XPR). By applying ±45° polarization at both ends, increment on system gain is benefited relative to polarized antennas at MS alone.

The channel state information (CSI) of all links should be available at all nodes for cooperative strategies design in relay networks. However, many channel estimation methods for amplify-and-forward (AF) relay networks only estimate a compound CSI of source-relay-destination (S-R-D) link. In this paper, we propose a channel estimation scheme for destination to disintegrate the CSIs of S-R and R-D links from the cascaded S-R-D link in AF relay networks. The proposed scheme estimates the CSI of R-D link firstly by utilizing some pilot signals inserted at the relay node. Then, the CSI of S-R link is estimated based on the received pilot signals and the pre-estimated CSI of R-D link. The channel estimation error of the proposed scheme is also analyzed. Especially, least square (LS) algorithm and minimum mean square error (MMSE) algorithm for the CSI estimation of S-R link are compared. We verify that the mean square error of LS estimation for S-R link is nonconvergent but that of MMSE estimation is convergent. At the end of this paper, simulation results are given to support the theoretical results.

In cooperative communication systems, multiple carrier frequency offsets (MCFO) appear due to the different accuracy of the nodes' oscillators and Doppler spreads, which drastically degrades the performance of space-frequency block coded (SFBC) cooperative systems. In this paper, a MCFO compensation algorithm including modified SFBC decoder, minimum Euclidean distance decision criterion and interference reconstructing eliminator is proposed. Besides, the bit error ratio (BER) lower bound of cooperative communication systems with MCFO is theoretically derived. From the simulations, the correctness of our theoretical analysis can be validated, and it is also proved that the proposed compensation algorithm achieves good BER performance with a large range of MCFO.

In this paper, measurement data from wideband multiple-input multiple-output (MIMO) channel measurements at 2.35 GHz are presented. Measurements were preformed in indoor hotspot scenario with no line of sight (NLOS). Space-alternating generalized expectation (SAGE) algorithm was utilized to estimate angular domain parameters and powers of multipath components (MPCs), which are then used to compute the circular angular spread (CAS) and to construct the power azimuth spectrum (PAS) of transmitter and receiver sides. It is found that PASs of both sides follow multi-cluster truncated Laplacian distribution. The spatial correlation, capacity and multiplexing gain are also presented. Due to rich scatters in the indoor hotspot environment, the correlation space is Q.2lambda and the multiplexing gain is 15, which indicate indoor hotspot MIMO channels facilitate multiplexing and could achieve high capacity. The results provide important basis for future channel modeling and technique evaluation.

In this paper, we propose a novel spatial autocorrelation model of the shadow fading process in urban macro environments. The proposed model is based on the empirical results obtained from extensive wideband radio channel measurement campaigns at 2.35 GHz in an urban area of a typical medium-sized Chinese city. The shadow fading component was extracted assuming a single-slope log-distance path loss model. The consistency with the level crossing theory of Gaussian processes is achieved by an implicit constraint on the parameters of the model. The proposed model gives a better fit to the empirical results in individual measurement routes than the widely reported exponential and double exponential models. An heuristic explanation of the proposed autocorrelation property is also presented.

The wideband channel measurements at 580 MHz, 2.35 GHz and 4.90 GHz have been performed in the urban micro- and macrocell scenarios in the cities of China with multiple-input multiple-output (MIMO) channel sounder. The measured cases include line-of-sight (LOS) and non-line-of-sight (NLOS) propagation. Statistical results and comparative analysis for both scenarios are presented in this paper, including path loss (PL), root mean square (rms) delay spread (DS) and maximum excess delay (maxED), and angular spread (AS). In NLOS case, the frequency dependent factor (FDF) is observed as 32.1 by fitting the PL model of 2.35 GHz and 4.90 GHz. Moreover, a larger rms DS in urban microcell and AS at both base station (BS) and mobile subscriber (MS) are found for the denser and higher buildings in the cities of China. As the frequency varying from 580 MHz to 4.90 GHz, the median rms DS is decreased from 330 ns to 130 ns for NLOS case.

This paper presents a timing synchronization method with shift-orthogonal constant amplitude zero auto correlation (CAZAC) sequences for the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Utilizing the unique properties of CAZAC sequences, at the receivers, unit pulses can be obtained by the symmetrical correlation to detect time offsets and differentiate inter-transmitter delays (ITDs). The performance of the proposed method is compared with traditional methods at different values of ITDs. Simulations demonstrate that the proposed CAZAC sequence-based method provides high accuracy in detecting the different time offsets caused by the distributed transmitters of the MIMO OFDM systems.

Wideband channel measurements have been performed at 5.25 GHz in hotspot areas in Beijing with a multiple- input multiple-output (MIMO) channel sounder. Based on the measured data in the indoor line-of-sight (LOS) and outdoor LOS/obstructed-line-of-sight (OLOS) scenarios, the frequency- dependent eigenvalues and average eigenvalue statistics are presented in this paper. In the indoor scenario, the probability density functions (PDFS) of the maximum and minimum eigenvalues are well fitted using the normal distribution. In the outdoor scenario, the log-normal distribution is proposed to model the pdfs of these two eigenvalues. As the indicator of spatial selectivity, the empirical cumulative distribution functions (CDFS) of condition number are obtained in two scenarios, indicating that the spatial multiplexing technique is more suitable in indoor LOS scenario than in the outdoor LOS/OLOS scenario. Additionally, the three-dimensional (3D) directional spread is calculated to illustrate the spatial dispersion of the multipath components. These information are of significance for the design and performance evaluation of MIMO communication systems employed in hotspot areas.

The performance of a multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system can be improved when the channel state information (CSI) is available for beamforming at the transmitter. However, even with the quantization of the CSI, the feedback requirement is still prohibitively unaffordable in a frequency division duplexing based MIMO-OFDM system with a large number of subcarri- ers. In this paper, two schemes with limited feedback, i.e., a nonlinear interpolation and a modified clustering based transmit beamforming schemes, are proposed. In the first method, the beamforming vectors for the non-pilot subcarriers in each cluster are constructed via that of the pilot subcarriers through nonlinear interpolation. In the second method, the beamforming vector for each cluster is selected to optimize the performance of a group of subcarriers which are chosen from that cluster based on equal spacing or a predefined random matrix. Simulations demonstrate that the proposed nonlinear interpolation and the modified clustering based transmit beamforming schemes outperform the existing limited feedback methods with the same feedback.

In this paper, the sum rate issue of a multiple-input multiple-output (MIMO) relay broadcast channel is studied, where the fixed relay station is used to support multiuser transmission in a downlink channel. With appropriate design, the sum rate can be expressed as a function of the processing units at the base station (BS) and relay, and the power allocation can be converted to a standard convex problem. We compared the sum rate of MIMO relay broadcast channel (BC) using time division multiple access (TDMA) to the strongest user, dirty paper coding (DPC) and beamforming (BF) when multiple antennas are deployed at each terminal in the system and separate power constraints at the BS and relay. Then, the sum rate bounds for each strategy and the sum capacity (achieved by DPC) gain over TDMA and BF are given, correspondingly. Finally, numerical results are presented to prove our discussions. With these results, we can easily obtain how far away TDMA and BF are from being optimal in terms of sum rate, and direct the practical system design.

In orthogonal frequency division multiplexing (OFDM) systems, time- and frequency-selective (or doubly-selective) fading leads to the loss of subcarrier orthogonality and the occurrence of inter-carrier interference (ICI), which increases an irreducible error floor in proportional to the normalized Doppler frequency offset. Channel estimation (CE) in rapidly time-varying multi-path scenarios is critical for ICI cancellation and coherent demodulation. In this paper, we introduce an iterative CE scheme to estimate time-varying channel parameters and a low-complexity equalization method to cancel ICI and detect data. The proposed CE technique performs an initial CE based on a piece-wise linear model. Then ICI is reconstructed and cancelled from the received signals. Estimating channel again by using signals of less interference, refined CE can be obtained. Meanwhile, a low-complexity equalizer is proposed to further improve BER performance. Finally, simulation results show good performance of the proposed CE method in doubly-selective fading channels.

In orthogonal frequency division multiplexing (OFDM) systems, some subcarriers at the borders of the allocated bandwidth are not used for transmission as virtual subcarriers. Because of these subcarriers, some conventional channel estimators are not applicable, especially for multiple input multiple output (MIMO) systems. In this paper, our analysis on the effect of virtual subcarriers is briefly presented based on discrete Fourier transform (DFT) estimator in the delay domain. Then, a novel channel estimation approach, which can recover the dispersive distortion of estimated channel impulse response (CIR) caused by virtual subcarriers, is proposed for MIMO-OFDM systems. The proposed channel estimation does not need to know the path delay and is insensitive to the delay tap number. And it can easy be realized. The simulations demonstrate the effectiveness of the proposed approach in both narrow band and wide band mobile wireless channels.

In this paper, a generic framework for validating wideband MIMO channel models based on channel measurement results is proposed. The framework is formulated as a series of continuous functions (metrics) and a definition of distance of continuous function space (degree of approximation). The metrics characterize the MIMO channel from different perspectives, and the distance provides a quantitative measure of the degree of approximation for the specified model. Several fundamental metrics which reflect the spatial multiplexing gain, diversity capability, time and frequency variability, are derived for exploring the frequency-selective fading property. Based on an extensive measurement campaign at 5.25 GHz, the propagation channel is reconstructed by a WINNER-like model. The metrics are calculated from both the model generated channel realizations and the measured impulse response as a demonstration. The proposed framework can be applied to compare different channel models and to evaluate the simplified version of channel models.

In this paper, we propose an accurate and efficient timing synchronization method for distributed multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system in both AWGN and multi-path Rayleigh fading channels. A modified cyclic delay synchronization pattern (MCDSP) is proposed and it is verified to be especially suitable for distributed system which needs to separate the signal arriving time of each transmit antenna. At the receiver, two additional methods termed antenna cluster separation window (ACSW) and multi-path backward searching (MPBS) are employed to combat the multi-path effect.

A joint timing synchronization and channel estimation algorithm is proposed for orthogonal frequency division multiplexing (OFDM) system. In the proposed scheme, the shift delay characteristic of synchronization sequence is revealed in channel estimation process. Through utilizing this characteristic, the correct symbol timing offset (STO) is jointly optimized with channel estimation via the minimum mean square error (MMSE) criterion. Simulation results demonstrate that the proposed scheme could bring almost ideal performance improvement for both channel and timing offset estimation.

Priority scheduling has been widely used in mobile ad hoc networks. However, most of the prior work related to priority scheduling is designed only for a single data channel. In addition, without providing certain mechanisms to incorporate priority scheduling, existing multi-channel MAC protocols can not provide differentiated service. In this paper, we propose a multi-channel priority MAC protocol which can significantly increase the throughput of high priority flows and reduce their average delay. Our protocol consists of four mechanisms: control phase contention mechanism, priority- oriented channel assignment mechanism, data phase contention mechanism and flow interpolation mechanism. Simulation results demonstrate the effectiveness of the proposed protocol.

Wideband channel measurements have been performed at 5.25 GHz in hotspot areas in Beijing with a multiple-input multiple-output channel sounder. The measured scenarios include indoor line-of-sight (LOS) and non-line-of-sight, and outdoor LOS and obstructed-line-of-sight. Statistical results of channel characteristics are presented which include the path loss models, root mean square delay spread, circular azimuth spread and average envelope correlation with respect to antenna separation. Comparative analysis of the results are provided. These information are of significance for the design and performance evaluation of international mobile telecommunications (IMT)-advanced systems.

In this paper, the statistical analysis of cluster is presented based on the outdoor wideband multiple-input multiple-output (MIMO) channel measurements to facilitate IMT-Advanced system design. Clusters are found in multi-dimensional space, i.e., on the azimuth of arrival-azimuth of departure-elevation of departure-delay domain. We identify clusters with an automatic cluster identification algorithm and a new method is developed to improve the algorithm in terms of convergence rate and accuracy, which can also be used in calculating the angular spread (AS) to avoid the ambiguity caused by the origin of the coordinate system. All cluster parameters are described by a set of probability density functions (pdfs) derived from the measured data. The cluster numbers are well fitted with Poisson distribution plus a minimum number of clusters, and both the delay spread and angular spread exhibit the Lognormal distribution. It is concluded that the elevation angle should not be neglected when terminals are rounded by rich scatterers in outdoor scenario. We also find that clusters with smaller delays have in general a higher cross-polarization discrimination (XPD) than that with larger delays.

Radio frequency allocation is of great importance in the multihop cellular network, because some extra resources (frequency or time slot) seem to be allocated to the relay station. Generally, the tradeoff must be made between the frequency reuse factor and the inter-cell interferences during establishing frequency allocation scheme. A "pre-configured and fixed (PreF)" frequency allocation scheme has been proposed in [1]. In this paper, the concept of "soft frequency reuse (SFR)" in [2] is first borrowed, and applied to the multihop cellular network. After that, the improvement is made to the SFR scheme, and a more efficient "modified SFR (MSFR)" frequency allocation scheme is proposed. Through the simulations, it is demonstrated that, almost the same cell throughput can be achieved in the PreF and SFR schemes for the uniform traffic distribution, and the MSFR scheme can provide nearly twice cell throughput as large as that in the aforementioned two schemes. Moreover, the SFR and MSFR schemes both outperform the PreF scheme for the non-uniform traffic distribution because of the support to dynamic frequency resource allocation in the proposed schemes.

Wideband Multiple-Input Multiple-Output (MIMO) channel measurements were preformed in outdoor-indoor scenario for peer-to-peer system at 5.25 GHz with 100 MHz bandwidth. Based on the measured data, a new power delay profile (PDP) model is proposed which is applicable regardless of the first path is the strongest or not. Time dispersion parameters and empirical model are presented. The RMS delay spread (RDS) is log-Gumbel distributed and the path number well obeys the Gao's distribution. The relationship between RDS and mean excess delay is also studied. Finally, the propagation pathways are reconstructed under the assumption of two-bounce scattering according to the geography of the measurement site and the spatio-temporal information provided by a high resolution algorithm.

Based on 5.25 GHz wideband channel measurements performed in indoor office environment, empirical path loss models in in-room line-of-sight (LoS), room-corridor, and room-room non-line-of-sight (NLoS) propagation conditions are developed for future wireless radio systems. One-slope and dual-slope log-distance models are adopted in in-room LoS and room-corridor conditions, respectively. In room-room NLoS condition, we propose the enhanced attenuation factor models: AF-extended and AF-linear models to further explore the effect of medium walls, heavy walls and doors. This work offers valuable propagation measurements in a frequency range that is being considered allocating to IMT-advanced systems.

Synchronization is a challenging task in ultra wideband (UWB) communications, and becomes more difficult in UWB based wireless sensor networks (WSNs) in the presence of multi-user interference (MUI). For such a system, we develop a synchronization scheme including a novel design of transmitted reference (TR) signal model to avoid MUI and an energy-efficient synchronization algorithm. The synchronization signal of cluster-head is designed to be TR symbols with normal reference pulse and the interfering signal of common neighbour nodes is designed to be TR symbols with alternant anti-polar reference pulse, thus the MUI-free operation is obtained. Furthermore, the synchronization algorithm employs sample mean and energy detection relying on the periodicity in the mean of synchronization signal. Theoretic analysis and simulative results prove that the proposed scheme is both reliable and scalable

This paper presents a comparative study of two receiver schemes for interleaved OFDMA uplink. In the first scheme, called the post-DFT processing based interference cancellation receiver, the CFOs are compensated in frequency domain and an iterative interference-cancellation scheme is used to reduce the inter carrier interference (ICI) and/or multiuser interference (MUI). The other scheme, the signal structure-based MMSE receiver, exploits the signal inner structure on the interleaved OFDMA uplink and separates single user signal waveform by compensates the effective carrier frequency offsets. Simulation results show that the signal structure-based MMSE receiver is robust to large CFOs but its computation complexity increases considerably with the number of users. Furthermore, it assumes to know the signal power. When the CFOs are small, the post-DFT processing based interference cancellation receiver is preferable because its computation is easy with the number of users and it needs not know signal power

In this paper, we propose a zero correlation zone (ZCZ) sequences-based carrier frequency offset estimation algorithm for interleaved OFDMA uplink. By introducing the effective carrier frequency offset (CFO) and designing an appropriate training sequence for each user, we propose a training sequence-based CFO estimation algorithm thus avoiding the computationally complex subspace-based method proposed by Cao & Tureli (2002). Zero correlation zone sequence set is used as training sequence to mitigate multipath inference and multiuser inference. Simulation results show that the proposed algorithm is effective in multi-path fading channel

In this paper we propose a multipath delay estimation scheme for MIMO-OFDM system with the code division preamble. The algorithm is composed of three steps: the delays of L1stronger paths are firstly estimated by coarse multipath delay estimation module. Then the channel coefficients of L1paths are estimated and fed back to interference reconstruction module, by which the interference from other antennas will be recovered. After interference cancellation, fine multipath delay estimation module is used to get the delays of L paths and accurate estimation of delays could be taken advantage by channel estimation module. It is verified by simulation that the proposed algorithm can correctly estimate all paths for 4 antennas once SNR is higher than 6 dB. As antenna number is increased to 16, the correct estimation probability of former 4 paths is still about 90% when SNR is higher than 3 dB. So the proposed multipath delay estimation scheme with interference cancellation can efficiently improve the accuracy of multipath delay estimation in MIMO-OFDM system.

In multiuser MIMO system, multiuser multiplexing and diversity gain can be achieved by spatial scheduling. However, the limited battery life and terminal size of the User Equipment (UE) in a cellular system put a constraint on MIMO when the tranmitter has more antenna than the receiver in downlink, the conventional MIMO schemes can only exploit partial multiplexing gain and diversity gain. In a multiuser MIMO OFDMA system, multiuser diversity gain can be achieved by exploiting the independent frequency and spatial selective fading one another by joint spatial and frequency scheduling. In this paper, different multiuser multiple access schemes with greedy scheduling of MIMO OFDMA are investigated, e.g. TDMA, FDMA/TDMA and SDMA/FDMA/TDMA. For full spatial-frequency multiuser diversity gain and spatial multiplexing gain can be achieved, SDMA/FDMA/TDMA obtains highest spectrum efficiency. Its gain exceeds that of TDMA and FDMA/TDMA at least 80% and 40% respectively. The more antennas configured, the more gain can be observed from SDMA/FDMA/TDMA.