Affine frequency division multiplexing (AFDM) has been considered as a promising waveform to enable high-reliable connectivity in the internet of vehicles. However, accurate channel estimation is critical and challenging to achieve the expected performance of the AFDM systems in doubly-dispersive channels. In this paper, we propose a sparse Bayesian learning (SBL) framework for AFDM systems and develop a dynamic grid update strategy with two off-grid channel estimation methods, i.e., grid-refinement SBL (GR-SBL) and grid-evolution SBL (GE-SBL) estimators. Specifically, the GR-SBL employs a localized grid refinement method and dynamically updates grid for a high-precision estimation. The GE-SBL estimator approximates the off-grid components via first-order linear approximation and enables gradual grid evolution for estimation accuracy enhancement. Furthermore, we develop a distributed computing scheme to decompose the large-dimensional channel estimation model into multiple manageable small-dimensional sub-models for complexity reduction of GR-SBL and GE-SBL, denoted as distributed GR-SBL (D-GR-SBL) and distributed GE-SBL (D-GE-SBL) estimators, which also support parallel processing to reduce the computational latency. Finally, simulation results demonstrate that the proposed channel estimators outperform existing competitive schemes. The GR-SBL estimator achieves high-precision estimation with fine step sizes at the cost of high complexity, while the GE-SBL estimator provides a better trade-off between performance and complexity. The proposed D-GR-SBL and D-GE-SBL estimators effectively reduce complexity and maintain comparable performance to GR-SBL and GE-SBL estimators, respectively.

Orthogonal time frequency space (OTFS) modulation and massive multi-input multi-output (MIMO) are promising technologies for next generation wireless communication systems for their abilities to counteract the issue of high mobility with large Doppler spread and mitigate the channel path attenuation, respectively. The natural integration of massive MIMO with OTFS in millimeter-wave systems can improve communication data rate and enhance the spectral efficiency. However, when transmitting wideband signals with large-scale arrays, the beam squint effect may occur, causing discrepancies in beam directions across subcarriers in multi-carrier systems. Moreover, the high-mobility wideband millimeter wave communications can induce the Doppler squint effect, leading to different Doppler shifts among the subcarriers. Both beam squint effect and Doppler squint effect (denoted as doubly squint effect) can degrade communication performance significantly. In this paper, we present an efficient channel estimation and hybrid precoding scheme to address the doubly squint effect in massive MIMO-OTFS systems. We first characterize the wideband channel model and the input-output relationship for massive MIMO-OTFS transmission considering doubly squint effect. We then mathematically derive the impact of channel parameters on chirp pilots under the doubly squint effect. Additionally, we develop a peak-index-based channel estimation scheme. By leveraging the results from channel estimation, we propose a hybrid precoding method to mitigate the doubly squint effect in downlink transmission scenarios. Finally, simulation results validate the effectiveness of our proposed scheme and show its superiority over the existing schemes.

Reconfigurable intelligent surface (RIS) is a key potential technology for the sixth generation wireless communication. The deployment of RIS in wideband communication systems can effectively mitigate severe path loss and against the blockage of line-of-sight path, which can improve transmission gain and enhance communication quality. However, with the increase of RIS array and bandwidth, beam-squint effect will occur and seriously damage the performance of communication systems. In this paper, we first establish a holographic RIS-aided wideband communication system model from the perspective of the electromagnetic wave propagation theory. Then, we analyze the holographic RIS electromagnetic characteristics under the beam-squint effect. Further, we derive the angle spread range, 3dB beam bandwidth, and beam coverage range to analyze the regularities of beam offset. Besides, we propose a new codebook design scheme to address the impact of the beam-squint effect. Finally, we introduce the true-time-delay (TTD) lines into the holographic RIS structure to mitigate the beam-squint effect. The simulation results reveal the influence of the beam-squint, and also verify the mitigation effect of TTD lines on the beam-squint effect.

Reconfigurable intelligent surface (RIS) and integrated sensing and communication (ISAC) are considered promising technologies for the sixth generation (6G) wireless communication. The deployment of RIS within the mmWave ISAC system can achieve better communication performance and sensing accuracy. The mmWave band signals can be utilized to enhance transmission rates and available bandwidth significantly. However, the increased size of the RIS array and bandwidth introduces the beam-squint effect, which impacts the performance of RIS-aided communication and sensing. In this paper, we analyze the beam-squint and beam-split effects on a uniform planar array of RIS. Moreover, we derive controllable beam-squint and beam-split ranges based on true-time-delay (TTD) lines and propose RIS-aided sensing schemes with beam-squint and beam-split for a mmWave ISAC system. The proposed schemes can utilize both time-domain and frequency-domain resources for beam scanning, which reduces the time overhead compared to traditional beam scanning schemes. Simulation results illustrate the effectiveness of the proposed RIS-aided user sensing schemes.

As mobile devices become increasingly popular and the need for indoor localization services grows, the localization of indoor mobile users is becoming more and more popular. However, the instability of received signal strength in the actual environment will have a detrimental influence on indoor localization, and the large multi-story buildings will also create new challenges. In this paper, we put forward a localization model with the graph-based location mapping network. The connection mode of the access points is used to construct a graph describing the location of reference points and users. Also, the graph neural networks are used to extract graph-level representation. This model can effectively capture the misaligned features. Moreover, the proposed approach is assessed on two public datasets, i.e., UJIIndoorLoc and UTSIndoorLoc, and the performance is evaluated against several leading-edge methods. Experimental results demonstrate that the proposed model outperforms existing solutions.

With the popularity of mobile devices and the increasing demand for indoor localization services, the localization of indoor mobile users is becoming more and more popular. However, many existing methods of building the radio map require collecting the received signal strength (RSS) of a large number of access points (APs), which causes high-hardware costs and large storage. Additionally, the instability of RSS in the actual environment will have a detrimental influence on indoor localization, and the large multistory buildings will also create new challenges. In this article, we propose a localization model with the combination of the AP selection network and the graph-based location mapping network. This model selects the optimal APs through the AP selection network and reduces the number of required APs. Then, the connection mode of the selected APs is used to construct a graph describing the location of reference points and users. Besides, the graph neural networks are used to extract graph-level representation, effectively capturing the misaligned features. Moreover, evaluated on the UJIIndoorLoc and UTSIndoorLoc data sets, the proposed method could not only reduce the number of required APs while ensuring localization performance but also outperform several state-of-the-art methods.

Realizing the wireless environmental sensing is another desired function of reconfigurable intelligent surface (RIS), in addition to enhancing the performance of wireless communication systems. In this paper, we design a holographic RIS-aided computational imaging system, which consists of a transmitter, a holographic RIS, a rectangular target and a receiver. Here, the target is composed of a series of discrete segments, each of which possesses a constant scattering density. The sensing task of the proposed system is to estimate the scattering densities of the target, which corresponds to the termcomputational imaging. The termholographicmeans that the RIS is modeled as a physically continuous surface with a physically continuous phase shift pattern, which can be approximately considered as as having massive (possibly infinite) number of elements within a finite space. Both the RIS and the target are subject to the electromagnetic boundary conditions, whose scattered fields are computed by the equivalent current method and the physical equivalent. Based on the computed scattered fields of the target, we derive the pathloss of the proposed system. In order to perform the imaging, we alter the phase shift pattern of the RIS such that the main energy of its scattered fields is focused towards different segments of the target successively, which then produces multiple measurements of the scattering densities and simultaneously ensures a low pathloss. After all measurements are completed, the scattering densities of the target can be estimated with the observed measurement vector and the reconstructed sensing channel, i.e., the computational imaging is accomplished. Simulation results show that the proposed imaging strategy performs well if the system parameters are designed properly.

Integrated sensing and communication (ISAC) has become a promising technology for future communication system. In this paper, we consider a millimeter wave system over high mobility scenario, and propose a novel simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) aided ISAC scheme. To improve the communication service of the in-vehicle user equipment (UE) and simultaneously track and sense the vehicle with the help of nearby roadside units (RSUs), a STAR-RIS is equipped on the outside surface of the vehicle. Firstly, an efficient transmission structure for the ISAC scheme is developed, where a number of training sequences with orthogonal precoders and combiners are respectively utilized at BS and RSUs for channel parameter extraction. Then, the near-field static channel model between the STAR-RIS and in-vehicle UE as well as the far-field time-frequency selective BS-RIS-RSUs channel model are characterized. By utilizing the multidimensional orthogonal matching pursuit (MOMP) algorithm, the cascaded channel parameters (i.e., the delays, the Doppler frequency shifts, the angles of arrivals, and the angles of departure of the scattering paths) of the BS-RIS-RSUs links can be obtained at the RSUs. Thus, the vehicle localization and its velocity measurement can be acquired by jointly utilizing these extracted cascaded channel parameters of all RSUs. Note that the MOMP algorithm can be further utilized to extract the channel parameters of the BS-RIS-UE link for communication service. With the help of sensing results, the reflection and refraction phase shifts of the STAR-RIS are delicately designed, which can significantly improve the received signal strength for both the RSUs and the in-vehicle UE, and can finally enhance the sensing and communication performance. Moreover, the trade-off design for sensing and communication is proposed by optimizing the energy splitting factors of the STAR-RIS. Finally, simulation results are provided to validate the feasibility and effectiveness of our proposed STAR-RIS aided ISAC scheme.

Millimeter wave communications are sensitive to complex scattering environments (e.g., to the presence of obstacles), which can be mitigated by reconfigurable intelligent surfaces (RISs). Traditional nearly passive RISs lack the ability to perform signal processing operations, which makes channel estimation in RIS-aided communications more challenging. Hence, in this paper, we focus on a hybrid RIS architecture, which is equipped with a small number of active elements. These active elements can be connected with baseband processing units through radio frequency (RF) chains. We estimate the whole channel, including the channel between the base station (BS) and the RIS, that between the users and the RIS, and that between the BS and the users. The whole channel is acquired at the hybrid RIS through the transmission of several segments of training pilots. In order to decrease the hardware cost, the BS and the hybrid RIS are equipped with RF chains with low-resolution analog-to-digital converters (ADCs). Since the numbers of BS antennas and RIS elements are very large, the estimation of the full-space channels is not straightforward. To tackle this problem, we propose a channel extrapolation scheme based on a joint selection model. Specifically, we select a BS antenna subset and an RIS element subset to be connected to the RF chains and to estimate the partial-space channels related to these subsets. Then, a reference-based variational auto-encoder model is used to implement the extrapolation from the partial-space channels to the full-space channels. Besides, the optimal joint selection pattern is acquired through a selection network to improve the channel extrapolation performance. Moreover, to overcome the quantization error caused by the use of low-resolution ADCs, we propose a two-stage repair scheme for channel estimation. Simulation results are provided to demonstrate the effectiveness of the designed channel extrapolation scheme.

Integrated sensing and communication (ISAC) has been regarded as a key technology for 6G wireless communications, in which large-scale multiple input and multiple output (MIMO) array with higher and wider frequency bands will be adopted. However, recent studies show that the beam squint phenomenon can not be ignored in wideband MIMO system, which generally deteriorates the communications performance. In this paper, we find that with the aid of true-time-delay lines (TTDs), the range and trajectory of the beam squint in near-field communications systems can be freely controlled, and hence it is possible to reversely utilize the beam squint for user localization. We derive the trajectory equation fornear-field beam squint pointsand design a way to control such trajectory. With the proposed design, beamforming from different subcarriers would purposely point to different angles and different distances, such that users from different positions would receive the maximum power at different subcarriers. Hence, one can simply localize multiple users from the beam squint effect in frequency domain, and thus reduce the beam sweeping overhead as compared to the conventional time domain beam search based approach. Furthermore, we utilize the phase difference of the maximum power subcarriers received by the user at different frequencies in several times beam sweeping to obtain a more accurate distance estimation result, ultimately realizing high accuracy and low beam sweeping overhead user localization. Simulation results demonstrate the effectiveness of the proposed schemes.

Integrated sensing and communication (ISAC) has become a promising technology for future communication system. In this paper, we consider a millimeter wave system over high mobility scenario, and propose a novel simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) aided ISAC scheme. To improve the communication service of the in-vehicle user and simultaneously track and sense the vehicle with the help of nearby roadside units (RSUs), a STAR-RIS is equipped on the outside surface of the vehicle to transmit and reflect the signal from the base station (BS). Firstly, an efficient transmission structure for the ISAC scheme is designed. Then, the time-frequency selective BS-RIS-RSUs channel model are characterized. Based on the estimated cascaded channel parameters (i.e., the delays, the Doppler frequency shifts, the angles of arrivals, and the angles of departure of the scattering paths) of the BS-RIS-RSUs links, the vehicle localization and its velocity can be acquired. With the help of sensing results, the reflection and refraction phase shifts of the STAR-RIS are designed for performance enhancememt. Moreover, the trade-off design for sensing and communication is proposed by optimizing the energy splitting factors of the STAR-RIS. Finally, simulation results are provided to validate the feasibility and effectiveness of our proposed STAR-RIS aided ISAC scheme.

Reconfigurable intelligent surface (RIS) can improve the communications between a source and a destination. Recently, continuous aperture RIS is proved to have better communication performance than discrete aperture RIS and has received much attention. However, the conventional continuous aperture RIS is designed to convert the incoming planar waves into the outgoing planar waves, which is not the optimal reflecting scheme when the receiver is not a planar array and is located in the near field of the RIS. In this paper, we consider two types of receivers in the radiating near field of the RIS: (1) when the receiver is equipped with a uniform linear array (ULA), we design RIS coefficient to convert planar waves into cylindrical waves; (2) when the receiver is equipped with a single antenna, we design RIS coefficient to convert planar waves into spherical waves. We then propose the maximum likelihood (ML) method and the focal scanning (FS) method to sense the location of the receiver based on the analytic expression of the reflection coefficient, and derive the corresponding position error bound (PEB). Simulation results demonstrate that the proposed scheme can reduce energy leakage and thus enlarge the channel capacity compared to the conventional scheme. Moreover, the location of the receiver could be accurately sensed by the ML method with large computation complexity or be roughly sensed by the FS method with small computation complexity.

In this paper, we propose a novel computer vision-based approach to aid reconfigurable intelligent surface (RIS) for dynamic beam tracking and implement the corresponding prototype verification system. A camera is attached at the RIS to obtain the visual information about the surroundings, with which RIS identifies the incident beam direction and the desired reflected beam direction, and then adjusts the reflection coefficients according to the pre-designed codebooks. We build a 20-by-20 RIS running at 5.4 GHz and develop a high-speed control board to ensure the real-time refresh of the reflection coefficients. Meanwhile we implement an independent peer-to-peer communications system to simulate the physical link between the base station and the user equipment. The vision-aided RIS prototype system is tested in two mobile scenarios: RIS works in near-field conditions as a passive array antenna of the base station; RIS works in far-field conditions to assist the communication between the base station and the user equipment. The experimental results show that RIS can quickly adjust the reflection coefficients for dynamic beam tracking with the help of visual information.

Beam training is one of the kernel problems in Millimeter-Wave(mmWave) massive multiple-input multiple-output(MIMO) systems. The beam direction explicitly relies on user location and is implicitly related to channel state information(CSI). Based on this fact, we propose a deep neural network-based novel downlink beam prediction framework to reduce the beam training overhead while achieving higher reliability. Considering that the user location and CSI are two completely different types and dimensions of information, the proposed neural network adopts adjustable feature fusion learning(AFFL) to fuse the two kinds of information. To reduce the beam training overhead, only the user location and the CSI of a minimal number of antennas are taken as the network’s inputs. In addition, when fusing, the signal-to-noise ratio(SNR) is used to adaptively adjust the weights of the two inputs on beam prediction output. Finally, simulation results corroborate that the proposed AFFL-based framework can achieve superior performance and robustness than the strategy which solely uses CSI, especially under low SNR conditions.

For high mobility communication scenario, the recently emerged orthogonal time frequency space (OTFS) modulation introduces a new delay-Doppler domain signal space, and can provide better communication performance than traditional orthogonal frequency division multiplexing system. This article focuses on the joint channel estimation and data detection (JCEDD) for hybrid reconfigurable intelligent surface (HRIS) aided millimeter wave (mmWave) OTFS systems. Firstly, a new transmission structure is designed. Within the pilot durations of the designed structure, partial HRIS elements are alternatively activated. The time domain channel model is then exhibited. Secondly, the received signal model for both the HRIS over time domain and the base station over delay-Doppler domain are studied. Thirdly, by utilizing channel parameters acquired at the HRIS, an HRIS beamforming design strategy is proposed. For the OTFS transmission, we propose a JCEDD scheme over delay-Doppler domain. In this scheme, message passing (MP) algorithm is designed to simultaneously obtain the equivalent channel gain and the data symbols. On the other hand, the channel parameters, i.e., the Doppler shift, the channel sparsity, and the channel variance, are updated through expectation-maximization (EM) algorithm. By iteratively executing the MP and EM algorithm, both the channel and the unknown data symbols can be accurately acquired. Finally, simulation results are provided to validate the effectiveness of our proposed JCEDD scheme.

Reconfigurable intelligent surface (RIS) is a revolutionary technology and can be used to assist communication systems by adaptively manipulating the wireless environment. In this paper, we propose a novel cascaded channel estimation algorithm for the RIS-assisted multiple-input multiple-output orthogonal frequency division multiplexing systems. Inspired by the channel compression idea, we can obtain a sub-sampled channel by turning off a fraction of RIS elements and then extrapolate it to the complete one, by which the pilot overhead is greatly reduced. The problem of channel extrapolation is transformed into recovering the physical parameters of the cascaded channel from the partial channel observations and is then formulated by the sparse Bayesian learning (SBL) framework. In order to circumvent the curse of high dimensional matrices inversion in the vector-matrix system, we further introduce the tensor structure into the SBL framework. Specially, by leveraging of the channel sparsity over the angle domain and delay domain, we derive the virtual channel expression in tensor form and model a Kronecker-structured prior distribution for the virtual channels. The multi-domain sparse properties of the virtual channel tensor can be effectively captured by a group of low-dimensional hyper-parameters, and thus reduce the computational complexity. In addition, the Cramer-Rao lower bound is derived for the proposed channel extrapolation. Simulation results show the superior performance of the proposed scheme.

Earth observation systems play important roles in many critical applications. The rapid increase of the number of satellites and their sensing capability, however, makes it challenging to send the massive amount of observed data back to the Earth. One promising direction to enhance the earth observation throughput is to use inter-satellite communication. Towards this, we identify two key design factors: 1) the capability to support on-demand scheduling of inter-satellite communication; and 2) the capability to co-optimize the scheduling of observation and transmission missions. For both, rigorous study is needed to determine whether they provide sufficient throughput gain to justify their additional complexity. Our work formulates a generic earth observation and transmission problem to study the maximum network throughput under different settings. By succinctly modeling the different constraints using a generalized time-varying graph representation, we can efficiently find the optimal scheduling solutions. We conduct an extensive study, which shows that using 40 relay satellites from the “starlink” constellation can increase the throughput of 10 sensing satellites from the “Gaofen” constellation by more than 400%. In particular, on-demand scheduling under heavy load and co-optimization of observation/transmission under light but time-critical load can improve the throughput by more than 180% and 100%, respectively.

Non-orthogonal multiple access (NOMA) is an important technology for the forthcoming 5G and beyond. However, its privacy often suffers from adversarial eavesdropping, especially for the users with higher transmit power. In this paper, we propose a jamming-aided secure transmission scheme for cooperative NOMA networks with a full-duplex (FD) relay. In this scheme, two pairs of users perform secure transmission with the help of a decode-forward (DF) relay, which forwards information and generates artificial jamming to counteract eavesdropping. The precoding vectors are designed to zero-force the artificial jamming at legal receivers. Then, the channel statistics are calculated, based on which the expressions of secrecy outage probability (SOP) are derived. Simulation results show the accuracy of our analysis, and demonstrate that the proposed scheme can effectively reduce the SOP and improve the effective secrecy throughput via artificial jamming and FD relaying.

This article focuses on a new path division multiple access (PDMA) for both uplink (UL) and downlink (DL) massive multiple-input multiple-output network over a high mobility scenario, where the orthogonal time frequency space (OTFS) is adopted. First, the 3D UL channel model and the received signal model in the angle-delay-Doppler domain are studied. Secondly, the 3D-Newtonized orthogonal matching pursuit algorithm is utilized for the extraction of the UL channel parameters, including channel gains, directions of arrival, delays, and Doppler frequencies, over the antenna-time-frequency domain. Thirdly, we carefully analyze energy dispersion and power leakage of the 3D angle-delay-Doppler channels. Then, along UL, we design a path scheduling algorithm to properly assign angle-domain resources at user sides and to assure that the observation regions for different users do not overlap over the 3D cubic area, i.e., angle-delay-Doppler domain. After scheduling, different users can map their respective data to the scheduled delay-Doppler domain grids, and simultaneously send the data to base station (BS) without inter-user interference in the same OTFS block. Correspondingly, the signals at desired grids within the 3D resource space of BS are separately collected to implement the 3D channel estimation and maximal ratio combining-based data recovery over the angle-delay-Doppler domain. Then, we construct a low complexity beamforming scheme over the angle-delay-Doppler domain to achieve inter-user interference free DL communication. Simulation results are provided to demonstrate the validity of our proposed unified UL/DL PDMA scheme.

Reconfigurable intelligent surface (RIS) is a revolutionary technology for achieving high rate and large coverage in future wireless networks by smartly reflecting the signals with adjustable phase shifts. To design the reflection beamforming, accurate individual channel state information is required at the RIS, which is a challenge task due to the lack of signal processing ability in passive mode. In this paper, we add signal processing units for a few antennas at the RIS to partially acquire the channels and extrapolate them to the full channels, in which the active antenna selection is a key point but has not been addressed yet. We construct an active antenna selection network that utilizes the probabilistic sampling theory to select the optimal locations of these active antennas. With this active antenna selection network, we further design two deep learning-based schemes, i.e., the channel extrapolation scheme and the beam searching scheme. The former utilizes the selection network and a convolutional neural network to extrapolate the full channels from the partial channels, while the latter adopts a fully-connected neural network to achieve the direct mapping from the partial channels to the optimal beamforming vector with maximal transmission rate. Simulation results show that the proposed optimal antenna selection outperforms the trivial uniform antenna selection, and the performance of beam searching is more stable than that of channel extrapolation with fewer active antennas.

A critical bottleneck of massive multiple-input multiple-output (MIMO) system is the huge training overhead caused by downlink transmission, like channel estimation, downlink beamforming and covariance observation. In this paper, we propose to use the channel state information (CSI) of a small number of antennas to extrapolate the CSI of the other antennas and reduce the training overhead. Specifically, we design a deep neural network that we call an antenna domain extrapolation network (ADEN) that can exploit the correlation function among antennas. We then propose a deep learning (DL) based antenna selection network (ASN) that can select a limited antennas for optimizing the extrapolation, which is conventionally a type of combinatorial optimization and is difficult to solve. We trickly designed a constrained degradation algorithm to generate a differentiable approximation of the discrete antenna selection vector such that the back-propagation of the neural network can be guaranteed. Numerical results show that the proposed ADEN outperforms the traditional fully connected one, and the antenna selection scheme learned by ASN is much better than the trivially used uniform selection.

This paper focuses on a new path division multiple access (PDMA) for both uplink (UL) and downlink (DL) massive multiple-input multiple-output network over a high mobility scenario, where the orthogonal time frequency space (OTFS) is adopted. First, the 3D UL channel model and the received signal model in the angle-delay-Doppler domain are studied. Then, along UL, we design a path scheduling algorithm to properly assign angle-domain resources at user sides. Correspondingly, the signals at desired grids within the 3D resource space of the base station are separately collected to implement the 3D channel estimation and maximal ratio combining-based data detection. Then, we construct a low-complexity beamforming scheme over the angle-delay-Domain domain to achieve interuser interference free DL communication. Simulation results are provided to demonstrate the validity of our proposed unified UL/DL PDMA scheme.

In this paper, we examine the acquisition of uplink and downlink channel parameters for the massive multiple-input multiple-output (MIMO) networks in the high mobility scene. We firstly formulate the time domain massive MIMO signal model along the uplink and adopt the expectation maximization based variational Bayesian (EM-VB) framework to recover the uplink channel parameters including angle, delay, Doppler frequency, and channel gain for each physical scattering path. Then, we fully exploit the angle, delay and Doppler reciprocity between uplink and downlink and reconstruct angles, delays, and Doppler frequencies for the downlink massive channels at the base station. Various numerical examples are presented to confirm the validity and robustness of the proposed scheme.

In high-mobility massive multiple-input multiple-output (MIMO) systems, Doppler shifts compensation can be combined with beamforming network to effectively suppress the channel time variation. The key of the beamforming network lies in the optimization of the common configurable amplitudes and phases (CCAP) parameter. In this paper, we reveal more insights of this approach by conducting the in-depth analysis. First, we demonstrate that the CCAP parameter optimizes the beamforming network to reduce channel time variation and approximates in a semi-sinusoidal form. Then, a scaling law between the asymptotic Doppler spread and the number of antennas M is derived, revealing that the asymptotic Doppler spread decreases at a rate of 1/M. We further prove that the optimal CCAP parameter obtained from Jakes' channel model can be directly applied to more general cases, while the inverse proportionality between the resulting asymptotic Doppler spread and the number of antennas remains valid. Numerical results confirm the correctness of the theoretical analysis.

Although it is often used in the orthogonal frequency division multiplexing (OFDM) systems, application of massive multiple-input multiple-output (MIMO) over the orthogonal time frequency space (OTFS) modulation could suffer from enormous training overhead in high mobility scenarios. In this paper, we propose one uplink-aided high mobility downlink channel estimation scheme for the massive MIMO-OTFS networks. Specifically, we firstly formulate the time domain massive MIMO-OTFS signal model along the uplink and adopt the expectation maximization based variational Bayesian (EM-VB) framework to recover the uplink channel parameters including the angle, the delay, the Doppler frequency, and the channel gain for each physical scattering path. Correspondingly, with the help of the fast Bayesian inference, one low complex approach is constructed to overcome the bottleneck of the EM-VB. Then, we fully exploit the angle, delay and Doppler reciprocity between the uplink and the downlink and reconstruct the angles, the delays, and the Doppler frequencies for the downlink massive channels at the base station. Furthermore, we examine the downlink massive MIMO channel estimation over the delay-Doppler-angle domain. The channel dispersion of the OTFS over the delay-Doppler domain is carefully analyzed and is utilized to associate one given path with one specific delay-Doppler grid if different paths of any user have distinguished delay-Doppler signatures. Moreover, when all the paths of any user could be perfectly separated over the angle domain, we design the effective path scheduling algorithm to map different users' data into the orthogonal delay-Doppler-angle domain resource and achieve the parallel and low complex downlink 3D channel estimation. For the general case, we adopt the least square estimator with reduced dimension to capture the downlink delay-Doppler-angle channels. Various numerical examples are presented to confirm the validity and robustness of the proposed scheme.

The application of non-orthogonal multiple access (NOMA) in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems can enhance spectral efficiency. In this paper, we propose an angle-domain NOMA scheme over the multi-cell mmWave massive MIMO networks. This scheme is optimized through both user scheduling and precoders/decoders design to maximize the system sum rate, where the precoders are decomposed into outer and inner ones. We construct the outer precoders with the help of the users' spatial angle information, i.e., beam signatures, and propose two design strategies for both inner precoders and decoders, i.e., joint optimization of precoders/decoders (JOPD) and cooperative NOMA (C-NOMA). Specifically, in JOPD, the precoders/decoders are obtained through maximizing a nonconvex function subject to the users' quality-of-service (QoS) constraints, where an alternate optimization algorithm based on the constrained concave-convex procedure is proposed for its solutions. In C-NOMA, we adopt interference alignment to cooperatively serve the cell-edge users and achieve simplified yet effective precoders/decoders. Furthermore, we optimize C-NOMA through power allocation. Afterwards, user scheduling algorithms are proposed for both JOPD and C-NOMA. Extensive simulations verify that the proposed schemes exhibit improved performance in terms of both sum rate and users' QoS compared to that of existing mmWave NOMA schemes.

This paper proposes a Bayesian downlink channel estimation framework for time-varying massive MIMO networks. In particular, the quantization effects at the receiver are considered. In order to fully exploit the sparsity and time correlations of channels, we formulate the time-varying massive MIMO channel as the simultaneously sparse signal model. Then, we propose a sparse Bayesian learning (SBL) framework to estimate the model parameters of the sparse virtual channel. The expectation maximization (EM) algorithm is employed to reduce complexity. Specifically, the factor graph and the general approximate message passing (GAMP) algorithms are used to compute the desired posterior statistics in the expectation step, so that high-dimensional integrals over the marginal distributions can be avoided. The non-zero supporting vector of the virtual channel is then obtained from channel statistics by a k-means clustering algorithm. After that, the reduced dimensional GAMP-based scheme is designed to make the full use of the channel temporal correlation so as to enhance the virtual channel tracking accuracy. Finally, the efficacy of the proposed framework is demonstrated through simulations.

In this paper, we propose an angle-domain NOMA transmission scheme over multicell massive MIMO systems, where multiple users' signal can be superposed to be served by the same spatially angle-domain beams. Then, we carefully consider the performance degradation resulting from the severe inter-cell interference, and formulate an optimization problem in terms of jointly optimizing precoders and decoders (JOPD) to seek an optimal transmission policy with quality of service (QoS) requirements of both cell-edge users and cell-center users, which consequently is a maximization of a nonconvex function with nonconvex constraints. To solve this challenging problem, we invoke the constrained concave convex procedure (CCCP) method to optimize precoders with fixed decoders, while the decoders can be readily optimized with obtained precoders. Consequently, we propose an alternating optimization algorithm based on CCCP (AoCCCP) to jointly optimize precoders and decoders and then obtain a suboptimal solution of the prime problem. Simulation results verify that the proposed scheme exhibits significant performance gain in terms of sum rate as well as QoS guarantee.

In this paper, user selection and transceiver design are proposed to guarantee the secure transmission in a multiple-input multiple-output interference network with an eavesdropper. First, user selection is performed to select the most suitable user to transmit confidential information according to the topology and path loss in each time slot. Then, based on user selection, the transceivers are jointly designed to maximize the secrecy rate of the selected user while guaranteeing a minimum transmission rate for other users. Due to the non-convexity of the problem, an alternate iteration algorithm is proposed to obtain the optimal solution with the help of successive approximations. Finally, simulation results are presented to show the effectiveness and efficiency of the proposed schemes.

This paper proposes a novel scheme for learning the channel statistics of the time- varying massive MIMO network. In particular, the effects of the quantization at the receiver are considered. Firstly, we formulate the massive MIMO channel as a simultaneously time-varying sparse signal model through virtual channel representation (VCR) and first order auto regressive (AR) model. Then, we propose a sparse Bayesian learning (SBL) framework to learn the model parameters of the sparse virtual channel. To avoid the unacceptable complexity, we apply the expectation maximization (EM) algorithm to achieve the approximate solution. Specifically, the factor graph and the general approximate message propagation (GAMP)-based message passing algorithms are used to compute our wanted posterior statistics in the expectation step. After that, the non-zero supporting vector of virtual channel is obtained from channel statistics by a k-means clustering algorithm. Finally, we demonstrate the efficacy of the proposed schemes through simulations.

As the typical delay tolerant networks, the satellite networks possess the intermittent connections, the large-scale time delays and the time-varying topologies. Obviously, such features seriously affect the delivery of mission data with certain requirements on the traffic or latency. To decrease the delivery time, existing relay-based contact graph routing (CGR) method selects the earliest reachable path to forward mission data. However, this method cannot guarantee the transmission of a large amount data and wastes the rare contact opportunities. Therefore, in this paper, we focus on the transmission QoS problem of multiple missions over the satellite networks, and design a QoS support routing strategy to achieve multiple flow-maximizing paths with acceptable delivery delays. Especially, with the storage time aggregated graph (STAG), we construct an on-demand mission model to depict both the network dynamic characteristics and the different mission requirements, and then reduce the QoS support problem as a graph-based maximum flow problem. To solve this problem, one STAG-based multiple flow-maximizing routing scheme is proposed to ensure the mission QoS and match the rare network resources, which has low computation complexity. Finally, compared with CGR, simulation results demonstrate the proposed scheme can achieve a higher mission completion rate and resource utilization rate.

Communications in high-mobility environments have received a lot of attention recently. In this paper, fast time-varying channels for massive multiple-input multiple-output (MIMO) systems are addressed. We derive the exact channel power spectrum density (PSD) for the uplink from a high-speed railway (HSR) to a base station (BS) and propose to further reduce the channel time variation via beamforming network optimization. A large-scale uniform linear array (ULA) is equipped at the HSR to separate multiple Doppler shifts in the angle domain through high-resolution transmit beamforming. Each branch comprises a dominant Doppler shift, which can be compensated to suppress the channel time variation, and we derive the channel PSD and the Doppler spread to assess the residual channel time variation. Interestingly, the channel PSD can be exactly expressed as the product of a pattern function and a beam-distortion function. The former reflects the impact of array aperture and is the converted radiation pattern of ULA, while the latter depends on the configuration of the beamforming directions. Inspired by the PSD analysis, we introduce a common configurable amplitudes and phases (CCAP) parameter to optimize the beamforming network, by partly removing the constant modulus quantized phase constraints of matched filter (MF) beamformers. In this way, the residual Doppler shifts can be ulteriorly suppressed, further reducing the residual channel time variation. The optimal CCAP parameter minimizing the Doppler spread is derived in a closed form. Numerical results are provided to corroborate both the channel PSD analysis and the superiority of the beamforming network optimization technique.

To estimate time-varying MIMO channel at base station, traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to extremely high overhead in massive MIMO systems. To tackle this problem, we propose a novel scheme for DL channel tracking in this paper. First, by utilizing virtual channel representation (VCR), we develop a dynamic uplink (UL) massive MIMO channel model with the consideration of off-grid refinement. Then, a coordinate-wise expectation maximization (EM) algorithm is adopted for capturing model parameters, including the spatial signatures, time-correlation factors, off-grid bias, channel power, and noise power. By exploiting the UL/DL angle reciprocity, the spatial signatures, time-correlation factors and off-grid bias of the DL channel model can be reconstructed with the knowledge of UL. However, channel power and noise power are closely related with the carrier frequency, which cannot be perfectly inferred from the UL. Instead of discovering these two parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with partial prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme.

The low-rank property of the channel covariances can be adopted to reduce the overhead of the channel training in massive MIMO systems. In this paper, with the help of the virtual channel representation, we apply such property to both time-division duplex and frequency-division duplex systems, where the time-varying channel scenarios are considered. First, we formulate the dynamic massive MIMO channel as one sparse signal model. Then, an expectation maximization-based sparse Bayesian learning framework is developed to learn the model parameters of the sparse virtual channel. Specifically, the Kalman filter (KF) and the Rauch-Tung-Striebel smoother are utilized to track the model parameters of the uplink (UL) spatial sparse channel in the expectation step. During the maximization step, a fixed-point theorem-based algorithm and a low-complex searching method are constructed to recover the temporal varying characteristics and the spatial signatures, respectively. With the angle reciprocity, we recover the downlink (DL) model parameters from the UL ones. After that, the KF with the reduced dimension is adopt to fully exploit the channel temporal correlations to enhance the DL/UL virtual channel tracking accuracy. A monitoring scheme is also designed to detect the change of model parameters and trigger the relearning process. Finally, we demonstrate the efficacy of the proposed schemes through the numerical simulations.

In this paper, the sum rate of opportunistic interference alignment (OIA) is analyzed in multiple-input-multiple-output interfering broadcast channels. The alignment metric upon which users are scheduled is based on the chordal distance between certain interfering subspaces at each receiver, and the closed-form expressions for the rates of the scheduled users are derived. Furthermore, we show that for a system in which each user has$N$receive antennas and the$j$th base station transmits$d_{j}$data streams, where$\sum _{j=1}^{I}d_{j}={N}+1$and${N}\ge 2$, the rate for each user can be approximated by the mean of a Gumbel random variable. Further analysis reveals that if the number of users in cell$i$scales as$\rho ^{\alpha }$, where$\rho $is the normalized transmit power and$\alpha \in [{0,1}]$, then cell$i$can achieve$\alpha d_{i}$degrees of freedom. The simulation results confirm the validity of the theoretical analysis and the accuracy of the approximation. Thus, the sum rate analysis provided herein is an effective performance evaluation method for multi-cell OIA.

With the capability of bidirectional communications on a single frequency band, the full-duplex (FD) operation can potentially double the spectral efficiency in physical layer. In network layer, nevertheless, it may cause severe mutual interference to the system. In this paper, we exploit interference alignment (IA) to address the interference in small cell networks, where some of the base stations simultaneously serve both uplink and downlink users on the same frequency via FD. Under such scenario, we first derive the feasibility condition for IA from Bezout's theorem and find that IA can be feasible only if a certain size constraint of the network is satisfied. On this basis, we then propose two clustering methods, i.e., minimized spectrum consumption clustering (MSCC) and minimized interference leakage clustering (MILC), both of which can perfectly eliminate the intra-cluster interference with IA. The difference between them is that MSCC aims at minimizing the number of clusters through allocating orthogonal resource blocks (RBs) for each cluster to avert inter-cluster interference, while MILC tries to minimize the aggregated inter-cluster interference with all clusters sharing the same RB. Extensive simulations verify that MSCC can achieve higher system sum rate, but MILC works better in terms of spectral efficiency.

Small-cell network is a promising solution to high video traffic. However, with the increasing number of mobile devices, it cannot meet the requirements from all users. Thus, we propose a caching device-to-device (D2D) scheme for small-cell networks, in which caching placement and D2D establishment are combined. In this scheme, a limited cache is equipped at each user, and the popular files can be prefetched at the local cache during off-peak period. Thus, dense D2D connections can be established during peak time aided by these cached users. To do this, first, an optimal caching scheme is formulated according to the popularity to maximize the total offloading probability of the D2D system. Then, the sum rate of D2D links is analyzed in different signal-to-noise ratio (SNR) regions. Furthermore, three D2D-link scheduling schemes are proposed with the help of bipartite graph theory and Kuhn-Munkres algorithm for low, high and medium SNRs, respectively. Simulation results are presented to show the effectiveness of the proposed scheme.

Task planning plays a vital role in the application of the earth observation satellites (EOS) as it can effectively reduce the task execution delay and the system energy consumption. However, it is a classic NP-hard problem. In this paper, we propose a graph-based scheduling algorithm to match the to-be-observed tasks with the sensor resources for the multi-satellite multi-task scenario. Specially, we construct a collision avoidance clustering graph (CACG) to model the relationship between tasks and resources, where the concept of collision set is creatively constructed to characterize the conflict relationships among tasks. Through analyzing which nodes can be a clique in the graph, we can get the aggregated tasks. As such a CACG-based algorithm is proposed to achieve satellite task scheduling, where three criteria,i.e.,the N-priority criterion, the T-priority criterion and the collision avoidance rule, are proposed to enhance the percentage of the completed task and decrease the delays. Finally, we demonstrate the effectiveness of the proposed schemes through simulations.

In this paper, we investigate a spatially sparse code multiplexing (SCM) transmission scheme for the massive multiple-input multiple-output (MIMO) networks to enhance the access connectivity. We construct a non- orthogonal transmission policy over both power and angle domains to fully utilize the limited angle-domain degree of freedom (DoF). Firstly, the mapping structure in the angle domain and the detection method are presented. Then, we formulate an optimization problem to seek an optimal transmission policy for the proposed SCM framework, where both the design of the mapping matrix and the power allocation are concerned. To simplify the non-convex problem, we solve the problem with three steps. During the first step, we allocate different angle-domain beams for users to obtain a sparse mapping matrix; during the second step, the prime optimization is transformed as a convex power allocation problem. Finally, we pursue a suboptimal transmission strategy for the multiple clusters with iterative power allocation. Simulation results verify that the SCM scheme exhibits significant performance gain in terms of sum rate.

As a revolutionary wireless transmission strategy, interference alignment (IA) can improve the capacity of cell-edge users. However, the acquisition of the global channel state information for IA leads to unacceptable overhead in the massive MIMO systems. To tackle this problem, in this paper, we propose an IA and soft-space-reuse (IA-SSR)-based cooperative transmission scheme under the two-stage precoding framework. Specifically, the cell-center and the cell-edge users are separately treated to fully exploit the spatial degrees of freedoms. Then, the optimal power allocation policy is developed to maximize the sum-capacity of the network. Next, a low-cost channel estimator is designed for the proposed IA-SSR framework. Some practical issues in IA-SSR implementation are also discussed. Finally, plenty of numerical results are presented to show the efficiency of the proposed algorithm.

The multi-commodity flow problem plays an important role in network optimization, routing, and service scheduling. With the network partitioning and the intermittent connectivity, the commodity flows in delay tolerant networks (DTNs) are time-dependent, which is very different from that over the static networks. As an NP-hard problem, existing works can only obtain sub-optimal results on maximizing the multi-commodity flow of dynamic networks. To overcome these bottlenecks, in this paper, we propose a graph-based algorithm to solve the maximum two-commodity flow problem over the DTNs. Through analyzing the relationship between the two commodities, we propose a maximum two-commodity flow theorem to simplify the coupling two-commodity flow problem as the two single-commodity flow ones. Then, with the help of the storage time aggregated graph (STAG) (a DTN model with less memory), we construct a pair of flow graphs to describe the reduced two single-commodity flows (addition flow and subtraction flow), and design the corresponding flow calculation methods. Moreover, we design a STAG-based dynamic combined flow algorithm to maximize the two-commodity flow. Finally, the computational complexity of the proposed algorithm is analyzed, and its efficacy has also been demonstrated through an illustrative example and numerical simulations.

The low-rank property of the channel covariances can be adopted to reduce the overhead of the channel training in massive MIMO system. In this paper, we exploit such low-rank property through virtual channel representation (VCR) under the time-varying channel scenario. Firstly, we reformulate the dynamic massive MIMO channel as one sparse signal model through VCR. Then, an expectation maximization (EM) based sparse Bayesian learning (SBL) framework is developed to estimate the statistical parameters of the sparse virtual channel. Specifically, the Kalman filter (KF) and the Rauch-Tung-Striebel smoother (RTSS) are applied to track the posterior statistics of the angle domain sparse channel in the expectation step, while a fixed-point theorem based algorithm and a low-complexity searching algorithm are separately developed to recover the temporal varying characteristics and the spatial signatures in the maximization step. Finally, we demonstrate the efficacy of the proposed schemes through simulations.

As one typical delay tolerant network (DTN), the satellite networks possess the intermittent connections, the large-scale time delays and time- varying topologies. Such features seriously affect the delivery of data with certain constraints about the traffic or the latency. In this paper, we design one QoS support routing strategy for the satellite networks to achieve multiple flow-maximize paths with acceptable delivery delays. In order to satisfy the demands of different missions, the storage time aggregated graph (STAG) is utilized to construct an on-demand mission flow model with some temporal constraints. Moreover, the proposed flow model carefully depicts the relation between the time-varying topologies and the features of different missions. Specially, one flow-maximizing routing scheme with shortest path is proposed for matching the rare contacts, which has low computation complexity. Finally, we demonstrate the efficacy of the proposed schemes through simulations.

mmWave massive multiple-input multiple-output (MIMO) system has gained much attention for its considerable improvement in system throughput. However, the cost of the complex hardware, e.g., the radio frequency (RF) chains, hinders it from the practical deployment. In this paper, we propose an angle space channel tracking method for mmWave massive MIMO systems with limited RF chains (hybrid scheme). Specifically, the users can be scheduled according to their DOA information, i.e. angle division multiple access (ADMA). Besides, the channel information can be divided into direction of arrival (DOA) information and gain information respectively, where DOA can be tracked through unscented Kalman filter (UKF), while the gain information can be obtained from beam training and spatial rotation. Numerical results are provided to corroborate our studies.

The millimeter-wave (mm-wave) massive multiple-input multiple-output (MIMO) system has gained much attention for its considerable improvement in system throughput. However, the cost of complex hardware, e.g., radio frequency (RF) chains, hinders it from practical deployment. In this paper, we propose an angle domain hybrid precoding and channel tracking method by exploring the spatial features of the mm-wave massive MIMO channel. The number of the effective spatial beams, or equivalently the RF chains, is enormously decreased via the operation of spatial rotation. The users are then scheduled by the angle division multiple access scheme, which groups users according to their direction of arrivals (DOAs). Meanwhile, a channel tracking method is designed for the subsequent data transmission through a small number of pilot symbols. Specifically, the channel information is divided into the DOA information and the gain information, where the DOA information is tracked by a modified unscented Kalman filter and the gain information is estimated from beam training. Numerical results are provided to corroborate our studies.

This paper proposes a new channel estimation scheme for the multiuser massive multiple-input multiple-output (MIMO) systems in time-varying environment. We introduce a discrete Fourier transform (DFT) aided spatial-temporal basis expansion model (ST-BEM) to reduce the effective dimensions of uplink/downlink channels, such that training overhead and feedback cost could be greatly decreased. The newly proposed ST-BEM is suitable for both time division duplex (TDD) systems and frequency division duplex (FDD) systems thanks to the angle reciprocity, and can be efficiently deployed by fast Fourier transform (FFT). Various numerical results have corroborated the proposed studies.

This paper proposes a novel channel estimation scheme for the multiuser massive multiple-input multiple-output (MIMO) systems. A discrete Fourier transform (DFT) aided spatial basis expansion model (SBEM) is first introduced to represent the uplink (UL)/downlink (DL) channels with much few parameter dimensions by exploiting the physical characteristics of the uniform linear array (ULA). With SBEM, pilot contamination in the UL training can be relieved by user scheduling exploiting their spatial information. Moreover, the UL spatial information can help to simplify the DL training based on the angle reciprocity. Compared to existing low-rank models, the newly proposed SBEM does not need any information of channel statistics and is suitable for both time division duplex (TDD) and frequency division duplex (FDD) systems. Moreover, the proposed method can be efficiently deployed by the fast Fourier transform (FFT) followed by linear estimator. Various numerical results are provided to corroborate the proposed studies.

In this paper, we target at cognitively detecting the presence of the primary user (PU) as well as recognizing PU's signal modulation. Since the existing modulation classification methods rely on fixed sensing period which may waste time when the modulations are easier to distinguish, we propose an automatic modulation classification (AMC) approach using likelihood-based (LB) and feature- based (FB) sequential detection methods, where SU calculates the likelihood ratio (LLR) sequentially to determine whether or not to stop listening. Referring to asymptotic analysis of the sequential methods, we formulate an optimization problem and derive the minimum sensing time under a constrained misclassification rate. Simulation results demonstrate that both LB and FB methods could significantly reduce the sensing time compared to fixed sensing period method.

In this paper, we design transmit beamforming for a full-duplex base station (FD-BS) considering joint self-interference mitigation and physical-layer security enhancement. The proposed designs are formulated to minimize the power consumption of FD-BS, under different signal- to-interference-and-noise-ratio (SINR) constraints. We strictly prove the optimality of SDR by showing the existence of rank-one solutions. Simulation results are provided to demonstrate the efficiency of the proposed algorithms.

The cognitive radio technology allows secondary user (SU) to share the licensed spectrum by adapting its transmission power in a sensing-based spectrum sharing manner. Reliable spectrum prediction and channel selection could alleviate the processing delays and enhance the spectrum utilization. In this paper, we propose a new strategy for spectrum prediction and channel selection using online machine learning techniques, which consists of three stages: 1) SU utilizes online learning techniques for the regression of received transmit power on different licenced frequency bands; 2) SU predicts the probability of each primary user's status (busy/idle) based on the power regression results; 3) SU optimizes channel selection in terms of expected ergodic capacities from the prediction outcomes. The proposed strategy can not only save time and energy, but also enhance the throughput of SU. The performance of the proposed strategy is evaluated through extensive simulations.

In this paper, we examine the channel estimation in an amplify-and-forward (AF) one-way relay network (OWRN) under time selective flat fading scenario, where the distributed space-time coding (DSTC) is adopted at relay nodes. Different from most existing works, our target is to estimate and track the individual channels of each relay hop instead of the composite channels. To reduce the number of the channel parameters to be estimated, we apply the polynomial basis-expansion-model (P-BEM) and convert the problem to estimating the channel coefficient-vectors (called in-BEM-CVs) of each relay hop. With the aid of the autoregressive (AR) model, we formulate the dynamic state space for the in-BEM-CV estimation. Specifically, we adopt the unscented Kalman filter (UKF) to track the in-BEM-CV dynamic variations in an forward manner, and utilize the unscented Rauch-Tung-Striebel smoother (URTSS) to smooth the UKF's estimations in an backward manner. To make the study complete, we also derive Bayesian Cramér lower bounds (BCRBs) for the in-BEM-CV estimation. Finally, numerical results are provided to corroborate the proposed studies.

In this paper, we present a new tracking algorithm based on time-multiplexed-superimposed training (TMST) scheme for the individual channels in amplify-and-forward oneway relay network (OWRN) under time-varying flat fading scenario. Due to the large number of unknowns, we apply the the polynomial basis-expansion-model (P-BEM) to approximate the channel vector of each individual hop by a coefficient-vector with much smaller size, called in-BEM-CV here. Then tracking the individual channel is converted to tracking the corresponding in-BEM-CVs. With the aid of Jakes model, we developed an auto-regressive (AR) process for the in-BEM-CVs and derive the hidden Markov model (HMM) for the in-BEM-CV tracking problem. A particle filtering (PF)-based algorithm to dynamically track the in-BEM-CVs is then designed. Finally, numerical results are presented to evaluate the proposed algorithms.

In this paper, we examine the pilot-based channel estimation in an amplify-and-forward (AF) one-way relay network (OWRN) under time selective flat fading scenario. Different from most existing works, our target is to estimate and track the individual channel of each relay hop instead of the composite channel. To reduce the number of the channel parameters to be estimated, we apply the polynomial basis-expansion-model (P-BEM) and convert the problem to estimating the channel coefficient-vector (called in-BEM-CV) of each relay hop. With the aid of the autoregressive (AR) model, we formulate the dynamic state space for the pilot-based in-BEM-CV estimation. Specifically, we adopt the unscented Kaiman filter (UKF) to track the in-BEM-CV dynamic variations in an online manner, and utilize the unscented Rauch-Tung-Striebel smoother (URTSS) to smooth the UKF's estimations in an offline manner. Finally, numerical results are provided to corroborate the proposed studies.

In this paper, we investigate the interference mitigation in femtocell networks, where femtocell access points (FAPs) are allowed to cooperate as different cooperative groups to allocate resources. We model the femtocell cooperation characteristics as a coalition formation game in partition form with non-transferable utility. Furthermore, a distributed coalition formation algorithm is proposed to enable each FAP to decide to depart from or join in a coalition independently, moreover, we devise a low complex iterative algorithm to optimize the allocation of each coalition's multi-dimensional resources for maximizing its FAPs' payoffs. By applying our proposed coalition formation scheme, a Nash stable FAP partition is formed and FAPs in each coalition can effectively exploit the cooperative gain to mitigate the interference and maximize the sum rate. Numerical results are provided to corroborate our proposed studies.

In this paper, we investigate the individual channel estimation for three-node one-way relay network (OWRN), where both source and destination are equipped with multiple antennas. Without resorting to the composite channel estimation, as did in the traditional work, we directly estimate the individual channels from an iterative linear minimum mean-square-error (LMMSE) estimator. The closed-form least square (LS) channel estimator is also derived through matrix unitary diagonalization to provide a good initialization for the iterative LMMSE estimator. To make the work more complete, we present two performance lower bounds: Bayesian Cramér lower bound (BCRB) and linear estimation lower bound (LELB), for the proposed algorithm. Numerical results are provided to corroborate our proposed studies.

In this paper, we adopt the time-multiplexed-superimposed training and investigate channel estimation for amplify-and-forward (AF) two-way relay network (TWRN) under doubly-selective channel scenario. With the aid of the complex-exponential basis-expansion-model (CE-BEM), we first develop the estimation model for BEM coefficient-vectors (BEM-CVs) of the individual channels between both sources and the relay. A two-step coarse estimator is proposed to obtain the BEM-CVs of the individual channels. Finally, numerical results are provided to corroborate the above studies.

In this paper, we design a segment training based individual channel estimation (STICE) scheme in the classical three-node it amplify-and-forward (AF) one-way relay network (OWRN). The linear minimum mean-square-error (LMMSE) channel estimator is used to obtain a good initialization, and an iterative maximum a posteriori (MAP) channel estimator is developed to improve the estimation accuracy. We then investigate the underlying power allocation at the relay node both to minimize the mean-square-error (MSE) of the individual channel estimation and to maximize the average effective signal-to-noise ratio (AESNR) of the data detection. The closed-form Bayesian Cramér-Rao Bound (CRB) is also derived to evaluate the proposed algorithm. Finally, numerical results are provided to corroborate the proposed studies.

In this paper, we design a segment training based individual channel estimation scheme in the classical three-node amplify-and-forward one-way relay network (OWRN). We investigate the underlying power allocation at the relay to minimize the mean-square-error (MSE) of the individual channel estimation and to maximize the average effective signal-to-noise ratio (AESNR) of the data detection. The optimal/sub-optimal power allocation schemes are also derived for the two objectives. Extensive numerical results are provided to corroborate the proposed studies.