In this paper, the covert performance of the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided Terahertz (THz) communication systems with rate-splitting multiple access (RSMA) over the generalized α-μ fading channel is studied, where the discrete phase error and noise power uncertainty are considered. By means of the Gaussian approximation and Gamma distribution, the closed-form outage probability (OP) of the legitimate users is firstly derived. Then, the covert performance of the system is analyzed, and average detection error probability (ADEP) is deduced for the warden, and resultant closed-form ADEP is obtained. By minimizing the ADEP, the optimal detection threshold is derived with closed-form expression. With these results, subject to the constraints of covertness and reliability, the power allocation (PA) coefficients are optimized to minimize the OP of the covert user. Two adaptive PA schemes based on two-dimensional (2D) and one-dimensional (1D) search methods are respectively proposed to achieve the solutions, and resulting lower OP is attained. Simulation results indicate that the theoretical OP and ADEP can agree the corresponding simulations well, and randomizing the noise power of warden can improve the covert performance. Moreover, the warden with the optimized threshold can obtain lower ADEP than that with a fixed threshold, and the system with two PA schemes has a lower OP than that with a fixed PA, especially the scheme based on 1D search has lower complexity while maintaining the superior performance.

Unmanned aerial vehicles (UAVs) have become essential platforms for 3D object detection. In contrast to DETR-style 2D detectors, the Euclidean distance between 3D predictions and the ground truth does not serve as an accurate proxy for sampling error due to complex projection geometry. To overcome this limitation, we propose the perpendicular distance from a predicted point to the ray connecting the camera optical center and the ground-truth, which serves as a precise geometric measure of sampling difficulty. Although this ray distance provides an ideal sampling reference, the isotropic circularity resulting from equal ray distances constrains the approach's ability to capture object shape and orientation. We therefore introduce an anisotropy-aware elliptical mapping that reformulates the circular projection into an ellipse aligned with the object's geometry. By integrating the ellipse-corrected ray distances into both the bipartite matching cost and the regression loss, we develop RayDETR-a geometry-consistent, multi-view 3D detection framework. Experiments on the UAV3D dataset demonstrate that RayDETR achieves state-of-the-art performance without compromising inference speed.

In order to improve the performance of two-dimensional (2D) direction of arrival (DOA) estimation, especially in multi-source and low signal-to-noise ratio (SNR) scenarios, this letter proposes an enhanced V -shaped coprime array (EVCA) geometry, which employs two semi-symmetric coprime subarrays as the two arms of the array. The proposed geometry enables a more flexible array layout and extends the array aperture, thereby enhancing estimation accuracy and resolution. The proposed EVCA can resolve a greater number of signal sources compared to the traditional V -shaped coprime array (VCA), effectively addressing the underdetermined scenario in which the number of sensors is insufficient relative to the number of sources. Moreover, the effectiveness of the theoretical analysis is validated through 2D DOA estimation simulations using the one-dimensional root-MUSIC algorithm. Numerical results demonstrate the clear superiority of the EVCA over the classical VCA structure in terms of resolution accuracy.

Terahertz (THz) communication has received much attention recently for its large bandwidth availability, high data-rate transmission and alleviating the spectrum shortage, and it can meet the requirements of Internet of Things with large system capacity and networking capability. In this paper, the performance of multi-antenna THz communication systems with rate-splitting multiple access (RSMA) under the hardware impairments and imperfect successive interference cancelation (SIC) are investigated, where the THz channel is modeled as a composite fading channel including the molecular absorption effects, misalignment fading and small-scale α-μ fading. Taking the hardware impairments and imperfect SIC into account, the probability density function and cumulative distribution function of the effective channel gain are derived. A joint zero-forcing and maximum ratio transmission beamforming design is employed to eliminate the interference among devices. Then, with the performance analysis, the closed-form outage probability (OP) and diversity gain of the system are respectively deduced. By minimizing the OP, a closed-form power allocation (PA) scheme is proposed to adjust the PA coefficients between the common stream and private streams, and resultant lower OP is attained. Moreover, the closed-form expression of the ergodic sum rate (ESR) is derived by means of Fox-H function and the Meijer-G function. With this ESR expression, the asymptotic ESR at high signal to noise ratio (SNR) is also provided to gain further insights. Furthermore, a simple upper bound of the ESR is derived for performance evaluation based on the Jensen’s inequality. Simulation results show that the theoretical analysis is effective, and the proposed PA scheme can obtain lower OP. Besides, the impact of different system and fading parameters on the performance are also analyzed.

In this paper, the joint power allocation (PA), passive beamforming (BF) and hybrid BF (HBF) including digital and analogue BFs are designed for an intelligent reflecting surface (IRS)-assisted millimeter-Wave (mmWave) communication system with imperfect channel state information (CSI) and multiple mobile users to optimize the weighted sum rate (WSR) and energy efficiency (EE). The achievable WSR and EE of the IRS-mmWave system are first derived based on imperfect cascaded CSI for performance optimization. Then, the non-convex constrained problem is formulated to maximize the WSR, where the PA, HBF, phase and amplitude of IRS elements are jointly optimized. Given PA and passive BF (PBF), closed-form suboptimal HBF is obtained for each iteration. Also, given HBF and PBF, using the block coordinate descent (BCD) methods, closed-form PA is derived. Moreover, the phase and amplitude of IRS elements are derived for PBF design during each iteration. With the obtained HBF, the digital and analogue BFs are also derived. Based on this, joint schemes of PA, HBF and PBF are developed. Besides, an efficient iterative algorithm based upon the alternating optimization (AO), weighted minimum mean-square error (WMMSE) and Dinkelbach methods are presented for EE maximization and the suboptimal solution is obtained. Correspondingly, the energy-efficient design for joint PA, HBF and PBF is provided. Simulation results verify the proposed solutions.

Oriented objects in images are typically embedded in complex backgrounds and exhibit arbitrary orientations. When using oriented bounding boxes (OBBs) to represent these objects, the periodicity of the angles and associated variations in side lengths lead to discontinuities in the angle loss. This paper fundamentally addresses this problem by proposing a trigonometric loss function in the complex plane. Moreover, a conformer RPN head is designed with convolution and multi-head self-attention, which can dynamically capture angular and classification information. The proposed loss function and conformer RPN head jointly generate high-quality oriented proposals. A category-aware dynamic label assignment based on predicted category feedback is proposed to address the limitations of solely relying on IoU for oriented proposal label assignment. This method makes negative sample selection more representative, ensuring consistency between classification and regression features. Experiments were conducted on five realistic oriented detection datasets, and the results demonstrate superior performance in oriented object detection with minimal parameter tuning and time costs. Specifically, mean average precision (mAP) scores of 82.02%, 71.99%, 69.87%, 46.45%, and 98.77% were achieved on the DOTA-v1.0, DOTA-v1.5, DIOR-R, STAR, and HRSC2016 datasets, respectively.

Efficient modulation classification remains a key challenge in contemporary communication systems research. In this work, we introduce a novel framework rooted in the principle of divide and conquer to address the complexities associated with jointly classifying linear and nonlinear modulation schemes. Our approach decomposes the classification task into multiple binary problems, effectively leveraging a hierarchical tree-based structure that integrates several low-parameterized CNNs. Simulation results validate the efficacy of our proposed method, demonstrating superior classification performance with a notable reduction in complexity compared to conventional single CNN-based approaches.

This work proposes novel approaches that jointly design user equipment (UE) association and power control (PC) in a downlink user-centric cell-free massive multiple-input multiple-output (CFmMIMO) network, where each UE is only served by a set of access points (APs) for reducing the fronthaul signalling and computational complexity. In order to maximize the sum spectral efficiency (SE) of the UEs, we formulate a mixed-integer nonconvex optimization problem under constraints on the per-AP transmit power, quality-of-service rate requirements, maximum fronthaul signalling load, and maximum number of UEs served by each AP. In order to efficiently solve the formulated problem, we propose two different schemes according to the different sizes of the CFmMIMO systems. For small-scale CFmMIMO systems, we present a successive convex approximation (SCA) method to obtain a stationary solution and also develop a learning-based method (JointCFNet) to reduce the computational complexity. For large-scale CFmMIMO systems, we propose a low-complexity suboptimal algorithm using accelerated projected gradient (APG) techniques. Numerical results show that our JointCFNet can yield similar performance and significantly decrease the run time compared with the SCA algorithm in small-scale systems. The presented APG approach is confirmed to run much faster than the SCA algorithm in large-scale systems while obtaining an SE performance close to that of the SCA approach. Moreover, the median sum SE of the APG method is up to about 2.8 fold higher than that of the heuristic baseline scheme.

In this paper, the energy consumption (EC) optimization of an aerial high altitude platform station (HAPS) aided mobile edge computing (MEC) network with non-orthogonal multiple access (NOMA) in the presence of imperfect successive interference cancellation is studied. Specifically, joint design schemes of the resource allocation (RA) and the two-dimensional (2D) horizontal position are proposed to minimize the sum EC subject to the different constraint conditions. In particular, we jointly optimize the receive beamforming (BF), the power allocation (PA), HAPS position, the local computation resource, the computation task offload coefficient, and the computation resource allocated for each user via the block coordinate descent method. Namely, given the other optimization parameters, we first optimize a 2D position of HAPS. Then, given the 2D position, by introducing the auxiliary variables, a joint design of BF, PA, offload coefficient and computation resource is solved by an efficient iteration algorithm based on the successive convex approximation method. Additionally, a suboptimal joint design scheme is also developed to lower the complexity. Simulation results show that the proposed two design schemes of the joint RA and position are effective in reducing the EC, and they have a lower EC when compared to benchmark schemes.

Cell-free massive multiple-input multiple-output (CF mMIMO) is known for its ability to provide ubiquitous connectivity. In this paper, we investigate the achievable spectral efficiency (SE) of a user-centric (UC) CF mMIMO system with both multi-antenna APs and users over joint-correlated Rayleigh fading channels. First, we provide a performance analytical framework for the system with the linear decorrelator and study the impact of hardware impairments (HIs) at transceivers on the uplink SE. Based on that, we discuss the local minimum mean-squared error (MMSE) and partial MMSE combining schemes and the partial large-scale fading decoding (LSFD) method from a scalable point of view. Besides, the exact closed-form SE expression is derived with maximum ratio combining (MRC). Then, we study the MMSE-based successive interference cancelation (MMSE-SIC) detector and give an approximate closed-form SE expression with MRC. In the simulations, we compare the linear decorrelator to the MMSE-SIC detector under different hardware-impaired scenarios. Numerical results correspond to the theoretical analyses and show that the impact of HIs can be mitigated by adding the number of receive antennas.

In Cell-Free Massive multi-input multi-output (CF mMIMO), it is challenging to assign regular pilots due to the pre-log pilot overhead on spectral efficiency (SE). This paper explores a superimposed-pilot-(SP)-assisted CF mMIMO system which avoids the separate pilot training duration by superimposing pilot symbols onto data symbols. We consider spatial-correlated Rician fading channels with and without random phase shifts, where linear minimum-mean-square-error (LMMSE) estimators are performed at each access point locally. Then, we derive the closed-form SE expressions with maximal-ratio (MR) combining. To fill the gap, we introduce novel expressions of MMSE combining vectors and compare their SE performance with approximate MMSE combining vectors. Next, a generic model is provided for the optimal large-scale fading decoding (LSFD), and we derive the closed-form suboptimal LSFD solutions with MR combining. A line-of-sight-based combining scheme is proposed based on the approximate analysis, where closed-form SE expressions are derived using MR and MMSE combining and corresponding optimal LSFD coefficients. Numerical results show that the combination of MMSE and optimal LSFD yields almost 200% enhancement over the combination of MR and simple centralized decoding in 95% likely per-user SE without phase shifts, and 111% enhancement when phase shifts exist.

Abstract Deep neural networks (DNN) based automatic modulation classification (AMC) has achieved high accuracy performance. However, DNNs are data‐hungry models, and training such a model requires a large volume of data. Insufficient training data will cause DNN models to experience overfitting and severe performance degradation. In practical AMC tasks, training the deep model with sufficient data is challenging due to the costly data collection. To this end, a novel probability density function (PDF) based data augmentation scheme and a method to determine the required minimum sampling size for data enlargement is proposed. Compared with the known image‐based augmentation scheme, the proposed waveform‐based PDF technique has low complexity and is easy to implement. Experimental results show that the required size of the training dataset is one order of magnitude smaller than the sufficient dataset in the additive white Gaussian noise channel, and effective recognition can be achieved using around 60% of the total examples under the Rayleigh channel. Moreover, the presented scheme can expand training data under frequency and phase offsets.

In this paper, the millimeter-wave (mmWave) communications and non-orthogonal multiple access (NOMA) are exploited for mobile edge computing (MEC) networks to improve the performance of task offloading. Aiming at improving the computation efficiency (CE) and ensuring the fairness among users, we study the CE optimization for mmWave-MEC with NOMA, where both the analog beamforming (ABF) and hybrid beamforming (HBF) architectures under the partial offloading mode are considered. First, according to the max-min fairness criterion, the CE optimization problem is formulated to jointly optimize the ABF at the base station and the local resource allocation of each user in mmWave-MEC with ABF. An efficient algorithm based on the penalized successive convex approximation is proposed to solve this non-convex problem. Then, the max-min CE optimization problem in mmWave-MEC with HBF is studied, where the joint design of the HBF at the BS and the local resource allocation of each user is carried out. By using the penalty function and the inexact block coordinate descent method, a feasible optimization algorithm is developed to tackle this challenging problem. Simulation results verify the convergence of the proposed algorithms and show that the proposed resource allocation schemes can improve the system CE effectively, and the mmWave-MEC with HBF scheme can obtain higher CE than that with ABF scheme. Besides, the NOMA scheme exhibits superior performance over the conventional orthogonal multiple access scheme in terms of CE.

Cell-free (CF) massive multi-input multi-output (mMIMO) is a potential key technology in future 6G wireless networks, which is in a position to service many users uniformly. In this paper, we aim to investigate the impact of multi-antenna access points (APs) and users on the channel estimation of CF mMIMO with two different linear minimum mean-squared error (LMMSE) estimators when low-quality hardware is applied. We firstly give the LMMSE estimator on multi-antenna users via vectorization operation with all radio frequency (RF) chains and the low-complexity LMMSE estimator with a single-RF chain, where the impact of transmit and receive spatial correlation, hardware impairment (HI) and different pilot assignment are considered. Then, we provide the comparison of the normalized MSE (NMSE) performance and computational complexity between the two schemes. Simulation results show that the single-RF scheme can significantly reduce the complexity as the number of antennas increases. Meanwhile, the loss of NMSE performance is relatively trivial, and the single-RF scheme may outperform the full-RF scheme with random pilot assignment and other schemes like element-wise MMSE and least-square estimators.

In this paper, the joint design of power allocation (PA), beamforming (BF) and positioning is studied for unmanned-aerial-vehicle (UAV) aided millimeter-Wave (UAV-mmWave) systems, with the objective of maximizing the energy efficiency (EE), under the constraints of maximum transmitting power, minimum data rate from the ground users and positioning range of the UAV. To address the above problem, we first obtain the positioning of the UAV, with the help of approximate beam pattern. Then, near-optimal BF and closed-form PA are derived given the obtained position, with the help of block coordinate descent method. To reduce the complexity, two suboptimal BF schemes with one-loop iteration and closed-form solutions are respectively derived. Furthermore, we propose the simplified algorithms for two special cases, i.e., only line-of-sight (LoS) path and Non-LoS (NLoS) path exist between the users and the UAV. Simulation results verify the effectiveness of the developed joint schemes and show the superior EE performance. Moreover, they can obtain almost the same performance as the existing benchmark schemes but with lower complexity.

In this paper, we present the computation-efficient resource allocation (RA) schemes for millimeter-wave mobile edge computing (mmWave-MEC) system with the aid of reconfigurable intelligent surface (RIS), which is used to assist the uplink communication from the users to the base station (BS). By means of the theoretical analysis, the achievable rate and computation efficiency (CE) are derived. Then, the optimization problem for the CE maximization under the constraints of the minimum rate, maximum power consumption and local CPU frequency is formulated, where the joint design of the hybrid beamforming at the BS and the passive beamforming at the RIS as well as the local resource allocation of each user is carried out. An effective iterative algorithm based on the penalized inexact block coordinate descent (BCD) method is proposed to obtain the computation-efficient RA scheme. Next, a low-complexity suboptimal RA scheme based on the BCD method is proposed, and corresponding algorithm is presented. Simulation results show that the proposed schemes are effective, and high CE can be attained. Moreover, the second scheme can achieve the CE performance close to the first scheme but with lower complexity. Besides, it is effective to deploy the RIS scheme in mmWave-MEC system, which can strike a balance between the CE and energy consumption when compared to the conventional relay schemes.

Most existing unmanned aerial vehicle (UAV) assisted wireless sensor network (WSN) studies usually assume that using a large SNR or a short distance can ensure good link quality. However, they don't consider the actual characteristics of UAV assisted WSN. In this paper, spatial characteristics of wireless links, link quality indicating capabilities of physical layer metrics and symmetry of uplink and downlink are analyzed by comparing the measured data of UAV assisted WSN and traditional WSN. The results show that link characteristics of UAV assisted WSN have changed significantly compared with those of traditional WSN. Firstly, connected region of the three traditional communication regions (connected region, transitional region, and disconnected region) is significantly compressed and even no longer exists, which makes that short distance can no longer guarantee reliable communication. Secondly, traditional physical layer metrics such as RSSI, SNR and LQI are no longer valid and even become deceptive, which may lead to wrong judgment of link quality. Finally, there is obvious overall asymmetry between uplink and downlink of UAV assisted WSN, and the quality of downlink is significantly higher than that of the uplink. The discovery of these new features would have great significance on guiding the design of UAV assisted WSN.

Energy efficiency (EE) optimization of joint power allocation (PA) and beamforming (BF) is studied for multiuser distributed multiple-input multiple-output (D-MIMO) systems with maximum ratio combining. Subject to power budget constraints, a non-convex constrained optimization problem is firstly formulated to maximize EE of single-user D-MIMO. We propose a near-optimal joint design scheme with the block coordinate descent method, which has an effective iterative algorithm to find optimal BF for fixed PA, and an iterative algorithm based on the Dinkelbach methods to find optimal PA for fixed BF. We also develop a low-complexity suboptimal joint scheme, which has closed-form BF and PA by maximizing an effective signal-to-noise ratio for fixed PA and a derived EE upper bound for fixed BF, respectively. It has lower complexity than the near-optimal scheme with slight performance loss. With these results, two joint design schemes are developed for multiuser D-MIMO. The first scheme can achieve the best performance, while the second one based on closed-form PA and BF has slight performance loss in comparing to that of the former, but with lower complexity. Simulation results verify the effectiveness of the developed schemes over some existing schemes.

Real-time and accurate packet reception rate estimation is crucial for wireless sensor networks. However, existing approaches usually rely on offline data collection and training, which limits their generality. Specifically, models fitted by the test data acquired under specific conditions cannot be used in all environments and for arbitrary packet sizes. In this paper, a simplification method for the theoretical bit error rate model of IEEE 802.15.4 2.4 GHz physical layer is proposed, which is about 18 to 38 times faster than the original one. Then, with the simplified model, a lightweight packet reception rate estimation approach is designed, which is self-adaptive to different environments and arbitrary packet sizes. With the proposed approach, offline data collection and training are no longer needed, which will reduce deployment cost effectively. Compared with state-of-the-art approaches, estimate error of the proposed one is reduced by 2.46%~74.97% in different environments, and by 2.46%~62.00% for different packet sizes.

A low-complexity likelihood methodology is proposed for automatic modulation classification of orthogonal space-time block code (STBC) based multiple-input multiple-output (MIMO) systems. We exploit the zero-forcing equalization technique to modify the typical average likelihood ratio test (ALRT) function. The proposed ALRT function has a low computational complexity compared to existing ALRT functions for MIMO systems classification. The proposed approach is analyzed for blind channel scenarios when the receiver has imperfect channel state information (CSI). Performance analysis is carried out for scenarios with different numbers of antennas. Alamouti-STBC systems with 2 × 2 and 2 × 1 and space-time transmit diversity with a 4 × 4 transmit and receive antenna configuration are considered to verify the proposed approach. Some popular modulation schemes are used as the modulation test pool. Monte-Carlo simulations are performed to evaluate the proposed methodology, using the probability of correct classification as the criterion. Simulation results show that the proposed approach has high classification accuracy at low signal-to-noise ratios and exhibits robust behavior against high CSI estimation error variance.

In the context of Automatic Modulation Classification (AMC), some recent works have utilized multiple features to train the neural network. With an ultimate aim to develop a systematic approach to select the most diverse and unique features, we propose and demonstrate a novel method to select the most diverse (2m) features from a larger feature set. Bhattacharyya distance metric for the dissimilarity between two probability distributions is utilized to select the features with the highest distance for all modulation pairs within a test pool. The proposed approach is analyzed for three different neural networks based classifiers, amidst AWGN and frequency-selective fading channels. A substantial reduction in computational complexity is achieved with an acceptable compromise on the classification performance.

Application of deep learning in the area of automatic modulation classification (AMC) is still evolving. An unsupervised sparse‐autoencoder‐based deep neural network (SAE‐DNN) is proposed to deal with the problem of AMC for much neglected frequency selective fading scenarios with Doppler shift. The authors propose a set of low complexity spectral and cumulant based features for training SAE‐DNN. The network is designed using forced dimensionality reduction and sparsity constraint to achieve a low complexity solution with improved ability to learn more refined and robust features from the input training data. A unique training method is presented in this study which incorporates a range of SNR values for the entire span of the training dataset, as compared to the conventional approach which only uses a single SNR value for all the training examples. A comprehensive performance analysis shows that the proposed method outperforms many conventional counterparts in the literature. Generalisation test verifies that network is feasible for all channel conditions. A robust classification behaviour is observed against phase‐frequency impairments and Doppler shift for frequency selective fading scenarios.

In this paper, we develop an optimal precoder and a suboptimal precoder for uplink distributed antenna systems with transmit correlation over Rician channels. The objective is to minimize a pairwise error probability (PEP) with constrained transmit power. The optimal scheme uses an efficient projection-based steepest descent algorithm to obtain the Gram matrix of the precoder, while the suboptimal scheme exploits the eigen-structure of the Gram matrix to obtain its unitary matrix (beamforming) and eigenvalues (power control). For a given beamforming (BF), an efficient power adaptation based on a fuzzy signal-to-noise ratio (SNR) is developed. The PEP analysis at low SNR is used to obtain a suboptimal BF. The derived BF and the fuzzy power adaptation have closed-form expressions that make the suboptimal scheme computationally efficient in comparison with existing methods. The BF can be re-optimized via the Riemannian steepest descent method to further reduce the PEP. Simulation results show that the proposed optimal scheme not only gives the performance the same as the existing optimal scheme when the latter can converge, but it also has lower computational complexity and faster convergence. Moreover, the suboptimal scheme can give a nearly optimal error rate.

The downlink capacity performance of distributed antenna systems (DAS) with antennas selection is investigated in a composite fading channel which takes path loss, Rayleigh fading and lognormal shadowing into account. According to the performance analysis, and using log-normal distribution approximation, the tightly approximate closed-form expressions of cumulative distribution function (CDF) and probability density function (PDF) of the effective SNR are obtained, respectively. Based on these results, the outage capacity performance of DAS is analyzed. By utilizing the Gaussian distribution approximation, a simple approximate outage capacity is derived. As a result, the closed-form expression is achieved. All these expressions will provide good theoretical performance evaluation for DAS. Simulation results show that the derived outage capacity is effective, and can match the corresponding simulation well.

Based on the asymptotic analysis in spatially correlated Rician fading channel, the precoders for space‐time block‐coded multiple‐input multiple‐output system at high and low signal‐to‐noise ratios (SNRs) are, respectively, designed to minimise the system bit error rate (BER). The precoding for high SNR provides closed‐form positive power allocation by using the Langrage optimisation method, and has the BER performance close to that of the optimal scheme, especially at high SNR. The Langrage multiplier does exist and is unique, and practical Newton's method is proposed to find its optimal value. Moreover, it has a closed‐form solution for some special cases. Thus, it avoids the iterative calculation of some existing precoding schemes. The precoding for low SNR has a closed‐form solution similar to onedirectional (1‐D) beamforming, and can obtain the performance very close to that of the optimal scheme at low SNR. By combining the above two schemes, a simple and effective suboptimal precoding scheme can be achieved for different SNRs. Simulation results show that the proposed scheme derived from the integration of high and low SNR can provide BER lower than the equal power precoding, and obtain the BER performance close to that of the existing optimal scheme with low complexity.

In this paper, unified BER performance and outage probability analyses of downlink multiuser scheduling for multiple-input multiple-output (MUS-MIMO) transmission/combining (transceiving) systems with imperfect channel state information (CSI) caused by feedback over Rayleigh fading channels is presented. The unified analysis is applicable to MIMO transceiving systems whose received signal-to-noise ratio (SNR) is given as largest order statistic of chi-square distribution. The performance of MUS-MIMO employing selective transmission/selective combining (ST/SC), ST/maximum ratio combining (MRC) and space-time block codes (STBC) can be analyzed by this general framework. Accurate and approximate unified closed-form BER and outage probability expressions of the MUS-MIMO transceiving system with normalized SNR-based scheduling in heterogeneous networks are derived. Based on the approximate BER and outage probability expressions, diversity gains are analyzed at high SNR. The results indicate that the MUS-MIMO for using the three transceiving schemes can achieve full diversity order for perfect CSI, while the diversity gain is degraded differently for the three transceiving schemes under imperfect CSI. Computer simulation shows that the theoretical analysis is in good agreement with simulation results, and the approximate BER expressions are very close to the accurate ones but with lower computational complexity.

This paper studies the system uplink outage probability in distributed antenna systems (DAS). Firstly, owing to the complexity of actual wireless environments, this paper establishes a composite channel model which takes path loss, lognormal shadowing and Rayleigh fading into account. Then, the probability density function (PDF) of the output signal-to-noise ratio (SNR) is derived. After that, an analytical expression of the uplink outage probability for the MS over a given position is obtained after employing the selective transmission (ST) scheme. Further, considering the distribution of MSs in the cell, a closed-form expression of the system outage probability is derived. Numerical results show that the closed-form expression of the system outage probability can provide sufficient precision for evaluating the outage performance of DAS.

This paper describes the use of orthogonal space- time block codes to overcome the performance and complexity difficulties associated with the use of an offset modulation with space-time trellis codes in multiple-input multiple-output communication systems. The orthogonal space-time block code is applied to offset QPSK modulated waveforms in the same way it would be applied to symbols. The procedure allows the receiver to orthogonalize the link. The main benefits of this orthogonalization are 1) the removal of the noise correlation at the input to the space-time trellis decoder and 2) the elimination of I/Q interference resulting from multi-antenna transmission. These benefits are achieved at the expense of data rate. The data rate reduction is a function of the rate of the orthogonal space-time block code.