In future integrated sensing and communication (ISAC)-based vehicular networks, infrastructures such as road-side units (RSUs) are expected to simultaneously support high-efficiency communication and ubiquitous sensing functionalities for multiple vehicles. However, this integration introduces a critical challenge in multi-objective optimization (MOO) to achieve shared sensing and communication precoding (SSCP). To address this challenge, we propose a multi-task learning (MTL) scheme to solve the MOO problem. Specifically, we design a Transformer-based hybrid data-knowledge-driven network (Transformer-HDKDN), which enhances communication learning efficiency by incorporating domain knowledge. Furthermore, we introduce a gradient and uncertainty weighting (GUW) algorithm to dynamically balance the loss functions of the communication and sensing subtasks. Simulation results demonstrate that the proposed MTL scheme outperforms the evaluated schemes in terms of SSCP performance.

The sixth generation (6G) wireless communication systems aim to obtain accurate channel state information (CSI) with higher frequencies and bandwidths, while accommodating a larger number of devices, placing high demands on the accuracy and time efficiency of CSI acquisition. However, existing CSI acquisition methods suffer from high pilot overhead and channel aging issues, and fail to achieve both accuracy and time efficiency simultaneously. To address these challenges, we propose a high-accuracy and low-complexity semi-deterministic channel model by combining the deterministic Ray Tracing (RT) model with the 3D Non-Stationary geometry-based stochastic models(GBSM). This model separates the computation of static and dynamic scenarios, deriving closed-form expressions for the channel impulse response (CIR). Furthermore, it innovatively models the mobility parameters of scatterers and integrates a Kalman filtering algorithm for high-precision trajectory prediction, enabling dynamic updation of CSI. In validation experiments, the modeled channel characteristics show good agreement with the measured ones, verifying the accuracy of the model. The results illustrate that the proposed method efficiently computes real-time CSI with high accuracy, achieves pilot-free channel prediction, and effectively mitigates the channel aging effect.

Integrated Sensing and Communication (ISAC) can support various applications, including target tracking, environmental sensing, motion detection and etc. In this paper, an indoor human posture detection method is proposed. First, the scattering points in the environment, including both the human body and other items, are located through ISAC sensing. Next, the environmental map is used as a prior knowledge and the point cloud of the human body will be separated. Then the human body point cloud will be used to detect the human posture through a neural network. Simulation results demonstrate that the proposed method can successfully reconstruct the indoor human body point cloud, and realize the human posture recognition with an accuracy of 95.7%. Additionally, the system can achieve an average detection speed of 48 FPS by the hardware platform used in this study.

The cooperation of unmanned aerial vehicles (UAVs) with ground users to form a distributed MIMO (D-MIMO) system is a promising approach to improve the quality of wireless service in communication hot-spots. However, the performance of the system is jointly constrained by the placement of UAV-base stations (UAV-BSs) and the backhaul signal processing capabilities of ground stations. To address the above issues, this paper proposes a cooperative framework for UAV-BS in communication hot-spots, which effectively enhances system performance by jointly considering access link optimization and ground station processing capacity constraints. Specifically, by dividing ground users into several groups and forming independent D-MIMO subsystems with corresponding UAV-BS clusters, the system capacity and connection probability are effectively improved. The joint optimization problem of user grouping and UAV-BS placement is modeled as a non-convex optimization problem, and an efficient solution combining heuristic search and expectation-maximization (EM) algorithm is proposed. Simulation results demonstrate that the proposed framework can significantly improve the communication performance of the system while satisfying backhaul processing capacity constraints.

Abstract The channel in massive multiple‐input multiple‐output systems is fast‐varying that the pilot signal needs to be sent frequently. To obtain timely channel state information with less pilot overheads, a symbol detection aided channel prediction scheme is proposed in this paper. Then, the prediction error lower bound of the proposed scheme within one interval of effective prediction is analysed. Besides, the approximate close‐form post‐processing signal to noise ratio is derived for zero‐forcing detector with imperfect channel predictions. Numerical simulations are implemented to verify the validity of theoretical analysis. The results show that the theoretical expressions have a close match with the real simulated performance under various simulation parameter settings. In addition, the frequency of transmitting the pilot signals can be significantly reduced when adopting this proposed method. Moreover, the application of the proposed scheme can be further expanded when combining it with channel coding, thereby greatly improving the spectrum efficiency of the system.

Smart cities have entered into a new era with the widespread commercialization of 5G. As a key supporting technique for smart cities, Internet of everything (IoE) puts forward higher requirements for real-time data and massive access. In this article, we propose a novel embedded cognitive radio (CR) enabled rate splitting multiple access (ECR_RSMA) solution, in which the secondary user (SU) can access more than one idle spectrum holes of the primary users (PUs) by spitting the data rate without causing additional interference to the PUs. To ensure the quality of service (QoS) of each user, a composite successive interference cancellation (SIC) decoding scheme is specially designed, and the channel prediction algorithm is employed to combat the channel aging effect. The effectiveness of the proposed ECR_RSMA is verified through computer simulations. Compared with the state-of-the-art MA schemes, such as CR_NOMA, the proposed solution can significantly improve the performance in terms of the spectrum efficiency as well as the outage probability.

In this paper, we investigate the frequency domain phase noise estimation in millimeter wave filter-bank multicarrier with offset quadrature amplitude (mmWave FBMC-OQAM) systems. In the frequency domain, the primary impairment introduced by phase noise is the common phase error (CPE), whose estimation performance is significantly affected by the inter-carrier interference (ICI) and inter-symbol interference (ISI) experienced by OQAM symbols. To address the above problem, we propose novel frequency domain phase noise estimation and pilot symbol design methods to mitigate the impact of ICI and ISI. Firstly, we quantify the interferences affecting each pilot symbol and design appropriate weights to mitigate the impact of ICI and ISI on the phase noise estimation. Then, through analysis, we observe that the ICI and ISI originate from the interaction between the imaginary intrinsic interferences and the phase noise terms. Accordingly, we propose a pilot symbol design method by eliminating the primary imaginary intrinsic interferences, thereby reducing the ICI and ISI. Furthermore, we analyze the mean squared error (MSE) lower bound and the computational complexity. Simulation results demonstrate that the proposed phase noise estimation method outperforms the traditional method and the proposed pilot structure further improves the accuracy of the phase noise estimation.

Reconfigurable intelligent surface (RIS) has emerged as a promising solution to unify the electromagnetic and wireless communication spheres, enabling freedom from the conventionally uncontrollable wireless environments. Most of the current research assumes that the RIS will be placed on a fixed site, and the main beam of the transmit signal will be aligned towards the geometric center of the RIS. Some studies investigate RIS rotation strategy to maximize the received signal power. However, in the practical situation, precise rotation is difficult to be implemented and the RIS might be placed in an im-proper position where the transmit beam can not be aligned to the RIS center. To realize more flexible RIS configuration, we design an off-center optimization method for the RIS system. Specifically, the transmit beam can be directed toward a non-central position of the RIS to maximize the received signal power. The theoretical solution is derived and validation experiments are carried out. A 256-element RIS prototype operating in the C-band is developed. The congruity between the measured and simulated results confirms the correctness of the theoretical analysis. It is validated that, by using the proposed off-center optimization method, significant improvement can be obtained, and at least 15% increase of the received signal power can be obtained under the experimental conditions.

Massive multiple input multiple output (mMIMO) is a promising technology for the next-generation wireless communication systems, yet accurate channel modeling is still a challenging problem. Recent field experiments revealed that, the channel is non-stationary in space, time and frequency (STF) domains. Meanwhile, it is also disclosed that, the channel changes in a smooth way. Namely, the channel should be consistent in space and time domains. This paper proposes a three-dimensional (3D) space-time consistency modeling method for the STF non-stationary mMIMO environments. Innovatively, a growth-curve based power attenuation factor (PAF) is introduced to evolve the birth and death (BD) process of the scattering clusters, so that the space-time consistency can be guaranteed. In validation experiments, the modeled channel characteristics are in good agreements with the measured ones, indicating that the proposed method is suitable for mMIMO channel modelling.

The smart grid is expected to be integrated with advanced communication and control technologies to enhance its efficiency and reliability, enabling bidirectional information and energy exchanges between power providers and consumers. Considering the substantial data generated by the smart grid, mobile-edge computing (MEC) is introduced to address the need for considerable computing capacity. Limited attention has been directed toward exploring the application of MEC in smart grid scenarios. In this article, we innovatively consider maximizing the sum of computing rate and weighted energy trading benefit for a scenario with the distributed energy resource aggregation, smart grid, and MEC. We formulate the considered utility maximization as a mixed integer nonlinear programming (MINLP) problem. In order to solve this problem, we break it down into two distinct subproblems: 1) the binary offloading decision problem and 2) the energy allocation problem. We propose a deep reinforcement learning (DRL)-based framework to determine the offloading decision and design an optimization algorithm for the energy allocation problem. The simulation results indicate that the proposed algorithm achieves approximately 98% of the traversal algorithm’s performance with only one-thousandth of its processing time.

In this work, we investigate the performance of reconfigurable intelligent surface (RIS) assisted near-field wideband system. By considering the large-scale effect of a high-dimensional RIS and frequency-selective channels, we derive an accurate array manifold of the RIS in the near-field from the scattering point of view. Subsequently, we conceive a near-optimal RIS phase design for a single-user scenario to alleviate the beam-squint effect of the wideband system. As for the multi-user case, we provide a virtual-subarray-based phase shift design, which mitigates the beam-squint effect as well as the mitigates deleterious effects of beam concentration. Numerical results show that the achievable data rate can be significantly improved by the proposed schemes compared to the benchmarks both in the single-user and multi-user cases. Explicitly, in the multi-user system, by leveraging the virtual-subarray-based phase design, the achievable sum-rate can be doubled compared to the conventional benchmarks.

Direction of arrival (DOA) estimation is widely used in many applications. Traditional DOA estimation generally adopts the second-order statistics and the uniform linear array (ULA) structure. However, the second-order statistics and the uniform array structure restrict the number of DOAs that can be accurately estimated. In addition, the performance of the second-order statistics-based methods is sensitive to noise. As the wireless environment is becoming increasingly complex with the advent of the 5G era, the propagation channel can be composed of numerous paths. When the number of paths exceeds the size of the antenna array, DOA estimation becomes an under-determined problem, which the second-order statistics-based traditional methods fail to deal with. In order to address the under-determined DOA estimation problem, this paper proposes a method based on higher-order statistics and non-uniform array structure. Higher-order statistics-based methods can not only expand the array aperture, but also suppress the additive Gaussian noise. However, if combined with a uniform array structure, the degrees of freedom of the expanded array is limited. Therefore, we further adopt a non-uniform array structure and optimize its structure. Consequently, the proposed method is capable of achieving$M^{2}$-level DOA estimation with an M-element array, while simultaneously providing good robustness to noise. For instance, using the proposed method, the DOAs of 20 incoming wave directions can be accurately estimated with a 6-antenna non-uniform array when the signal-to-noise ratio is as low as 0 dB.

As an extension of the Internet of things (IoT), Internet of vehicles (IoV) paradigm plays a crucial role in advancing the development of smart cities. IoV relies on vehicular communication, enabling real-time interactions between vehicles, roadside infrastructure, and pedestrians. The main goal of IoV is not only to enhance road safety services but also to support time-sensitive IoT applications. In this context, cellular vehicle-to-everything (C-V2X) communication emerges as a prominent technology for achieving IoV goals. However, C-V2X communication system faces great challenges due to spectrum scarcity and the requirement for low-latency communication in high mobility and dynamic channel conditions. To meet these challenges, cognitive radio (CR)-inspired nonorthogonal multiple access (NOMA) has emerged as a promising solution to enhance the system capacity and spectrum efficiency. In this paper, we propose a novel CR paradigm, which utilizes the spectrum holes in an embedded mode. Namely, the secondary users (SUs) are allowed to access the holes released by idle primary users (PUs) without degrading the performance of active PUs. In addition, considering the channel aging phenomenon, we perform channel prediction to reduce the performance degradation. An online learning-based scheme that enables real-time resource allocation within the embedded CR assisted NOMA framework is then designed. Simulation results demonstrate superior performance gain of the proposed scheme. When compared with the conventional NOMA, the assistance of CR brings around threefold capacity gain, and when compared with the random allocation scheme, the capacity is increased by 21%.

We consider the extra degree of freedom offered by the rotation of the reconfigurable intelligent surface (RIS) plane and investigate its potential in improving the performance of RIS-assisted wireless communication systems. By considering radiation pattern modeling at all involved nodes, we first derive the composite channel gain and present a closed-form upper bound for the system ergodic capacity over cascade Rician fading channels. Then, we reconstruct the composite channel gain by taking the rotations at the RIS plane, transmit antenna, and receive antenna into account, and extract the optimal rotation angles after investigating their impacts on the capacity. Moreover, we present a location-dependent expression of the ergodic capacity and investigate the RIS deployment strategy, i.e. the joint rotation adjustment and location selection. Finally, simulation results verify the accuracy of the theoretical analyses and deployment strategy. Although the RIS location has a big impact on the performance, our results showcase that the RIS rotation plays a more important role. In other words, we can obtain a considerable improvement by properly rotating the RIS rather than moving it over a wide area. For instance, we can achieve more than 200% performance improvement through rotating the RIS by 42.14°, while an 150% improvement is obtained by shifting the RIS over 400 meters.

Intelligent reflecting surface (IRS) has been considered as a potential solution to enhance the performance of wireless communication systems. So far, the relevant works are mainly under the assumption of continuous phase shift, which is impractical due to the complexity and hardware cost. Finite discrete phase shifts, on the other hand, will cause significant performance deterioration. To solve this problem, we propose a codebook-based phase adjustment scheme via time-coding modulation with finite resolution. Specifically, by leveraging proper time-coding sequence, almost-full phase coverage with low-bit resolution can be achieved. Then, considering the scenario of IRS assisted wireless power transfer system, we propose a phase matching algorithm to achieve accurate energy-beam alignment. It is shown by simulation results that the performance of our proposed scheme is very close to that of the optimal one which is obtained assuming the continuous phase shift.

In this paper, channel tracking in fast-varying MIMO environments is considered. Firstly, the high-dimension channel vector of each user is decomposed into a semi-static factor load matrix (FLM) and a low-dimension factor coefficient vector (FCV). Thereby, channel tracking is replaced by the infrequent FLM tracking and low-complexity FCV tracking. Secondly, channel variations are represented by local polynomial modeling, based on which a recursive least square (RLS) algorithm with optimal forgetting factor (FF) is proposed. It is verified that the proposed algorithm has higher accuracy, better tracking ability and lower computational complexity than the existing methods.

With the evolution of wireless networks, new techniques including massive multiple-input multiple- output (MIMO) and millimeter wave are adopted to satisfy the demands for diversified services. However, it has been verified by field tests that the traditional wide sense stationary assumption for wireless channel does not hold anymore. As a result, traditional channel state information (CSI) acquisition methods, especially the statistical CSI acquisition, cannot be applied straightforwardly in such a circumstance. In this paper, we propose a pre-processing method for channel sensing in the non-stationary environment. Specifically, the data sampled from channel training is treated as a channel image, where the statistical channel state is represented by gray-scale. Then the computer vision technique, specifically, the edge detection method, is used on the channel image to detect the homogeneous sub-regions. Within each sub-region, the channel is statistically stationary, and then the CSI can be obtained by existing methods. It is verified by simulation results that, the proposed method can help to improve the CSI acquisition accuracy in the non- stationary environment.

Smart city is coming into urban life with the development of information and communication technologies. As an integral part of the smart city, massive heterogeneous Internet of Things devices face enormous challenges in multiple accessing and signal processing. In this paper, we propose an innovative and flexible scheduling method under the channel field multiple access framework to meet these requirements. In particular, artificial intelligence (AI) technology is adopted by the proposed solution. It is proved that the proposed AI-based scheduling method can obtain better performance than the existing methods by low complexity, especially, such performance gain becomes more significant as the number of devices increases.

In this paper, we consider the uplink channel parameter estimation problem in the presence of pilot contamination for massive multiple-input-multiple-output (MIMO) systems. We propose a parallel factor (PARAFAC)-based estimation scheme, which exploits the low-rank property of massive MIMO channels caused by the finite scattering in a physical environment. Specifically, we first parameterize the channel in terms of three parameters, i.e., fading coefficients, directions of arrival (DOAs), and delays; thereby, the channel is characterized via three equivalent PARAFAC models. Then, the proposed PARAFAC-based scheme is developed, which jointly estimates these three channel parameters using an alternating least squares (ALS) algorithm. Therein, we certify the identifiability of the three channel parameters of the PARAFAC models to mitigate the pilot contamination and state the convergence of the ALS algorithm, which guarantees that the three channel parameters can be uniquely determined with the proposed scheme. Moreover, to further reduce the computational complexity, two advanced schemes are proposed by antenna selection and reducing the estimation frequency of DOAs and delays, respectively. Simulation results show that the proposed schemes can achieve both low computational complexities and close to optimal Cramer-Rao Bound performance.

Abstract In this paper, a massive multiple input multiple output downlink scenario is considered where the number of users varies in a large dynamic range. An adaptive joint precoding and pre‐equalization with reduced complexity is proposed. Specifically, the successive over‐relaxation method is employed in the pre‐equalization process to avoid the high‐dimensional channel matrix inversion, and a reduced‐length feedback filter is proposed to reduce the computational complexity of the precoding. Moreover, an adaptive transceiver structure is proposed to switch on/off the precoding process so that multiple users can be accommodated with the least cost of the computational complexity. Simulation results show that, compared with the traditional scheme, the proposed adaptive joint precoding and pre‐equalization can save about 90% of the computational complexity. Copyright © 2016 John Wiley & Sons, Ltd.

To realize high-speed communication, broadband transmission has become an indispensable technique in the next-generation wireless communication systems. Broadband channel is often characterized by the sparse multipath channel model, and significant taps are widely separated in time, and thereby, a large delay spread exists. Accurate channel state information is required for coherent detection. Traditionally, accurate channel estimation can be achieved by sampling the received signal with large delay spread by analog-to-digital converter ADC at Nyquist rate and then estimate all of channel taps. However, as the transmission bandwidth increases, the demands of the Nyquist sampling rate already exceed the capabilities of current ADC. In addition, the high-speed ADC is very expensive for ordinary wireless communication. In this paper, we present a novel receiver, which utilizes a sub-Nyquist ADC that samples at much lower rate than the Nyquist one. On the basis of the sampling scheme, we propose a compressive channel estimation method using Dantzig selector algorithm. By comparing with the traditional least square channel estimation, our proposed method not only achieves robust channel estimation but also reduces the cost because low-speed ADC is much cheaper than high-speed one. Computer simulations confirm the effectiveness of our proposed method. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, we address the resource allocation (RA) problem for uplink single input multiple output (SIMO) single carrier frequency division multiple access (SC-FDMA). A novel RA algorithm for joint user scheduling and resource block allocation is presented. Particularly, with the aim of improving the spectral efficiency (SE) and reducing the computational complexity, a sub-optimal power projection method based on users' spatial correlation is employed, which chooses the users for each RB. A parametric multiple access channel (MAC) model is used for modelling the receive antenna correlation at the base station. Our simulation results show that the proposed scheme improves the the SE considerably, while keeping the complexity at an acceptable level. We also investigate the variation of different system design parameters, i.e., RB size, number of receive antenna, etc, on the SIMO SC-FDMA uplink SE, to shed insight on the role of these parameters in system SE optimization.

ABSTRACT Single‐carrier frequency domain adaptive antenna array (SC‐FDAAA) has been proposed and proved in our previous study to be effective in suppressing multiple access interference in a severely frequency selective fading channel. In this paper, we studied the performance of SC‐FDAAA in a distributed antenna network (DAN). To make it clear whether the performance of SC‐FDAAA can benefit from the distributed nature of DAN or not, we made a comparison between DAN and the traditional cellular network, that is, central antenna network. The bit error rate distribution and the system capacity (both link capacity and cellular link capacity) are presented. It is shown that by using the SC‐FDAAA, cellular link capacity is maximized by using single frequency reuse. In addition, the capacity of SC‐FDAAA can also benefit from the distributed nature of DAN. Copyright © 2012 John Wiley & Sons, Ltd.

In single-carrier (SC) transmission with multiple receive-antennas, multi-user access can be realized by using interference suppression. In our previous study, SC frequency-domain adaptive antenna array (SC-FDAAA) transceiver was proposed to realize frequency domain interference suppression and it was proved that SC-FDAAA transceiver can accommodate up to the number-of-receive-antennas users. However, when the number of users is larger than the number of receiver antennas, the performance of SC-FDAAA receiver will be significantly degraded. In order to accommodate a larger number of users than the number of receive antennas, an SC-adaptive transceiver is proposed in this study based on non-orthogonal frequency allocation and frequency-domain interference suppression. The effectiveness of the proposed transceiver is shown by simulation results.

Frequency domain adaptive antenna array (FDAAA) was proposed in our previous study and was proved to be an effective method to suppress interference caused by frequency selective fading and multiple access interference (MAI) in single-carrier (SC) transmission. The performance of FDAAA receiver will be affected by the antenna placement parameters such as antenna separation and spread of angle-of-arrival (AOA) of resolvable paths. In this paper, we analyze the impact of antenna separation and AOA spread on the radiation pattern of FDAAA receiver and shows the effects of two important parameters: antenna separation and AOA spread.

Least mean square (LMS) based adaptive algorithms have been attracted much attention since their low computational complexity and robust recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods, e.g., zero-attracting LMS (ZA-LMS), reweighted zero-attracting LMS (RZA-LMS) and Lp- norm sparse LMS (LP-LMS), have also been proposed. To take full advantage of channel sparsity, in this paper, we propose several improved adaptive sparse channel estimation methods using Lp-norm normalized LMS (LP-NLMS) and L0-norm normalized LMS (L0-NLMS). Comparing with previous methods, effectiveness of the proposed methods is confirmed by computer simulations.

Accurate channel state information (CSI) is necessary for coherent detection in amplify and forward (AF) broadband cooperative communication systems. Based on the assumption of ordinary sparse channel, efficient sparse channel estimation methods have been investigated in our previous works. However, when the cooperative channel exhibits partial sparse structure rather than ordinary sparsity, our previous method cannot take advantage of the prior information. In this paper, we propose an improved channel estimation method with partial sparse constraint on cooperative channel. At first, we formulate channel estimation as a compressive sensing problem and utilize sparse decomposition theory. Secondly, the cooperative channel is reconstructed by LASSO with partial sparse constraint. Finally, numerical simulations are carried out to confirm the superiority of proposed methods over ordinary sparse channel estimation methods.

Coherent detection and demodulation at the receiver requires channel state information (CSI). We investigate channel estimation problem in sparse multipath non-orthogonal amplify-and- forward (NAF) cooperative networks. Traditional linear estimation methods can obtain lower bound at the cost of spectrum efficiency which is becoming more and more scarcity. In this paper, system model is described from sparse representation perspective. Based on the compressed sensing theory, we propose several compressed channel estimation methods to exploit sparsity of the cooperative channels. Simulation results confirm the superiority of proposed methods than LS-based linear estimation method.

In this paper, frequency-domain one-tap weight control for single-carrier multiple access with multiple antennas is considered. Adaptive antenna array (AAA) weight control, diversity combining (DC) weight control and least square (LS) weight control are studied. These three weight control algorithms belong to multiple-input multiple-output (MIMO) beam forming, MIMO diversity and MIMO multiplexing, respectively. The weight control algorithms will be evaluated and compared in terms of computational complexity as well as bit error rate (BER) performance in a frequency-selective Rayleigh fading channel.

In broadband DS-CDMA uplink transmission using frequency-domain equalization (FDE) without cyclic prefix (CP) insertion, the transmission performance degrades due to inter block interference (IBI) and circular property loss. In this paper, IBI cancellation and circular property restoration is proposed and the performance improvement is evaluated by computer simulation. It is shown that IBI cancellation & circular property restoration can improve the bit error rate (BER) performance in a frequency-selective Rayleigh fading channel. It is also shown that circular property restoration is more powerful than the IBI cancellation.

This paper studies the multi-user downlink transmit beamforming algorithms for the broadband single-carrier distributed antenna network (SC-DAN). The distributed antennas will be used as antenna arrays by using transmit beamforming. In this work, the beamforming weight is designed in order to avoid the interference cancellation from the mobile terminals. Two methods are adopted. One is to use the maximum ratio transmission (MRT) beamforming weight to maximize the desired signal's power at each mobile terminal. The other one is to use the null steering beamforming weight to minimize the interference power at each mobile terminal. The beamforming algorithms using the two methods are described and their performances are compared by computer simulations.

In this paper, a single-carrier frequency domain adaptive antenna array (FDAAA) is proposed for the uplink transmission in cellular systems. The FDAAA algorithm will deal with co-channel interference and exploit the frequency selectivity of the channel fading jointly by using adaptive antenna array in the frequency domain. The receiver of the base station (BS) needs to adjust the AAA weight to optimize the system performance. The conventional cellular structures using frequency reuse factors 1, 3, 4 and 7 are considered. The performance of the proposed algorithm is confirmed by simulations.

In the cellular MIMO systems, the system capacity can be increased by multi-user diversity (MUD). On the other hand, the system capacity can also be increased by effective frequency reuse factor allocation (FRF) algorithm. In this paper, a multi-user hybrid FRF algorithm is proposed. Both MUD and effective FRF allocation will be realized. As a result, the system capacity is increased greatly. The system capacities by using the proposed algorithm are theoretically analyzed and the effectiveness of the multi-user hybrid FRF algorithm is testified by numerical results.

In this paper, a frequency domain adaptive antenna array (FD-AAA) algorithm is proposed for the uplink detection in the broadband single-carrier multiple access system. By employing AAA in the frequency domain, the detector is able to suppress both the inter-chip interference (ICI) and the multiuser interference (MUI). Comparison between the proposed FD-AAA algorithm and the frequency domain receive diversity combining (FD-RDC) algorithm is made through simulation. It is shown that when there is no MUI, the bit error rate (BER) performances of the two algorithms are close to each other, i.e., the proposed algorithm can achieve the same frequency diversity gain and receive diversity gain as the FD-RDC algorithm does. However, with the existence of MUI, the proposed algorithm shows a great superiority over the FD-RDC algorithm.

This letter addresses the problem of symbol error probability (SEP) analysis for maximum likelihood (ML) detection in multiple-input multiple-output (MIMO) multiplexing systems. A new analytical method is presented based on the total probability theorem. The effects of imperfect channel estimation and power allocation scheme are investigated. The accuracy of the proposed method is demonstrated by Monte-Carlo simulations. It is shown that the analytical results match quite well with the simulation ones irrespective of the signal-to noise- ratio (SNR).

This paper investigates the effects of channel estimation errors on zero-forcing (ZF) vertical Bell laboratories layered space time (V-BLAST) detection. An analytical method is presented to derive the symbol error probability (SEP) of the signals detected at each stage. The effects of imperfect channel estimation on the SEP performance of V-BLAST detection are then studied. It is shown that ZF-VBLAST detection is very sensitive to the channel estimation errors under high signal to noise ratio (SNR). It is also shown that when optimal ordering is adopted, the effects of channel estimation errors are more significant on the latter detection stages.

This paper addresses the problem of performance analysis for maximum likelihood (ML) detection in two-input multiple-output multiplexing systems. A novel analytical method is presented to formulate the symbol error probability (SEP). Based on the total probability theory, the SEPs of the two transmitted signals are obtained in closed-form by solving the SEP equations. Both equal and unequal power allocations are investigated. The accuracy of the proposed method is verified by Monte-Carlo simulations. The proposed method can also be extended to systems with more than two inputs.

In this paper, an analytical approach to performance analysis of vertical Bell laboratories layered space time (V-BLAST) detection with optimal ordering for systems with two transmit antennas is presented. The post-detection signal-to-noise-ratio (SNR) at each stage is derived and the symbol error probabilities (SEP) of the signals are then given in closed-form. The analysis takes into account the effects of optimal ordering, imperfect channel estimation and error propagation, which were rarely considered in the literature due to the difficulties in evaluation. The accuracy of the analysis is demonstrated by Monte-Carlo simulations.