To realize integrated sensing and communication (ISAC), multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC systems have been widely discussed. However, current beamforming designs for multi-user MIMO-OFDM ISAC systems are mostly based on iterative optimization-based approaches, which commonly induce high runtime complexity. To resolve this issue, this paper first investigates the relevant beamforming design approach that is based on iterative weighted minimum mean square error (WMMSE) problem and successive convex approximation (SCA). Then, a deep unfolding learning-based beamforming design framework is proposed via unfolding the iterative WMMSESCA procedure into a neural network (NN)-based architecture. To efficiently train the proposed framework, a loss function for unsupervised learning is proposed. Also, approaches that can enhance the training efficiency are discussed. Simulation results demonstrate that our proposed approach can outperform existing methods in ISAC performance, while significantly reducing the computation time.

The multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) system has been intensively investigated in the past years, as it is a promising candidate for realizing ISAC in next-generation wireless systems. However, even though it is clear that OFDM-based systems could suffer from high peak-to-average power ratio (PAPR), the design of low-PAPR multi-user MIMI-OFDM ISAC systems has not been well-explored. To fill this gap, this paper investigates the low-PAPR multi-user MIMI-OFDM ISAC system design. Specifically, by using radar ambiguity function and symbol-level precoding concept, this paper first formulates a low-PAPR multi-user MIMI-OFDM ISAC system design problem that optimizes the sensing performance subject to communication performance and PAPR constraints. To solve the problem, the communication performance and PAPR constraints are first transformed to convex constraints, and then the successive convex approximation is used to derive an iterative solution approach. Simulation results show that our proposed design approach can provide the effective low-PAPR ISAC design that outperforms the reference scheme.

To address spectrum scarcity and achieve the perceptive network, integrated sensing and communication (ISAC) systems have been extensively studied. However, research on multi-user ISAC systems utilizing multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) architectures remains incomplete. This paper aims to bridge this gap by exploring digital and hybrid beamforming designs for multi-user MIMO-OFDM ISAC systems. Specifically, the paper investigates the impact of beamforming on the range-Doppler domain of radar sensing and system capacity, and formulates a corresponding design problem. The paper then addresses the capacity maximization problem by converting it into a weighted minimum mean square error problem and employs successive convex approximation (SCA) to develop an iterative approach for digital beamforming design. Similarly, the paper formulates a hybrid beamforming design problem and proposes a design approach based on the alternating direction method of multipliers (ADMM) and SCA. The complexity of the proposed approaches is analyzed. Simulation results demonstrate that our approaches are effective and can outperform existing approaches in the literature.

Acquiring downlink channel state information (CSI) at the base station is vital for optimizing performance in massive Multiple input multiple output (MIMO) Frequency-Division Duplexing (FDD) systems. While deep learning architectures have been successful in facilitating UE-side CSI feedback and gNB-side recovery, the undersampling issue prior to CSI feedback is often overlooked. This issue, which arises from low density pilot placement in current standards, results in significant aliasing effects in outdoor channels and consequently limits CSI recovery performance. The main objective of this work is to solve this issue by introducing a new CSI upsampling framework at the gNB as a post-processing solution to address the gaps caused by undersampling. Leveraging the physical principles of discrete Fourier transform shifting theorem and multipath reciprocity, our framework effectively uses uplink CSI to mitigate aliasing effects. We further develop a learning-based method that integrates the proposed algorithm with the Iterative Shrinkage-Thresholding Algorithm Net (ISTA-Net) architecture, enhancing our approach for non-uniform sampling recovery. Our numerical results show that both our rule-based and deep learning upsampling methods significantly outperform traditional interpolation techniques or multiple state-of-the-art approaches by 8-13 dB and 2-10 dB, respectively, in terms of normalized mean square error.

Total isotropic sensitivity (TIS) measurement is strongly required by the industry, but the procedure takes a long time. We explore a machine learning (ML) approach to speed up TIS test procedure. The experiments are conducted using 5G devices and frequency bands. The results show that our methodology can improve measurement efficiency by 35% to 65%, while still maintain high accuracy within 1 dB deviation from standard procedure. Disconnection is a critical issue during TIS measurement, so we design a calibration mechanism to reduce the risk of disconnection. Our approach can be applied widely to different system configurations. It supports not only 5G but also previous generations on different frequency bands.

The investigation of the joint radar and communication (RadCom) systems has drawn attention in recent years. However, the relevant investigation of the multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) based joint RadCom system is incomplete, especially for the robust beamforming design. To fill this gap, by first formulating a robust design problem that jointly considers the estimation of ranges, Doppler velocities, and angles of targets under uncertainty along with the system capacity, this paper studies the robust beamforming design for the MIMO-OFDM joint RadCom system. Then, since the design problem can be reformulated as a semidefinite programming with rank-1 constraint, we propose using the semidefinite relaxation to solve the problem. Simulation results show that our proposed design can outperform the reference methods in the literature.

To resolve the spectrum scarcity and realize the perceptive network, integrated sensing and communication (ISAC) systems have been widely studied. However, the study of the multi-user ISAC system based on the multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) architecture is far from complete. Thus, this paper aims to fill this gap by investigating the beamforming design for multi-user MIMO-OFDM ISAC systems. By studying the beamforming effect on the range-Doppler domain of the radar sensing and the system capacity, a design problem is formulated. Then, by converting the capacity maximization to the weighted minimum mean square error minimization along with the use of successive convex approximation, an iterative approach is proposed to solve the design problem. Simulation results show that our design approach can outperform the reference methods in the literature.

Acquiring downlink channel state information (CSI) is crucial for optimizing performance in massive Multiple Input Multiple Output (MIMO) systems operating under Frequency Division Duplexing (FDD). Most cellular wireless communication systems employ codebook-based precoder designs, which offer advantages such as simpler, more efficient feedback mechanisms and reduced feedback overhead. Common codebook-based approaches include Type II and eType II precoding methods defined in the 3GPP standards. Feedback in these systems is typically standardized per subband (SB), allowing user equipment (UE) to select the optimal precoder from the codebook for each SB, thereby reducing feedback overhead. However, this subband-level feedback resolution may not suffice for frequency-selective channels. This paper addresses this issue by introducing an uplink CSI-assisted precoder upsampling module deployed at the gNodeB. This module upsamples SB-level precoders to resource block (RB)-level precoders, acting as a plug-in compatible with existing gNodeB or base stations.

In recent years, the integration of sensing and communication has gained significant attention. In this context, the adoption of the multiple-input multiple-output (MIMO)orthogonal frequency division multiplexing (OFDM) dual-functional radar and communication (DFRC) system is promising. However, comprehensive exploration of the MIMO-OFDM DFRC system remains incomplete, particularly concerning beamforming design across various radar transmission modes. To address this gap, this paper investigates the joint design of beamforming and subcarrier allocation for MIMO-OFDM DFRC systems. We formulate an ambiguity function-based radar and communication joint design problem. Subsequently, we propose a solution approach that solves the problem by iteratively solving the beamforming and subcarrier allocation subproblems. Simulations are conducted to assess the efficacy of the proposed design approach. The results demonstrate that our approach can effectively adapt to different radar modes and outperform reference methods.

Fault diagnosis for wireless networks is commonly conducted by human experts. However, such manual diagnosis becomes much less feasible due to the growing complexity of wireless networks. To resolve this issue, automatic fault diagnosis has been studied in self-organizing networks (SONs). However, existing works mostly consider that only a single network fault could occur at a time, which might not be true in practice. Therefore, we in this paper consider that multiple faults with different levels of severity can occur simultaneously and investigate the multi-fault and severity diagnosis for SONs. We first consider using supervised learning techniques to conduct the diagnosis and propose the corresponding diagnosis neural networks. Then, since the characteristics of different network scenarios could be different and it is costly to collect labeled data for all network scenarios, we further propose an unsupervised transfer learning approach that can effectively transfer the diagnosis system from the source domain with labeled data to the target domain with unlabeled data. We conduct extensive simulations to validate our approaches. Results show that our approach can outperform all the reference approaches. Furthermore, results also show that the performance of our transfer learning-based diagnosis is close to that of the supervised learning-based diagnosis.

As appropriate deployment of unmanned aerial vehicles (UAVs) in UAV-assisted wireless networks is critical for the next-generation wireless networks, we in this paper propose centralized and decentralized UAV deployment approaches that can be applied to any UAV-assisted wireless networks for any performance metrics. The proposed centralized deployment combines the deep neural network (DNN)-based surrogate model with the zeroth-order optimization (ZOO) such that the deployment can be optimized via using the predicted network performance of the surrogate model. Since the accurate prediction of the DNN surrogate model is critical, we discuss its design and update approaches. To let UAVs update their locations for better network performance by exchanging local information with neighboring UAVs, the proposed decentralized deployment approaches combine distributed optimization frameworks with ZOO and DNN surrogate model. We conduct realistic simulations in different network scenarios with different performance metrics to evaluate our proposed approaches. Results show that our proposed can outperform all the reference schemes in all scenarios considering different performance metrics.

Massive MIMO systems can achieve high spectrum and energy efficiency in downlink (DL) based on accurate estimate of channel state information (CSI). Existing works have developed learning-based DL CSI estimation that lowers uplink feedback overhead. One often overlooked problem is the limited number of DL pilots available for CSI estimation. One proposed solution leverages temporal CSI coherence by utilizing past CSI estimates and only sending channel state information-reference symbols (CSI-RS) for partial arrays to preserve CSI recovery performance. Exploiting CSI correlations, FDD channel reciprocity is helpful to base stations with direct access to uplink CSI. In this work, we propose a new learning-based feedback architecture and a reconfigurable CSI-RS placement scheme to reduce DL CSI training overhead and to improve encoding efficiency of CSI feedback. Our results demonstrate superior performance in both indoor and outdoor scenarios by the proposed framework for CSI recovery at substantial reduction of computation power and storage requirements at UEs.

Transmitter-side channel state information (CSI) is vital for large MIMO downlink systems to achieve high spectrum and energy efficiency. Existing deep learning architectures for downlink CSI feedback and recovery show promising improvement of UE feedback efficiency and eNB/gNB CSI recovery accuracy. One notable weakness of current deep learning architectures lies in their rigidity when customized and trained according to a preset number of antenna ports for a given compression ratio. To develop flexible learning models for different antenna port numbers and compression levels, this work proposes a novel scalable deep learning framework that accommodates different numbers of antenna ports and achieves dynamic feedback compression. It further reduces computation and memory complexity by allowing UEs to feedback segmented DL CSI. We showcase a multi-rate successive convolution encoder with under 500 parameters. Furthermore, based on the multi-rate architecture, we propose to optimize feedback efficiency by selecting segment-dependent compression levels. Test results demonstrate superior performance, good scalability, and high efficiency for both indoor and outdoor channels.

Acquiring downlink channel state information (CSI) at basestation (gNB) is crucial for optimizing performance in massive MIMO FDD systems. Deep learning (DL) architectures have shown successes in enabling UE-side CSI feedback and gNB-side recovery, but often lack flexibility and/or require volumes of customized training data for specific RF channel environments and compression ratios. This work proposes a new CSI feedback architecture called zero-replacement (ZR). ZR is free from customized training and can be directly applied to new and unseen channel scenarios without pre-training and/or customization. It is also scalable and simple to implement, making it suitable for practical massive MIMO wireless deployment. We further generalize a Select-ZR algorithm, which switches between different sparse transformation techniques to enhance recovery performance. Our numerical results demonstrate that both proposed ZR and Select-ZR algorithms achieve competitive CSI recovery accuracy and feedback efficiency across various channels against highly complex data-driven DL models.

To improve unmanned aerial vehicles (UAVs)-assisted wireless networks, the appropriate deployment of UAVs is critical. However, as existing deployment approaches are commonly based on specific models that cannot be easily generalized, we in this paper propose a generalized deployment approach for UAV-assisted wireless networks by combining the deep neural network (DNN) based surrogate model with a zeroth-order optimization (ZOO). The design of the ZOO is presented. Furthermore, since the accuracy of the surrogate model is critical, we discuss its design and update approaches. We conduct practical simulations to evaluate our proposed approach. Results show that our proposed approach can significantly outperform all the reference schemes.

To enable the intelligent transportation systems (ITSs), using radars to monitor the road environments has recently drawn attention. However, the optimization of deployment and parameters for radars in road environments has not been well-explored. To fill this gap, we in this paper investigate the joint radar deployment and parameter optimization approach for radar networks in road environments. Specifically, considering the radar FoVs, signal and interference powers, static blockage, and the impact of radar configuration, we formulate a coverage reward maximization problem that helps optimize the configuration parameters, pan angles, transmit powers, frequency band allocation, and locations of radars. Then, based on the problem, we develop an optimization approach by first decomposing the problem into subproblems, and then conducting optimization by iteratively solving the subproblems. Simulation results show that our proposed approach can outperform the reference schemes.

To further improve the performance of millimeter wave (mmWave) communications, the reconfigurable intelligent surface (RIS)-aided hybrid precoding has been studied in recent years. Following this direction, we in this paper study the joint hybrid precoder and RIS design for RIS-aided multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. We consider two types of the phase shifters which have infinite or finite resolution, and formulate the joint design problem. Designs that can jointly optimize the hybrid precoder and RIS for two types of phase shifters are then respectively proposed. Simulation results show that our proposed designs can outperform the reference design and the design in the literature.

To realize the gain guaranteed by the massive multiple-input multiple-output (MIMO) technology with a balanced tradeoff between the performance improvement and the hardware complexity, systems based on dynamic-connected hybrid analog and digital architectures are considered. To provide a more practical design, we in this paper investigate the hybrid precoder design for MIMO-OFDM systems with dynamic-connected architecture and low-resolution phase shifters and propose a design that is suitable for the systems with and without the subarray structure. Simulation results show that our proposed design can have the performance that is almost identical to the state-of-the art designs with the infinite-resolution phase shifters.

To satisfy the increasing demands of wireless traffic in vehicular networks, how to significantly improve vehicular networks becomes a critical issue. Motivated by the potential benefits of jointly using edge-computing and edge-caching in vehicular networks, this paper considers investigating the quality-aware caching, computing and communication (3C) optimization for video delivery in vehicular networks. By incorporating the quality-awareness with 3C models, we formulate a quality-aware joint 3C optimization problem. Then, by considering the practice that the caches might be pre-determined along with the formulated 3C problem, we obtain the quality-aware joint 2C optimization problem with caching. We propose effective approaches to solve these two problems. Simulation results show that our proposed approaches can outperform the reference schemes significantly.

In this paper, a machine learning-based object detection and tracking approach in radar system is proposed via using the range-angle map as the input. Specifically, by using the You Only Look Once (YOLO) for object detection and Deep Simple Online and Realtime Tracking (D-SORT) for tracking, the proposed approach can improve the detection and tracking performance, reducing the parameters needed to be manually selected, and providing more relevant information, such as the shape, size, and category of the object. We conduct the realistic simulations to evaluate the proposed approach. Results show that our proposed approach can outperform the conventional radar processing approach in terms of detection and tracking performance. Furthermore, results indicate that the object categorization of the proposed approach is accurate.

Motivated by the emerging internet of things (IoT) applications, wireless systems encounter the challenges of providing accurate indoor localization to massive IoT devices. Although the received signal strength indicator (RSSI)-based fingerprinting can provide accurate localization with low system requirements, it still suffers from multipath and fading effects. To resolve this, we propose a beam domain-based fingerprinting localization that can leverage the spatial feature with multiple antenna systems to improve the localization. Specifically, we consider using the beam domain receive power map (BDRPM), which is an RSSI-based map that captures important features of spatial fingerprints of the environment, for localization. To learn the environmental fingerprints via using BDRPMs and to conduct the localization, we propose a deep-learning approach based on the 2D convolutional neural network and auto-encoder structure. We conduct practical simulations to evaluate our proposed localization approach. The results show that our approach can provide very accurate localization, be resistant to environmental changes, and outperform the reference schemes in the literature.

Although the frequency-modulated continuous-wave (FMCW)-based scheme has been considered a candidate for realizing integrated radar-communication (RadCom) systems, it is limited by low spectral efficiency. Thus, this study proposes a frequency hopping-aided FMCW-based RadCom system along with the corresponding radar detection and communication signal demodulation approaches. The proposed system can significantly improve the transmission rate with slight radar performance degradation. In addition, both the radar and communication subsystems of the proposed RadCom system are analyzed. Consequently, a symbol interleaving approach that can mitigate radar performance degradation is proposed. Furthermore, the interference is shown to not degrade the radar performance significantly because of frequency hopping. However, to address the issue of the interference significantly degrading communication performance, interference avoidance and mitigation approaches using scheduling and interference rejection combining, respectively, are proposed. Simulations are conducted to evaluate the proposed RadCom system. The results validate the analysis and demonstrate that the proposed RadCom system can provide radar detection performance that is almost identical to conventional FMCW radar with a high transmission rate. Furthermore, the results confirm the effectiveness of the proposed interleaving and interference management methods.

Having self-organizing ability is regarded as one of the vital features for modern wireless communication networks. Such self-organizing networks (SONs) thus draw significant attention in past years. As fault diagnosis is one of the essential functionalities for SONs, in this paper, we investigate the multi-fault and fault severity level diagnosis. Specifically, we propose deep learning-based approaches that can determine the faults and their corresponding levels by utilizing the network key performance indicators (KPIs). Furthermore, to enhance recall, we propose a loss function design that can effectively trade false alarm rate against recall. We conduct simulations adopting a practical setup to evaluate the performance. Results show that our proposed approaches can accurately diagnose multiple faults and determine their severity levels.

In this paper, we consider the automotive orthogonal frequency division modulation-radar in millimeter wave band. To avoid interference between different radar systems, resources need to be split and then used by different radar systems. This thus degrades the radar performance as compared to the radar system having full resources (FRs). To mitigate this issue, we develop a deep learning-based range-Doppler (R-D) map reconstruction approach along with a time-frequency resource allocation scheme. In the reconstruction approach, we propose a deep learning-based convolutional neural network to reconstruct the R-D map such that the reconstructed R-D map can be close to the R-D map under FRs. In the resource allocation scheme, we propose a block-wise interleaved method that can facilitate the proposed reconstruction approach. Simulation results show that our proposed approach can effectively mitigate the performance degradation of radar systems when resources are shared among users.

To realize the intelligent transportation, environmental awareness of roadside units (RSUs) is of paramount importance. One of the approaches to enable the environmental awareness of RSUs is to equip RSUs with radar systems. However, as more and more radar systems are installed, manually monitoring whether these radar systems work in their normal states becomes impossible. To resolve this issue, a radar system state self-diagnosis method is proposed in this paper by using the radar sensing information with deep learning techniques. Specifically, by using the proposed feature extraction approach, we first effectively convert the huge amount of radar sensing data into useful features. Then, by using the proposed deep neural network to interpret the extracted features, the radar systems can self-diagnose whether there exist faults on the systems. We verify our proposed method via real-world experiments. Results show that our proposed method can accurately diagnose the radar system and report the faults.

For a millimeter wave (mmWave) system consisting of a basestation (BS) and a mobile user-equipment (UE), the problem of signal-and-data detection is investigated, and a low complexity ML-MMSE receiver for mmWave beamforming, beamtracking, and data-symbols detection is proposed. Specifically, with a (practical) hybrid beamforming transceiver architecture at both the BS and UE, a multistage angle-of-arrival (AoA) estimation based beamtraining algorithm that provides fast acquisition of the best beam pairs for the BS-UE link is developed. In addition, a novel joint beamtracking and data-symbols detection algorithm, equipped with an adaptive equalizer, is also developed. The algorithm can simultaneously track the best receiving beams, and produce the data estimates that are easy to extract soft bit information for soft decoding; also, it can tackle the dynamic changes of the baseband effective channel. Analytical and simulation results show that the proposed receiver performs well over a broad range of SNR-it can rapidly acquire the most dominant AoAs for beamforming and constantly track the best moving beams due to UE's mobility or device rotation-and in particular, it achieves near-optimal spectral efficiency for a mobile UE with a single RF chain or very few RF chains.

Beamforming is a key technology in communication systems of the fifth generation and beyond. However, traditional optimization-based algorithms are often computationally prohibited from performing in a real-time manner. On the other hand, the performance of existing deep learning (DL)-based algorithms can be further improved. As an alternative, we propose an unsupervised ResNet-inspired beamforming (RI-BF) algorithm in this paper that inherits the advantages of both pure optimization-based and DL-based beamforming for efficiency. In particular, a deep unfolding technique is introduced to reference the optimization process of the gradient ascent beamforming algorithm for the design of our neural network (NN) architecture. Moreover, the proposed RI-BF has three features. First, unlike the existing DL-based beamforming method, which employs a regularization term for the loss function or an output scaling mechanism to satisfy system power constraints, a novel NN architecture is introduced in RI-BF to generate initial beamforming with a promising performance. Second, inspired by the success of residual neural network (ResNet)-based DL models, a deep unfolding module is constructed to mimic the residual block of the ResNet-based model, further improving the performance of RI-BF based on the initial beamforming. Third, the entire RI-BF is trained in an unsupervised manner; as a result, labelling efforts are unnecessary. The simulation results demonstrate that the performance and computational complexity of our RI-BF improves significantly compared to the existing DL-based and optimization-based algorithms.

To achieve new applications for 5G communications, physical layer security has recently drawn significant attention. In a wiretap channel system, our goal is to minimize information leakage to an eavesdropper while maximizing the performance of transmission to the desired or legitimate receiver. Complicated systems or channel models make it difficult to design secrecy systems based on the information theory. In this paper, we propose a deep learning-based transceiver design for secrecy systems as an alternative. Specifically, we modify the loss function design of a variational autoencoder, which is a special type of neural network, making it possible to provide both robust data transmission and security in an unsupervised fashion. We further investigate the impact of an imperfect channel state information and use simulation results to prove that our approach can outperform the existing learning-based methods.

Recently, non-contact vital sign estimating devices, which are used for health monitoring, have gradually gained interest among researchers. However, most of these devices have the disadvantages of high power consumption and high cost, which limit their practicality. Therefore, a less-expensive radar-based system is suggested for long-term health monitoring. Existing radar-based vital sign estimating schemes introduce unacceptable estimating errors. In order to improve the precision and stability, we employ Newtonized Orthogonal Matching Pursuit (NOMP) algorithm. NOMP provides better estimating results compared to existing schemes in vital sign estimation tasks. However, the performance of NOMP deteriorates severely under conditions of low signal-to-noise ratio, which causes poor power efficiency. In this study, we propose deep learning (DL)-aided NOMP schemes to tackle the aforementioned issue. Our simulation results and over the air measurements suggest that DL-aided NOMP schemes are superior to existing schemes.

In automotive radar systems, the range and Doppler velocity of vehicles surrounding the radars can be estimated by performing a fast Fourier transform (FFT) on the processed received signals reflected by the vehicles. The trade-off between unambiguity for estimation and resolution in FFT-based estimation methods can be broken with low computational complexity by introducing the Chinese remainder theorem (CRT). However, there are two challenges in CRT-based methods: the additional target association procedure and the error propagation drawback. In this study, a novel multi-waveform radar frame structure is proposed to facilitate the use of the CRT. Based on the frame structure, a corresponding CRT-based target association method is proposed to eliminate ghost targets. Moreover, a generative adversarial neural network (GAN)-based target association method is proposed to further address the error propagation drawback. Simulation results show the robustness of the GAN-based method, with an outstanding performance compared to other rule-based methods, even in severe error scenarios.

The Society of Automotive Engineers (SAE) emphasizes that radars have become a critical technology. With the consideration of application scenarios and cost, increasing importance has been attributed to millimeter Wave (mmWave) radars. Because of the unique features of simultaneous detection and communication, Orthogonal Frequency- Division Modulation (OFDM) radars have been discussed frequently in the literature. In this paper, under the architecture of an OFDM radar, we propose a novel protocol which divides different radar users (i.e., different vehicles) into different logical channels by time and frequency. Following the rules of the protocol, a non- cooperative interference avoidance logical channel selection method is also proposed to choose a logical channel with the least inter-carrier interferences (ICIs). Simulation results show that the proposed logical channel selection method can choose a nearly optimal channel in a short time with a high probability.

Millimeter wave (mmWave) techniques have attracted much attention in recent years owing to features such as substantial bandwidth for communication, and it has applications in radar systems and location applications. To compensate for the severe path loss in mmWave bands, beamforming techniques with a massive antenna array are usually employed to provide high directivity. However, the resulting high-gain and narrow pencil beam make the beam alignment costlier and much more difficult. Hence, conducting beam alignment with a low overhead becomes critical. Herein, we propose a promising solution that does not require channel knowledge and treats the beam selection as an image reconstruction problem; thus, deep neural networks can be employed to operate the beam domain image reconstruction. This approach can be divided into two stages: off-line training and on- line prediction. The overhead of the on-line beam selection can be significantly reduced via off-line Eigen-beam extraction without degrading the beamforming performance. Simulations are conducted to confirm the performance of the proposed framework in scalability and robustness.

The well-known cell-averaging constant false alarm rate (CA-CFAR) scheme and its variants suffer from masking effect in multi-target scenarios. Although order-statistic CFAR (OS-CFAR) scheme performs well in such scenarios, it is compromised with high computational complexity. To handle masking effects with a lower computational cost, in this paper, we propose a deep-learning based CFAR (DL- CFAR) scheme. DL-CFAR is the first attempt to improve the noise estimation process in CFAR based on deep learning. Simulation results demonstrate that DL-CFAR outperforms conventional CFAR schemes in the presence of masking effects. Furthermore, it can outperform conventional CFAR schemes significantly under various signal-to-noise ratio conditions. We hope that this work will encourage other researchers to introduce advanced machine learning technique into the field of target detection.

The use of unmanned aerial vehicles (UAVs) as aerial wireless base stations has been recognized as an effective approach to on-demand deployment for providing services during a temporary event or emergency situation. High UAV mobility can be fully utilized to create line-of-sight connection and alleviate cross-link interference. While most of the prior works have studied UAV deployment, trajectory design and resource allocation strategies for improving the network throughput or energy efficiency, in this paper, we are interested in prolonging the lifetime of ground users for communications. The lifetime is defined as the communication time for the ground user before its battery is exhausted. We consider a frequency division multiplexing (FDM) uplink system where the ground users are served by multiple UAVs. We formulate a joint user association, power control, bandwidth allocation and UAV deployment problem for lifetime maximization, and propose an efficient approximation algorithm through judicious problem reformulation and successive convex approximation (SCA) techniques. For the scenario with only a single UAV, we show that the problem can be globally solved by simple bisection. Simulation results are presented to demonstrate that the proposed algorithms can achieve near-optimal performance and greatly outperform the heuristic methods.

This paper proposes a new compressive sensing based downlink channel state information (CSI) estimation scheme for FDD massive MIMO systems. The proposed approach, which involves two-stage weighted block ℓ1-minimization, exploits the block sparse nature of the angular domain representation of the MIMO channel matrices, as well as the existence of common scattering paths in the realistic propagation environment. In the first stage of our method, a conventional block ℓ1-minimization program is solved to extract the information about the common/individual supports of the multi-user channel matrices. In the second stage, a weighted block ℓ1-minimization algorithm, with the weighting coefficients suitably chosen to exploit the acquired support knowledge, is then performed for channel matrix estimation. Analytic performance guarantees of the proposed method are specified using the block restricted isometry property of the sensing matrix; specifically, the I-norm reconstruction error upper bounds achieved by our approach are derived. The analytic results allow us to discuss the selection of weighting coefficients for enhancing CSI estimation performance. Computer simulations show that our method achieves better estimation accuracy as compared to an existing greedy-based algorithm.

In this paper, we investigate the bit error rate (BER) for multicast multiple-input multiple-output (MIMO) systems, employing spatial modulation (SM) and its variants, called multicast SM-type MIMO systems, in Rayleigh fading channels. The system BER, here, is derived by first attaining the BER of the worst receiver of each channel realization set, and then averaging over all possible sets. We first consider the uncorrelated channels. By exploiting the system statistics, a tight BER upper bound is proposed and the diversity is discussed for the systems. We then perform the asymptotic analysis, and show that the BER of the multicast SM-type MIMO system can be alternatively analyzed by analyzing the simple point-to-point SM-type MIMO system with Weibull fading channels. Through this property, a closed-form asymptotic BER upper bound is provided and the impact of the receiver number on BER is analyzed. Subsequently, our investigation is extended to correlated channels where all the receivers share the same correlation statistics. The BER analysis is performed again through the framework similar to the uncorrelated case. In the analysis, the tight upper bound is derived and the effect of correlations is analyzed. Moreover, we provide an explicit expression for the SNR degradation caused by the receive correlation through the analysis. Finally, simulations are exploited to evaluate the BER of the multicast SM-type MIMO systems and validate the analyses.

This paper proposes a new compressive sensing-based downlink channel state information (CSI) estimation scheme for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. The proposed scheme, which involves two-stage weighted block ℓ1-minimization, exploits the block sparse nature of the angular domain representation of the MIMO channel matrices and the existence of common scattering paths in the realistic propagation environment. In the first stage of the implemented scheme, a conventional block ℓ1-minimization program is solved to extract the information about the common/individual supports of the multiuser channel matrices. In the second stage, a weighted block ℓ1-minimization algorithm, the weighting coefficients of which are suitably chosen to exploit the acquired support information, is used to estimate the channel matrices. The analytic performance guarantees of the proposed scheme are specified based on the block restricted isometry property of the sensing matrix. Specifically, the upper bounds of the ℓ2-norm reconstruction error are derived using various assumptions regarding the weighting. The obtained analytical results enable a discussion of the selection of weighting coefficients to enhance the CSI estimation performance, and the determination of a sufficient condition under which the proposed scheme outperforms the unweighted naive solution. Computer simulations show that the proposed method achieves higher estimation accuracy as compared to an existing greedy-based algorithm.

This paper studies the joint power and admission control (JPAC) problem for orthogonal frequency division multiplexing access (OFDMA) based heterogeneous networks. We consider a small-cell network coexisting with a macro-cell network. Small cells are not only subject to constraints imposed by interference with the macro-cell network but also by the minimum achievable rates of secondary user equipment (SUE). The goal is to admit as many SUE as possible to satisfy the minimum rate requirements while maximizing a certain network utility associated with the admitted SUE. To this end, we formulate two JPAC problems aimed at maximizing the network spectral efficiency (SE) and network energy efficiency (EE), respectively, where the latter has not been considered before. In light of the NP-hardness of the admission control and SE maximization problems, prior works have often treated the two problems separately without considering OFDMA constraints. In this paper, we propose a novel joint optimization framework that is capable of considering power control, admission control, and resource block assignment simultaneously. Via advanced convex approximation techniques and sequential SUE deflation procedures, we develop efficient algorithms that jointly maximize the SE/EE and the number of admitted SUE. Simulation results show that the proposed algorithms yield substantially higher SE/EE and admit more SUE than existing methods.

In this paper, the distributed channel access schemes for ALOHA-based cognitive radio networks are considered. In the considered system, time is divided into frames, which are further divided into sensing phase and transmission phase. We derive channel sensing policy in the sensing phase, and channel access policy in the transmission phase for a secondary user (SU) to maximize its throughput. To mitigate high complexities of the above scheme, we propose a threshold-based channel access scheme. In this scheme, an appropriate threshold is set based on channel occupancy information and channel state information, and only channels providing potentially high throughput will be sensed and accessed. The proposed schemes are fully distributed, i.e., no extra information exchange is needed among SUs, which is a highly desired and beneficial property. Simulation results confirm that the proposed schemes outperform prior random access schemes.

In this paper, we propose two generalized precoder designs to enhance the bit error rates for the general category of spatial modulation (SM) in multiple-input multiple-output (MIMO) systems with channel state information at the transmitter (CSIT). We investigate typical SM-MIMO systems and propose two optimization formulations for designing precoders. Our design rationale for the first formulation is to maximize the minimum Euclidean distance among codewords; for the second formulation, it is to minimize the total signal power for the lower-bounded Euclidean distances among codewords. Since both formulations are non-convex and their optimal solutions are generally intractable, we propose an algorithm that acquires effective solutions by iteratively solving the alternative convex problem linearized and approximated from the original non-convex problem. Discussions on complexity analysis, performance comparisons, design challenges, and robustness in imperfect CSIT are then provided. By generalizing the design formulations, the proposed precoder designs can be extended to generalized SM, which completes the investigation for virtually all SM-type systems. Simulation results show that the proposed designs improve the performance of SM-/GSM-MIMO systems and outperform existing precoding methods with a potentially higher complexity cost.

In multiple-input multiple-output (MIMO) systems adopting space shift keying (SSK), the use of adaptive precoder on transmitter offers the opportunity to improve its performance significantly. One major challenge in precoder operation is the difficulty in obtaining the full channel state information on transmitter (CSIT). In this work, we investigate the precoder design for SSK-MIMO systems with limited feedback through using the codebook for the precoding. We formulate a distortion metric to evaluate the quality of a codebook based on the maximum minimum Euclidean distance criterion and propose a codebook design criterion accordingly. Two effective codebook design algorithms are proposed based on thorough analysis of the criterion. Simulation results show the performance improvement of the proposed codebook-based precoding, and also support our analyses on the codebook design.

This work investigates the configuration selection in IA-aided UL CoMP systems which pursues the maximal achievable sum rate. The configuration is defined by the number of data streams transmitted in each user. Intuitively, the solution can be found by exhaustively calculating the achievable sum rate for all the possible configurations, and select the configuration leading to the maximal achievable sum rate. The exhaustive search method is infeasible due to its high complexity. Based on the characteristics of IA-aided UL CoMP systems, an algorithm is proposed to obtain the configuration with extremely low complexity, and the proposed approach achieves comparable performance as in exhaustive method.

The authors investigate linear transceiver design in uplink (UL) coordinated multipoint transmission and reception (CoMP) multiple‐input multiple‐output (MIMO) systems with joint detections. A two‐stage design algorithm is proposed by exploiting the technique of interference alignment, optimising the structure of the effective channel and employing power loading, with the goal to achieve high throughput and convergence performance. In contrast to conventional CoMP transceiver design, which is investigated under a predefined number of data streams transmitted by each user, the authors further investigate the selection of the number of data streams, called the configuration selection, and propose a corresponding low‐complexity algorithm. By combining the proposed linear transceiver algorithm and low‐complexity configuration selection algorithm, this work presents a new practical framework for linear transceiver design in UL CoMP MIMO systems. The simulation results confirm that the proposed algorithms achieve higher sum‐rate performance than prior linear transceivers used in UL CoMP MIMO systems. Furthermore, the proposed transceiver offers comparable performance to existing IA‐aided transceivers with significantly faster convergence.

An efficient interference alignment (IA) aided transceiver design algorithm for uplink (UL) coordinated multipoint (CoMP) systems is proposed to mitigate interference. We use the block QR decomposition (BQRD) to resolve the interdependency of precoders among user equipment (UE) through the successive interference cancellation (SIC) technique. To further improve the efficiency, an additional constraint is employed by a projection operation. Simulation results show that the proposed algorithm substantially reduces iterations and obtain comparable performance as in conventional approaches.

This paper investigates new decentralized dynamic resource allocation scheme which exploits game theoretic regret-matching procedure to enhance the spectrum efficiency for cognitive femtocell networks. In order to ensure spectrum usage, we develop a new resource allocation scheme with joint overlay and underlay strategy to increase frequency reuse factor among cognitive femtocell networks. The proposed scheme can avoid interference to macrocell and the other femtocell user equipments; furthermore, the number of iterations required is less than the original regret-matching scheme, and it has been proved that at least one of correlated equilibrium exists in proposed algorithm. Computer simulations are presented to verify the proposed scheme which is effective for resource allocation. The proposed scheme is comparable to the performance of exhaustive search scheme and outperforms the original regret-matching scheme.

Femtocell networks are an advantageous low-cost technology for providing indoor coverage and high-capacity solutions. Femtocell networks reuse the spectrum resources in co-channel environments due to its high geometric density. In this paper, a distributed algorithm is proposed, which uses a cluster-based self-organizing approach to managing the spectrum and power resources among femtocells. The aim of the proposed algorithm is to achieve an optimized throughput of the networks in a decentralized manner. The algorithm involves three phases: sensing phase, sniffer phase, and power control phase, and is performed in each femtocell. Simulation results indicate that the proposed algorithm improves the uplink throughput of the femtocell networks and effectively avoids interference with the conventional macro-cellular networks.

Achieving high system throughput for limited- feedback communications against the time-varying channel effect is rather crucial in modern mobile system designs. Within the beamforming setup, this paper derives analytic throughput results for both the "more feedback less often" and "less feedback more often" scenarios. More specifically, under the assumptions that (i) the channels over two consecutive time slots follow the first-order Markov model and (ii) in each time slot, reliable feedback of a fixed amount of bits is allowed, the achievable system throughput over two consecutive time slots in both scenarios are characterized. In particular, while the exact throughput is in an integral form, we derive the associated closed-form approximate formulae which facilitate throughput evaluation without resorting to numerical integration. The analytic results also lead to a very low-complexity throughput-based mode selection scheme. Simulation study shows that: (1) the derived closed-form approximation is quite accurate; (2) with the aid of the proposed mode selection method, the throughput performance is robust against the channel temporal variation.

We propose a BER improved power allocation scheme for D-STTD systems over i.i.d. Rayleigh fading channels under the QR-based successive detection framework. Instead of relying on BER under a fixed channel realization, the adopted design criterion is the mean BER (assuming there is no inter-layer error propagation) averaged with respect to the channel distribution. Such a design metric has two-fold advantages: (i) It is analytically tractable and is closely related to a block error probability upper bound when inter-layer error propagation occurs, and (ii) There is no need for repeated feedback of the instantaneous channel information. By exploiting a distinctive channel matrix structure unique to D-STTD systems we derive a closed-form approximate upper bound of the considered BER metric; through minimization of this bound an optimal power allocation scheme is obtained. Numerical simulation is used to illustrate the performance of the proposed method.

Medium access control (MAC) protocol design for cooperative networks over multi-packet reception (MPR) channels is a challenging topic, but has not been addressed in the literature yet. In this paper, we propose a MAC protocol to exploit the cooperation diversity for throughput enhancement over MPR channels. The proposed approach can efficiently utilize the idle periods for packet relaying, and can thus effectively limit the throughput loss resulting from the relay phase. By means of a Markov chain model, the worst-case throughput analysis is conducted. Specifically, we derive (i) a closed-form upper bound for the throughput penalty of the direct link that is caused by the interference of concurrent packet relay transmission; (ii) a closed-form lower bound for the throughput gain that a user with packet transmission failure can benefit thanks to cooperative packet relaying. The results allow us to investigate the throughput performance of the proposed protocol directly in terms of the MPR channel coefficients. Simulation results confirm the system-wide throughput advantage achieved by the proposed scheme, and also validate the analytic results.

Bit assignment for local sensor data quantization in the decentralized best-linear-unbiased-estimation (BLUE) scenario is widely addressed in the signal processing research for wireless sensor networks. When the timely knowledge of the instantaneous sensor noise variance (for implementing the BLUE fusion rule) is too costly to obtain, one plausible alternative is to exploit the associated statistical characterization. Related such proposals, however, do not explicitly take into account the communication link impairments such as channel fading. In this paper we extend the current results to the more realistic case when signal transmission is subject to the fading effect. We show that the optimal bit allocation problem can be reformulated in the form of convex optimization, and then derive an analytical solution. Through numerical simulation the proposed solution is seen to outperform the uniform energy allocation scheme.

Medium access control (MAC) protocol design for cooperative networks over multi-packet reception (MPR) channels is a challenging topic, but has not been addressed in the literature yet. In this paper, we propose a cooperative multi-group priority (CMGP) based MAC protocol to exploit the cooperation diversity for throughput enhancement over MPR channels. The proposed approach can bypass the computationally-intensive active user identification process. Moreover, our method can efficiently utilize the idle periods for packet relaying, and can thus effectively limit the throughput loss resulting from the relay phase. By means of a Markov chain model, the worst-case throughput analysis is conducted. The results allow us to investigate the throughput performance of the proposed CMGP protocol directly in terms of the MPR channel coefficients. Simulation results confirm the system-wide throughput advantage achieved by the proposed scheme, and also validate the analytic results.

This paper studies the energy-constrained MMSE decentralized estimation problem with the best-linear-unbiased-estimator fusion rule, under the assumptions that i) each sensor can only send a quantized version of its raw measurement to the fusion center (FC), and ii) exact knowledge of the sensor noise variance is unknown at the FC but only an associated statistical description is available. The problem setup relies on maximizing the reciprocal of the MSE averaged with respect to the prescribed noise variance distribution. While the considered design metric is shown to be highly nonlinear in the local sensor transmit energy (or bit loads), we leverage several analytic approximation relations to derive a associated tractable lower bound; through maximizing this bound a closed-form solution is then obtained. Our analytical results reveal that sensors with bad link quality are shut off to conserve energy, whereas the energy allocated to those active nodes is proportional to the individual channel gain. Simulation results are used to illustrate the performance of the proposed scheme.

This brief considers blind signal source separation via algebraic known modulus algorithm. It is shown that through proper signal modulus design the estimation accuracy of the beamforming vector, as well as the performance of signal separation, can be improved. Specifically, based on a matrix perturbation analysis we propose a criterion, in the form of minimizing the maximal singular value of the modulus code matrix, for enhancing robustness of the beamforming vector against measurement noise. A closed-form solution is then derived and its performance is tested through numerical simulation.

We consider MIMO single-carrier block transmission over time-varying multipath channels, under the assumption that the channel parameters are not exactly known but are estimated via the least-squares training technique. While the channel temporal variation is known to negate the tone-by-tone frequency-domain equalization facility, it is otherwise shown that in the time domain the signal signatures can be arranged into groups of orthogonal components, leading to a very natural yet efficient group-by-group symbol recovery scheme. To realize this figure of merit we propose a constrained-optimization based receiver which also takes into account the mitigation of channel mismatch effects caused by time variation and imperfect estimation. The optimization problem is formulated in an equivalent unconstrained generalized-sidelobe-canceller setup. This enables us to directly model the channel mismatch effect into the system equations through the perturbation technique and, in turn, to further exploit the statistical assumptions on channel temporal variation and estimation errors for deriving a closed-form solution. Within the considered framework the proposed robust equalizer can be further combined with the successive interference cancellation mechanism for further performance enhancement.

Relying on a simple flag-assisted mechanism, a multi- group priority queueing (MGPQ) medium access control (MAC) protocol is proposed for the multipacket reception (MPR) channel. The proposed MGPQ scheme is capable of overcoming two major performance bottlenecks inherent in the existing MPR MAC protocols. First, the proposed solution can automatically produce the list of active users by observing the network traffic conditions, removes the need of active user estimation algorithm, and thus can largely reduce the algorithm complexity. Second, the packet blocking constraint imposed on the active users for keeping compliant with prediction is relaxed. As a result, the proposed MGPQ is not only applicable to both homogeneous and heterogeneous cases, but also outperforms the existing MPR MAC protocols. Simulation results show that the network throughput can be improved by 40% maximum and 14% average as compared with the well known dynamic queue (DQ) MAC protocol.

We consider MIMO single-carrier block transmission over time-varying multipath channels, under the assumption that the channel parameters are not exactly known but are estimated via the least-squares training technique. While the channel temporal variation is known to negate the tone-by-tone frequency-domain equalization facility, it is otherwise shown that in the time domain the signal signatures can be arranged into groups of orthogonal components, leading to a very natural yet efficient group-by-group symbol recovery scheme. To realize this figure of merit we propose a constrained-optimization based receiver which also takes into account the mitigation of channel mismatch effects caused by time variation and imperfect estimation. The optimization problem is formulated in an equivalent unconstrained generalized-sidelobe-canceller setup. This enables us to directly model the channel mismatch effect into the system equations through the perturbation technique and, in turn, to further exploit the statistical assumptions on channel temporal variation and estimation errors for deriving a closedform solution. Within the considered framework the proposed robust equalizer can be combined with the successive interference cancellation mechanism for further performance enhancement. Flop count evaluation and numerical simulation are used to evidence the advantages of the proposed scheme.

This paper studies minimal-energy decentralized estimation in sensor networks under best-linear-unbiased-estimator fusion rule. While most of the existing related works require the knowledge of instantaneous noise variances for energy allocation, the proposed approach instead relies on an associated statistical model. The minimization of total energy is subject to certain performance constraint in terms of mean square error (MSE) averaged over the noise variance distribution. A closed-form formula for the overall MSE metric is derived, based on which the problem can be reformulated in the form of convex optimization and is shown to yield an analytic solution. The proposed method shares several attractive features of the existing designs via instantaneous noise variances; through simulations it is seen to significantly improve the energy efficiency against the uniform allocation scheme.

We consider MIMO-OFDM transmission, in a scenario that the adopted cyclic-prefix (CP) length is shorter than the channel delay spread for boosting data rate and, moreover, the channel parameters are not exactly known but are estimated using the least-squares (LS) training technique. By exploiting the receiver spatial resource, we propose a constrained-optimization based linear equalizer which can mitigate inter- symbol interference and inter-carrier interference incurred by insufficient CP interval, and is robust against the net detrimental effects caused by channel estimation errors. The optimization problem is formulated in an equivalent unconstrained generalized-sidelobe- canceller (GSC) setup. The channel parameter error is explicitly incorporated into the constraint-free GSC system model through the perturbation technique; this allows us to exploit the presumed LS channel error property for deriving a closed-form solution. Simulation results confirm the effectiveness of the proposed method.

This paper investigates the least-squares (LS) estimation of unknown deterministic parameters from a standard linear model characterized by a class of block-circulant-with-circulant-block (BCCB) matrix. We propose a method for designing the BCCB system matrix coefficients to minimize the mean square error incurred by the LS estimate, under certain equality and inequality constraints. By exploiting the eigenvalue characteristic of BCCB matrices, precise analysis is undertaken to derive a closed-form solution. The considered optimization problem arises in the study of blind channel estimation for single-carrier block transmission with cyclic prefix; the presented analysis reveals several key features associated with the BCCB family, and shows an original investigation of the BCCB matrix structure for facilitating linear optimal parameter estimation

This paper studies the V-BLAST detection in a general multiuser space-time wireless system, in which each user's data stream is either (orthogonal) space-time block coded (OSTBC) for transmit diversity or spatially multiplexed (SM) for high spectral efficiency. The motivation behind this work is that each user adopting a signaling scheme better matched to his own channel condition proves to improve the individual link performance but the resultant co-channel interference mitigation problem is scarcely addressed thus far. By exploiting the algebraic structure of orthogonal code, it is shown that the V-BLAST detector in the considered dual-signaling environment allows for an attractive group-wise implementation: at each iteration a group of symbols, transmitted either from an OSTBC station or from an antenna of an SM terminal, are jointly detected. The group detection property, resulting uniquely from the use of orthogonal codes, potentially improves the dual-mode signal separation efficiency, especially when the OSTBC terminals are dense in the cell. The embedded structure of the channel matrix is also exploited for deriving a computationally efficient detector implementation. Flop count evaluations and numerical examples are used for illustrating the performance of the proposed V-BLAST based solution

This paper investigates multiuser orthogonal space-time block coded signal detection within the ordered successive interference cancellation (OSIC) framework. Both the zero-forcing and minimum-mean-square-error ordering criteria are considered. When each user terminal is equipped with no more than four transmit antennas, it is shown that orthogonal transmit redundancy leads to an appealing signal ordering property: in each processing layer the transmitted symbols of an arbitrary user are associated with an identical ordering metric. This guarantees the feasibility of (user based) group-wise symbol recovery through the OSIC mechanism. Analytic bit-error-rate performance is given. Computer simulations and flop count evaluations are also provided for comparing the OSIC based solution with existing multiuser detection schemes reported for the considered system

A multi-antenna generalized sidelobe canceller (GSC) based equalizer for inter-symbol interference (ISI) suppression is proposed for high-rate single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) systems without cyclic prefix (CP). The proposed method relies on the block representation of the OFDM transmission and exploits the ISI subspace structure in the associated multi-antenna system model. A computationally efficient partial adaptivity (PA) implementation of the GSC equalizer is also provided for reducing the receiver complexity. Simulation results show that the proposed GSC-based solution yields an equalization performance almost identical to that obtained by the conventional CP-based OFDM system and is highly resistant to the increase of channel delay spread.

A new MIMO transceiver is proposed for the downlink of high data rate multicarrier CDMA (MC-CDMA) systems over frequency-selective multipath channels. The design of the transceiver involves the following procedure. First, the data stream is demultiplexed into multiple substreams, which are then encoded by a set of space time block codes (STBC) for achieving spatial diversity. Second, the coded substreams are spread by a set of space-path spreading (SPS) codes which is designed to achieve path multiplexing by exploiting independent multipath channels and pre-suppress the multiple access interference (MAI) without the use of channel state information (CSI). These substreams are then transmitted simultaneously from multiple antennas. At each mobile station, a simple matched filter (MF) is used to despread the received data. Linearly combining the MF outputs with the aid of CSI and employing a multi-user detection scheme can then separate the mutually interfering signals from the multiple transmit antennas and restore the diversity gain due to SPS and STBC. An increased spectral efficiency and diversity gain can thus be achieved at the same time. Simulation results confirm that the proposed transceiver offers better performance than the conventional BLAST transceiver, and achieves nearly the performance of STBC and space-time spreading BLAST transceiver.

A robust iterative multicarrier code-division multiple-access (MC-CDMA) receiver with adaptive multiple-access interference (MAI) suppression is proposed for a pilot symbols assisted system over a multipath fading channel with frequency offset. The design of the receiver involves a two-stage procedure. First, an adaptive filter based on the generalized sidelobe canceller (GSC) technique is constructed at each finger to perform despreading and suppression of MAI. Second, pilot symbols assisted frequency offset estimation, channel estimation and a RAKE combining give the estimate of signal symbols. In order to enhance the convergence behavior of the GSC adaptive filters, a decisions-aided scheme is proposed, in which the signal waveform is first reconstructed and then subtracted from the input data of the adaptive filters. With signal subtraction, the proposed MC-CDMA receiver can achieve nearly the performance of the ideal maximum signal-to-interference-plus noise ratio receiver assuming perfect channel and frequency offset information. Finally, a low-complexity partially adaptive (PA) realization of the GSC adaptive filters is presented as an alternative to the conventional multiuser detectors. The new PA receiver is shown to be robust to multiuser channel estimation errors and offer nearly the same performance of the fully adaptive receiver.

A CDMA receiver with enhanced multiple access interference (MAI) suppression is proposed. The design of the receiver involves the following procedure. First, adaptive correlators are constructed at different fingers based on the scheme of generalized sidelobe canceller (GSC) to collect the multipath signals and suppress MAI blindly. In particular, a partially adaptive (PA) realization of the GSC correlators is proposed based on the conjugate gradient (CG) technique. The next step is then a simple coherent combining of the correlator outputs with pilot aided channel estimation. Finally, further performance enhancement is achieved by an iterative scheme in which the signal is reconstructed and subtracted from the GSC correlators input, leading to faster convergence of the receiver. The proposed low complexity PA receiver is suitable for downlink CDMA, and shown to outperform the conventional fully adaptive MMSE receiver by using a small number of pilot symbols.

A multistage multicarrier CDMA (MSMC-CDMA) receiver with enhanced multiple access interference (MAI) suppression is proposed for a reverse link time-multiplexed pilot symbols assisted system over multipath channels. The design of the receiver involves the following procedure. First, a blind adaptive matched filter is attached to each finger to perform despreading and combat MAI. Second, channel estimation and a RAKE combiner gives the estimate of the signal. Finally, signal reconstruction is accomplished by exploiting the channel estimate, data decisions, and signal's signature. The reconstructed signal is then subtracted from the data sent to the next stage. With signal reconstruction and subtraction as stages proceed, the MSMC-CDMA receiver can achieve nearly the performance of the optimal maximum SINR (MSINR) receiver, as confirmed by simulations.

A beamspace-time (BT) receiver is proposed for interference suppression and multipath diversity reception in sectored wireless code division multiple access communications. The scheme involves two stages. First, a set of adaptive space-time diversity processors, in the form of beamformer-correlator pairs, is constructed which provides effective suppression of unwanted interference and reception of signals from a prescribed space-time region. Second, the output data obtained by these processors are maximum ratio combined to capture the signal multipath components coherently. The proposed BT receiver is blind in that no training signal is required. The only information required is the signature sequence, timing and a rough estimate of the angle of arrival of the signal for selecting the sector of interest.

A beamspace-time (BT) RAKE receiver is proposed for multiple access interference (MAI) suppression and multipath diversity reception in sectored wireless CDMA communications. The scheme involves two stages. First, a set of diversity beamformers is constructed and then combined for each finger which provides effective reception of in-sector signals and suppression of out-of-sector MAI. Second, an adaptive correlator is attached to each combined beam to combat in-sector MAI and collect signal multipaths in a coherent manner. The proposed BT RAKE receiver is blind in that no training signal is required. The only information required is the signature sequence, timing and a rough estimate of the angle of arrival (AOA) of the signal.

An application of the radial-basis function neural networks (RBFNN) to the angle-of-arrival (AOA) estimation of a desired source in multipath environments is investigated. In conjunction with a set of judiciously constructed beamformers, the RBFNN are used to estimate the desired AOA within an angular sector of interest (ASOI). With a pilot signal emitted from each of the training AOAs within the ASOI, the RBFNN is trained with the higher-order statistics (HOS) estimated from the received array data. In principle, the RBFNN AOA estimator maps the complex HOS into the desired angle response as an function approximator. By matching the HOS to the center vectors associated with the hidden nodes and linearly combining the node values, an AOA estimate results. The efficacy of the proposed AOA estimator is confirmed by computer simulations.