This work proposes novel beam tracking methods to significantly reduce training overhead and combat harsh channel environments that lead to tracking failures, including blockages, multipaths, user movements, and rotations. The proposed beam tracking system is designed using a new low-carbon-footprint machine learning technique called Green Learning (GL). We introduce a GL-based prediction model (GLPM) along with an anti-blockage method (ABM) and an anti-rotation method (ARM) to enhance robustness. In a 5G NR beam management scenario utilizing a near-realistic ray-tracing channel, the proposed integrated beam tracking scheme significantly reduces the number of Synchronization Signal Blocks (SSBs) by 46–57 and outperforms traditional beam sweeping methods, achieving 1.3 to 4 times higher data rates compared to sweeping with 64 SSBs. Additionally, the proposed approach delivers superior accuracy while maintaining lower computational complexity than deep learning-based methods. Moreover, when examining instantaneous tracking ability, the proposed beam tracking scheme can closely follow ideal cases and rescue frequent tracking failures in conventional beam sweeping caused by harsh channel environments. This rescue is more pronounced in low SNR regions, where conventional beam sweeping can frequently lose connection entirely, while the proposed scheme maintains satisfactory link quality thanks to its accurate beam tracking that provides sufficient received power. From the experiments, the GLPM accounts for 70% to 93% of the overall operations, resulting in low latency and computational complexity. The ABM and ARM do not need to operate frequently but effectively address the shortcomings of GLPM in specific environments, significantly enhancing overall performance. The superior tracking ability and reduction in training redundancy demonstrate that the proposed integrated beam tracking technique is a promising solution for future 5G/6G beam management.

MIMO-OFDM is a key physical-layer architecture for current and next-generation wireless communication. This paper proposes a receive beamforming design scheme for MIMO-OFDM radars configured with partially-connected hybrid linear arrays, in an attempt to improve target parameter (i.e., angle, range, and velocity) estimation. Unlike existing joint estimation methods, our approach first adopts a subspace algorithm to acquire the angle information at the analog beamformer output. Then, maximum signal-to-interference ratio (MSINR) digital beamforming is conducted for target signal alignment to achieve (i) accurate range-velocity estimation thanks to suppressed interference, and (ii) automatic pairing of the identified parameter triple. Moreover, the received signal structure at the analog beamformer output is exploited for obtaining side information of the target range, which can be used to accelerate the search of range and velocity. Simulation results are used to illustrate the effectiveness of the proposed scheme.

This work proposes a novel machine learning (ML) based beam tracking scheme. The proposed scheme is inspired by a new learning method, called Green Learning (GL), which has several nice properties including lightweight, low complexity, and logical transparency. GL has demonstrated outstanding performance in the field of image/video processing, and this work continues showing its great potentials in beam tracking problem. In a setting of 5G NR (New Radio) beam tracking protocol, the proposed GL beam tracking scheme demonstrates several advantages over conventional ML methods including an improved accuracy in tracking angle of arrival, less sensitive to channel signal-to-noise ratio, an extended time duration to lose tracking, and lower computational complexity in both training and inference. These effective and efficient properties make the proposed GL beam tracking suitable for future communications, especially with critical demand of low carbon footprint.

This work attempts to minimize power consumption for analog beamforming with non-ideal power amplifier (PA) models under the constraints of a per-PA maximum power and a user target rate in low earth orbit (LEO) satellite communications. We propose a majorization-minimization (MM) scheme, utilize a surrogate function to transform the problem into a second-order cone programming (SOCP) one, and derive an interference-free analog precoder. Simulation results show that the proposed scheme converges rapidly and tends to either pull the PAs on the antenna array to the maximum power level or directly turn off the PAs instead of staying in the middle power levels. This observation together with the proposed scheme may be used to simplify the PA design in practice, since the middle power levels are seldom visited and there is no necessity to design a great number of middle power stages.

This work proposes beamforming solutions to minimize system bit error rate (BER) for low earth orbit (LEO) satellite communications with non-orthogonal multiple access (NOMA). Designing beamformers to minimize BER in NOMA schemes is a nonlinear problem. We resort to the interior-point method to solve this problem, which can significantly improve the BER performance. To reduce computational complexity, we further propose another beamforming solution based on the projected gradient descent (PGD) scheme, which not only achieves comparable performance as the interior-point method but also greatly reduces the computational complexity. We evaluate the proposed beamforming schemes in a designed LEO satellite scenario. Simulation shows that the proposed system significantly outperforms conventional schemes in terms of BER.

Millimeter-wave (mmWave) networks offer the potential for high-speed data transfer and precise localization, leveraging large antenna arrays and extensive bandwidths. However, these networks are challenged by significant path loss and susceptibility to blockages. In this study, we delve into the use of situational awareness for beam prediction within the 5G NR beam management framework. We introduce an analytical framework based on the Cramér-Rao Lower Bound, enabling the quantification of 6D position-related information of geometric reflectors. This includes both 3D locations and 3D orientation biases, facilitating accurate determinations of the beamforming gain achievable by each reflector or candidate beam. This framework empowers us to predict beam alignment performance at any given location in the environment, ensuring uninterrupted wireless access. Our analysis offers critical insights for choosing the most effective beam and antenna module strategies, particularly in scenarios where communication stability is threatened by blockages. Simulation results show that our approach closely approximates the performance of an ideal, Oracle-based solution within the existing 5G NR beam management system.

Angle-of-arrival (AoA) estimation via hybrid uniform linear arrays is subject to inherent ambiguity incurred by mixing many subarray measurements into just few RF chains. With the aid of non-uniform subarray placement, this paper proposes a low-complexity beam-space MUSIC algorithm capable of achieving ambiguity-free multiple AoA estimation. An ambiguity-free condition, specified by inter-subarray spacings, is derived, leading to various array configurations guaranteeing unique AoA recovery. Simulation results show that our proposed approach compares favorably with an existing temporal-domain based MUSIC method at reduced computational complexity.

Determining the location of Wi-Fi access points (APs) is vital for various Wi-Fi-based applications, such as localization, security, and AP deployment. Considerable effort has been exerted in the field of AP localization. In contrast to studies that require additional robots with specialized antenna arrays, we present EasyAPPos, a lightweight, always-on, and user-centered AP positioning solution that utilizes widely available mobile phones. We focus on addressing three challenges in AP positioning. First, the patch antenna on a mobile phone has a limited angular range due to its size, but our approach proposes a method for utilizing human natural rotation to enhance angular diversity. Second, our angle-based algorithm does not require synchronous clocks between the mobile device and the APs, in contrast to existing algorithms that require this synchrony to transform propagation delays into positions. Nevertheless, our algorithm can still utilize asynchronous delay information. Third, the low bandwidth of Wi-Fi beacon frames, which only provide limited capacity to counteract the effects of multipath, is addressed by performing AP positioning under challenging conditions. We validate EasyAPPos through simulations and experiments, which demonstrate its ability to achieve decimeter-level positioning accuracy even under harsh conditions.

This paper proposes a novel physical beam sharing system to use few radio-frequency (RF) chains to support more users. The concept of beam sharing is to design a beam pattern to simultaneously benefit all users. In this proposed system, every user can receive a number of data streams equal to the number of RF chains at the base station (BS) and achieve full multiplexing gain. A special design example is that the BS can support multiple users simultaneously even with only one RF chain. As a result, the computational and hardware costs of the BS can be significantly reduced due to the decreased RF chains in deploying a dense communications network. We first derive beamforming designs for this proposed system in a MISO channel with one data stream. Then, the results are further extended to MIMO channels with multiple data streams. Simulation results show that the proposed schemes with the derived solutions outperform conventional systems in several interested parameter settings, and have advantages in terms of fairness and complexity compared with NOMA (non-orthogonal multiple access) systems. Moreover, we also implemented 28 GHz hardware platforms with an$8 \times 8$antenna array to verify the feasibility and advantages of the proposed schemes as well as measure the resulting beam patterns. The over-the-air (OTA) testings validate the concept of the proposed physical beam sharing and show that it outperforms conventional physical beamforming schemes.

Many operators have been bullish on the role of millimeter-wave (mmWave) communications in fifth-generation (5G) mobile broadband because of its capability of delivering extreme data speeds and capacity. However, mmWave comes with challenges related to significantly high path loss and susceptibility to blockage. Particularly, when mmWave communication is applied to a mobile terminal device, communication can be frequently broken because of rampant hand blockage. Although a number of mobile phone companies have suggested configuring multiple sets of antenna modules at different locations on a mobile phone to circumvent this problem, identifying an optimal antenna module and a beam pair by simultaneously opening multiple sets of antenna modules causes the problem of excessive power consumption and device costs. In this study, a fast antenna and beam switching method termed Fast-ABS is proposed. In this method, only one antenna module is used for the reception to predict the best beam of other antenna modules. As such, unmasked antenna modules and their corresponding beam pairs can be rapidly selected for switching to avoid the problem of poor quality or disconnection of communications caused by hand blockage. Thorough analysis and extensive simulations, which include the derivation of relevant Cramér-Rao lower bounds, show that the performance of Fast-ABS is close to that of an oracle solution that can instantaneously identify the best beam of other antenna modules even in complex multipath scenarios. Furthermore, Fast-ABS is implemented on a software defined radio and integrated into a 5G New Radio physical layer. Over-the-air experiments reveal that Fast-ABS can achieve efficient and seamless connectivity despite hand blockage.

Signal recovery through coarse quantization of a linear transform output has many applications in engineering, such as channel estimation and signal detection in massive MIMO systems. A recently proposed scheme, known as generalized expectation consistent signal recovery (GEC-SR), can achieve Bayesian inference and exhibit better robustness than many existing methods. However, recovering signals with large transform matrices continue to present a computational burden for GEC-SR. In this study, we develop a novel decentralized architecture by leveraging the core framework of GEC-SR called "deGEC-SR." deGEC-SR offers excellent performance as GEC-SR and runs tens of times faster than GEC-SR. We derive the theoretical state evolution of deGEC-SR and demonstrate its accuracy using numerical results.

Millimeter-wave (mmWave) communication has become a promising option for meeting the multi-fold increase in demand for mobile data in the fifth-generation (5G) mobile broadband. However, when mmWave is applied to a mobile terminal device, communication can be frequently broken due to rampant hand blockage. Although this problem can be overcome by configuring multiple sets of subarrays at different locations, developing a fast and efficient operation that can find the best subarray and beam direction with power, complexity, and latency constraints is extremely challenging. In this study, we propose a fast antenna and beam switching method termed `Fast-ABS' that uses only one antenna module for the reception to predict the best beam of other subarrays. Through extensive simulations, we demonstrate that Fast-ABS achieves efficient and seamless connectivity under hand blockage. In addition, we implement Fast-ABS on software radios and integrate it into the 5G New Radio physical layer. Our experiments show that the performance of the proposed beam switching method is close to that of an “Oracle” solution that can instantaneously identify the best beam of other subarrays even in complex non-line-of-sight scenarios.

Continuous learning seeks to perform the learning on the data that arrives from time to time. While prior works have demonstrated several possible solutions, these approaches require excessive training time as well as memory usage. This is impractical for applications where time and storage are constrained, such as edge computing. In this work, a novel training algorithm, regularized subspace approximation classifier (RSAC), is proposed to achieve lightweight continuous learning. RSAC contains a feature reduction module and classifier module with regularization. Extensive experiments show that RSAC is more efficient than prior continuous learning works and outperforms these works on various experimental settings.

We propose a new transmission scheme for uplink URLLC systems, which can coexist with the eMbb transmission while can still meet the reliability and latency requirements of the URLLC. The proposed scheme spreads the data in both time and frequency domains. The performance of the proposed system is analyzed and closed form expressions are derived. From the analytical results, we optimize the parameters including constellation levels, spreading length and transmit power to maximize the sum rate of the URLLC UE. Simulation results show that the proposed solution significantly improve the performance and the data rate can be up to 8.9 Mpbs in the LTE TDL-C nominal channel with co-existence and achieved URLLC requirements.

Phase retrieval algorithms are now an important component of many modern computational imaging systems. A recently proposed scheme called generalized expectation consistent signal recovery (GEC-SR) shows better accuracy, speed, and robustness than numerous existing methods. Decentralized GEC-SR (deGEC-SR) addresses the scalability issue in high-resolution images. However, the convergence speed and stability of these algorithms heavily rely on the settings of several handcrafted tuning factors with inefficient turning process. In this work, we propose deGEC-SR-Net by unfolding the iterative deGEC-SR algorithm into a learning network architecture with trainable parameters. The parameters of deGEC-SR-Net are determined by data-driven training. Numerical results show that deGEC-SR-Net provides substantially faster convergence than deGEC-SR and exhibits superior robustness to noise and prior mis-specifications.

This work proposes a system to predict the outcomes of the defibrillation during the period of ventricular fibrillation. Accurate outcomes can avoid inefficient defibrillation that causes severe myocardial injury. In this system, we apply a neural network model and use the frequency components of ECG signals as training data to determine the neuron coefficients. The trained system is then validated (tested) using different set of data to justify the performance. Experimental results are provided to show superior performance of the proposed system.

Analog beamforming is widely used in the incoming 5G/B5G communications systems to leverage against power consumption by significantly reducing the number of RF chains. In this paper, we propose a new multiuser beamforming scheme that allows the number of RF chains to be smaller than the number of users. In addition, we propose the beamforming algorithms to minimize the transmit power and weighted symbol error rates for the proposed scheme. For both high- and low-SNR regimes, the proposed beamforming designs provide simple closed-form solutions. The simulation results show that with half the number of RF chains and, thus, lower hardware cost, the proposed scheme outperforms the conventional zero-forcing beamforming systems in several interested parameter settings. Meanwhile, the performance degradation due to the quantization effect of the proposed system is less pronounced than that of the conventional beamforming systems.

This work proposes a new algorithm and analytical methods for predicting the successful rate of a defibrillation during ventricular fibrillation. Accurate predictions help in improving outcomes of cardiopulmonary resuscitation. This also helps in finding a good defibrillation timing to avoid ineffective defibrillation, which leads to myocardial damages and thus a poor prognosis after the resuscitation. Simulation results show that the proposed algorithm outperforms conventional methods in terms of several commonly used performance indices; meanwhile, the proposed analysis also well matches the practical experimental results.

Massive multiuser multiple-input multiple-output (MU-MIMO) systems are expected to be the core technology in fifth-generation wireless systems because they significantly improve spectral efficiency. However, the requirement for a large number of radio frequency (RF) chains results in high hardware costs and power consumption, which obstruct the commercial deployment of massive MIMO systems. A potential solution is to use low-resolution digital-to-analog converters (DAC)/analog-to-digital converters for each antenna and RF chain. However, using low-resolution DACs at the transmit side directly limits the degree of freedom of output signals and thus poses a challenge to the precoding design. In this paper, we develop efficient and universal algorithms for a downlink massive MU-MIMO system with finite-alphabet precodings. Our algorithms are developed based on the alternating direction method of multipliers (ADMM) framework. The original ADMM does not converge in a nonlinear discrete optimization problem. The primary cause of this problem is that the alternating (update) directions in ADMM on one side are biased, and those on the other side are unbiased. By making the two updates consistent in an unbiased manner, we develop two algorithms called iterative discrete estimation (IDE) and IDE2. IDE demonstrates excellent performance and IDE2 possesses a significantly low computational complexity. Compared with state-of-the-art techniques, the proposed precoding algorithms present significant advantages in performance and computational complexity.

A receiver architecture with low-resolution analog-to-digital converters (ADCs) coupled with large antenna arrays has drawn considerable interest in the millimeter wave (mm-wave) system. Although architecture with pure one-bit ADCs has low power cost, such a system presents many challenges for synchronization, channel estimation, and power level estimation. Research has been conducted recently on the so-called mixed one-bit system, in which most antennas are equipped with one-bit ADCs, and a few have high-resolution ADCs. Despite the advantages of this system, studies on how to efficiently use high-resolution outputs to aid the one-bit system are lacking. This research considers the channel estimation problem to fill this gap and proposes a two-step channel estimator by utilizing the different features of mixed outputs. The channel gain can be extracted from high-resolution ADCs, and the channel angle can be extracted by the combination of additional one-bit ADCs. The proposed estimator leverages the sparsity feature of the channel in mm-wave. In contrast to previous works that used compressive sensing techniques, which confine the estimate to the set of grid angle points and induce estimation bias, the proposed estimator is gridless and treats the angle as a continuous parameter. The simulation results demonstrate that the proposed method yields significantly lower mean square errors than the conventional maximum likelihood estimator. In addition, this paper investigates ways to further improve the channel estimate by arranging the locations of high-resolution ADCs. Several useful observations on system design are obtained through our analysis.

In this paper, we propose a MIMO precoding transceiver to achieve data secrecy at the physical layer. The proposed system exploits the Restricted Isometry Property (RIP) widely used in compressive sensing to encrypt the data. Because the channels at the legitimate receiver and the eavesdropper are inherently different, the corresponding sensing matrices are different. Thus the eavesdropper cannot decode the data successfully. More specifically, when full channel state information (CSI) at the legitimate receiver is known to the transmitter, the proposed precoder can simultaneously maximize receive SNR and attain secrecy. Simulation results corroborate theoretical results, and show that the proposed system enjoys different advantages when different recovery algorithms are used.

This study investigates how to quantize the masked beamforming systems to maximize the secrecy rate for MISOSE (multiple-input, single-output, single-eavesdropper) channels and assume that this eavesdropper equipped with only single antenna, where only partial channel state information (CSI) at the legitimate receiver is available to the transmitter. In this case, the artificial noise (AN) leaks to the legitimate receiver due to CSI quantization. In the literature, all quantization bits are used to quantize the beamforming vector. Then the null space of this quantized beamforming vector is used to transmit the AN. We find that such quantization schemes can result in serious interference at the legitimate receiver. To overcome this issue, we propose that the beamforming vector and the AN vector should be quantized separately, where the beamforming vector should be selected from a codebook to maximize the beamforming gain and the AN vector should be selected from another codebook to minimize the leakage (or interference). Theoretical results show that separate quantization can significantly reduce the AN leakage at the legitimate receiver. Furthermore, based on the proposed quantization scheme, we show how to allocate bits to separately quantize the beamforming vector and the AN vector to maximize the secrecy rate. By using the proposed quantization and bit allocation schemes, the secrecy rates of masked beamforming systems can be improved compared to the conventional quantization schemes. Simulation results corroborate the theoretical results.

In this paper, we propose an MIMO precoding and postcoding transceiver to achieve data secrecy at the physical layer. When full channel state information (CSI) of the legitimate receiver is known to the transmitter, the proposed precoder can simultaneously maximize to receive signal-to-noise ratio (SNR) and attain secrecy. When only partial CSI is available, a modified Lloyd algorithm is proposed to construct codebooks for quantizing the precoder. As will be explained in this paper, the use of the proposed codebooks does not affect the secrecy of the proposed system. In addition, we propose a low-complexity postcoder to compensate the SNR loss when full CSI is not available. Furthermore, the performance of the proposed system is analyzed from the view points of both recovery rate and secrecy. Based on the analyzed results, we show how to achieve full recovery rate and perfect secrecy for the proposed system. Finally, simulation results corroborate the theoretical results, and show that the proposed system enjoys different advantages when different recovery algorithms are used.

We propose a training sequence that can be used at the handshaking stage for multi-cell networks. The proposed sequence is theoretically proved to enjoy several nice properties including constant amplitude, zero autocorrelation, and orthogonality in multipath channels. Moreover, the analytical results show that the proposed sequence can greatly reduce the multi-cell interference (MCI) induced by carrier frequency offset (CFO) to a negligible level. Therefore, the CFO estimation algorithms designed for single-user or single-cell environments can be slightly modified, and applied in multi-cell environments; an example is given for showing how to modify the estimation algorithms. Consequently, the computational complexity can be dramatically reduced. Simulation results show that the proposed sequences and the CFO estimation algorithms outperform conventional schemes in multi-cell environments.

Beamforming (or precoding) techniques have been widely adopted in modern MIMO OFDM systems. Using beamforming can significantly improve the receive SNR of OFDM systems. However, the combination of transmit signals after beamforming deteriorates the peak-to-average power ratio (PAPR), which has long been considered a major issue of OFDM systems. High PAPR not only complicates the design of the power amplifier, but also increases power consumption. In this paper, we theoretically analyze the PAPR performance of MIMO OFDM systems that adopt either one of the two popular beamforming schemes, i.e. MRT (maximum ratio transmission) and EGT (equal gain transmission). The analysis considers different numbers of channel taps after sampling. The results may provide important reference for practical designs when evaluating the required power amplifiers and power consumption. Moreover, the theoretical results show that MRT OFDM systems generally perform much worse than EGT OFDM systems in terms of PAPR. Furthermore, motivated from the derived results, PAPR reduction algorithms are proposed for both MRT OFDM and EGT OFDM systems. It is worth to mention that for MRT OFDM systems, the proposed algorithm can improve both PAPR and bit error rate; for EGT OFDM systems, the proposed algorithm improves PAPR while it only slightly degrades bit error rate.

We propose a Sub-Nyquist sampling scheme using only one mixer, one filter bank with M subfilters and M low rate ADCs. Compared to the MWC (Modulated Wideband Converter) system in [1], the proposed sampling system has several complexity advantages. First, due to the use of only one mixer, the hardware complexity for analog circuits is significantly reduced. Moreover, the system only needs one PN sequence and thus the search complexity for the best sequence is greatly reduced. The use of the best sequence enables the proposed system to achieve a smaller mean square error (MSE) between the original and the reconstructed signals than that of the MWC system. Simulation results are provided to show the performance superiority of the proposed system in terms of MSE and recovery percentage.

This paper proposes an orthogonal matching pursuit (OMP-) based recovering algorithm for compressive sensing problems. This algorithm can significantly improve recovering performance while it can still maintain reasonable computational complexity. Complexity analysis and simulation results are provided for the proposed algorithm and compared with other popular recovering schemes. We observe that the proposed algorithm can significantly improve the exact recovering performance compared to the OMP scheme. Moreover, in the cases with high compressed ratio, the proposed algorithm can even outperform the benchmark performance achieved by the subspace programming and linear programming.

In this work a sphere decoder with low complexity is proposed and implemented. We propose a simplified norm algorithm, which is called admissible set elimination (ASE), to dramatically decrease the number of searching nodes. In addition, the decoder uses table-look-up to acquire the enumeration order of different constellations. As a result, the critical path is shortened and the throughput is enhanced. Compared to the optimal ML detector, the proposed scheme greatly improves the complexity and throughput, while the performance only degrades around 0.5 dB. The proposed scheme is fabricated by a TSMC 90 nm process. The area is 0.85 mm2, and the average throughput can be up to 411.3 Mbps when the clock rate is 108.7 MHz.

This paper presents an multipath delay commutator (MDC)-based architecture and memory scheduling to implement fast Fourier transform (FFT) processors for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems with variable length. Based on the MDC architecture, we propose to use radix-Nsbutterflies at each stage, whereNsis the number of data streams, so that there is only one butterfly needed in each stage. Consequently, a 100% utilization rate in computational elements is achieved. Moreover, thanks to the simple control mechanism of the MDC, we propose simple memory scheduling methods for input data and output bit/set-reversing, which again results in a full utilization rate in memory usage. Since the memory requirements usually dominate the die area of FFT/inverse fast Fourier transform (IFFT) processors, the proposed scheme can effectively reduce the memory size and thus the die area as well. Furthermore, to apply the proposed scheme in practical applications, we letNs=4 and implement a 4-stream FFT/IFFT processor with variable length including 2048, 1024, 512, and 128 for MIMO-OFDM systems. This processor can be used in IEEE 802.16 WiMAX and 3GPP long term evolution applications. The processor was implemented with an UMC 90-nm CMOS technology with a core area of 3.1 mm2. The power consumption at 40 MHz was 63.72/62.92/57.51/51.69 mW for 2048/1024/512/128-FFT, respectively in the post-layout simulation. Finally, we analyze the complexity and performance of the implemented processor and compare it with other processors. The results show advantages of the proposed scheme in terms of area and power consumption.

The adoption of equal gain transmission (EGT) in MIMO systems can greatly reduce the computational complexity and the design effort of power amplifier. In a temporally correlated channel environment, the channel varies slowly and so does the corresponding beamforming vector. If the slowly varying channel condition can be taken into consideration while designing the feedback schemes of EGT, the computational complexity and feedback overhead of EGT are expected to be further diminished dramatically. In this paper, we propose a differential feedback scheme (DFS) with two algorithms to further improve the performance in EGT. Simulation results show the advantages of the proposed DFS in terms of the feedback overhead and the system performance.

In this paper, we investigate multimode antenna selection for zero forcing receiver to maximize the overall data rate. The optimal selection scenario can be achieved by exhaustive search. However, antenna selection using exhaustive search leads to complicated computational burden. To reduce the complexity, we propose a greedy search algorithm for antenna selection. Using the proposed algorithm, the computations can be greatly reduced while the achievable data rate is nearly the same with exhaustive search. Moreover, generally fixed bit budgets are used in practical design. Hence, we propose to use water-filling bit allocation to further improve the performance of the proposed antenna selection scheme. Simulation results are provided to show the advantages of the proposed multimode antenna selection with bit allocation.

This paper proposes a codebook design that considers transmit antenna selection and beamforming simultaneously. Under the same amount of feedback bits, the proposed codebook can reduce the number of RF units more than half while its performance is still maintained closely (within 0.1 dB loss) to the codebook designs that use all transmit antennas. Since RF units are expensive, the proposed codebook can greatly reduce the hardware implementational cost with slight performance degradation. Moreover, we analyze the proposed codebook and derive a lower bound of symbol error rate (SER). The derived SER lower bound generally matches the simulation results. Simulation results show the proposed codebook with around half active transmit antennas performs comparable to the Lloyd codebook design with full active antennas.

We design equal gain precoders with scalar quantization in MIMO systems with limited feedback, which do not require the predefined codebook. In such precoders, bit allocation can be used to quantize the precoding vector/matrix to further improve the system performance. There are two conventional bit allocation schemes. One is the uniform bit allocation and the other is the optimal bit allocation obtained by using exhaustive search. The uniform bit allocation is simple but turns out to have obvious performance degradation. On the other hand, the exhaustive search method leads to the optimal performance. However, its computational complexity is extremely high and may be somewhat impractical to be realized. In this paper, we propose two bit allocation schemes for the MIMO equal gain precoder with scalar quantization. Both of the proposed methods are with low complexity and their performance is close to the optimal scheme. Consequently, the proposed bit allocations provide good trade-off between performance and complexity.

The beamforming vectors of an equal gain transmission (EGT) contains phase information only and thereby enjoys several implementational advantages when compared to the optimal scheme, i.e. maximum ratio transmission (MRT). The implementational advantages make EGT a promising solution for simple transceiver design while offering a performance comparable to that of MRT. This solution motivates us to explore how close the performance can be between EGT and MRT. The maximum SNR loss between EGT and MRT is known to be 1.05 dB in MISO channels. However, little is known about the SNR loss in MIMO channels, since no closed-form solution is available for the best EGT in MIMO channels. In this work, a suboptimal closed-form EGT design for MIMO channels is proposed and its performance is analyzed. Interestingly, the maximum SNR loss between the proposed EGT and the MRT (both employing MRC in receiver) in MIMO channels is shown to be approximately 1.05 dB as well. Moreover, instead of applying conventional all transmit antennas, this study proposes to adopt antenna selection, to further improve the performance of EGT. Two antenna selection algorithms are proposed and the corresponding performance is analyzed. When the proposed antenna selection algorithms are applied to EGT, the SNR loss between EGT and MRT can be reduced to as low as 0.45-0.65 dB, with the numbers of transmit antennas ranging from 4 to 8. One of the proposals with fixed number of transmit antennas not only outperforms conventional EGT but also requires fewer number of RF (radio frequency) components; also, it employs constant power in each transmit antenna like EGT does. As a result, hardware complexity can be reduced by this proposal. Furthermore, design strategies to apply the proposed EGT and antenna selection algorithms in systems with limited feedback are also suggested.

In this paper, we propose a low-complexity transmit antenna selection algorithm for MIMO multiuser channels. This algorithm greedily finds the optimal transmit antenna subset in base station. When the transmit power for individual data streams is equal, the proposed algorithm can be applied to the zero-forcing and the MMSE precoding schemes to further simplify the computational complexity. Simulation results show that the proposed low-complexity suboptimal antenna selections can achieve very close performance with the optimal antenna selection scheme in. As a result, a good trade-off between complexity and performance is attained by the proposed antenna selection schemes.

The maximum SNR loss between equal gain transmission (EGT) and the optimal scheme, i.e. maximum ratio transmission (MRT) is 1:05 dB in MISO channels. However, little is known about the performance loss of EGT in MIMO channels, since there is no simple closed-form solution available for the best EGT in MIMO channels. In this study, an EGT design for MIMO channels is proposed and its performance is analyzed theoretically. Interestingly, the SNR loss between the proposed EGT and the MRT in MIMO channels is shown to be at most 1:05 dB as well. Unlike the optimal MRT, which requires both magnitude and phase information of channels, EGT only needs phase information and hence has several implementional advantages compared to the MRT; that is, it can greatly relax the effort of power amplifier and computational complexity. Consequently, simple transceiver design can be realized using the proposed EGT with small performance degradation.

We consider the design of the equal gain precoder in flat-fading and limited feedback multiple-input multiple-output (MIMO) systems. The advantage of the equal gain precoder based on the scalar quantization (SQ) is that it is easier to encode the feedback information and no need to construct codebook in advance. In the sort of precoder, the bit allocation methods will dominate the system performance. In this paper, we explore an efficient bit allocation algorithm for the scalar quantized equal gain precoder. The proposed bit allocation scheme can improve the performance of the straightforward scalar quantized equal gain precoder. Compared to the optimal bit allocation scheme with exhaustive search, the proposed algorithm greatly reduces the computational complexity while the performance is near to the optimal scheme.

We investigate the use of lattices in phase quantizers and equal gain beamformers. We propose proper scaling methods so that the Voronoi regions of all lattice points are regular in the phase quantizers. As a result, existing fast algorithm for various lattices can be applied directly. In addition, we derive the number B of quantization bits with the proposed scaling. We found that the proposed scaling leads to efficient quantization bits. Some of the proposed scaling can even achieve integer B. Moreover, we analyze the mean square error for the proposed phase quantizers. Simulation results corroborate the theoretical results.

A precoding technique applied to symbols transmitted in an ultra-wideband (UWB) system can concentrate the signal power at the receiver for a higher data detection rate. The codeword design problem for optimal UWB precoding is investigated in this work. After the problem formulation, we examine its solution in the full-rank and the reduced-rank subspaces to obtain different tradeoffs between the detection performance and the amount of feedback messages. Furthermore, we propose two subspace selection schemes, each of which corresponds to a code design method. The resultant optimal codewords improve the performance of previous work significantly at the expense of a slightly increased amount of feedback messages. Finally, the performance of a precoded UWB system with various codewords is compared via computer simulation.

We propose a simple search algorithm to reduce the computational complexity and power consumption for equal gain precoder. Simulation results show that the equal gain precoder with the proposed search algorithm can achieve nearly the same performance as that of the Grassmannian precoder. The proposed search algorithm shows several advantages in practical implementation as will be explained later in this paper.

There have been many results on designing transceivers for MIMO channels. In early results, the transceiver is designed for a given bit allocation. In this paper we will jointly design the transceiver and bit allocation for maximizing bit rate. By using a high bit rate assumption, we will see that the optimal transceiver and bit allocation can be obtained in a closed form using simple Hadamard inequality and the Poincare separation theorem. In the simulation, we will demonstrate the usefulness of the joint design. Simulation results, in which a high bit rate assumption is not used in allocating bits, show that a higher bit rate can be achieved compared to previously reported methods.

We propose to use the Rake receiver to improve the system performance by collecting more signal power in an ultra-wideband (UWB) system precoded by the channel phase information, called the CPP-UWB system [1], in this work. The performance of the proposed system is determined by the selected channel taps and the corresponding combining schemes for those Rake fingers. Three combing schemes, Le., the maximum ratio combining (MRC), the minimized mean square error (MMSE) and the zero-forcing (ZF), are proposed. When the MRC scheme is applied to the Rake receiver with M fingers, M channel taps can be easily found to maximize the averaged signal power at the receiver output by exploiting the property of the channel- phase-precoded channel. It is shown by simulation results that the proposed scheme significantly outperforms the preceding system using one matched filter as proposed in [1].

We propose to use carrier interferometry (CI) codes to reduce the complexity of the maximum- likelihood multiuser-detection (ML-MUD) receiver in MC-CDMA systems with carrier frequency offsets (CFOs). Complexity reduction is achieved by exploiting the sparsity of the cross-correlation matrix of CI codes. It is shown that, for a fully-loaded MC-CDMA with spread gain N over a multipath channel with length L, the complexity of the ML-MUD receiver grows exponentially with 2L-1 (instead of N), where L << N in a typical setting. Moreover, an upper bound for the minimum error probability is derived. Finally, simulation results are given to corroborate theoretical results.

The channel phase precoding (CPP) technique was recently proposed for the ultra-wideband (UWB) communication system in [1] to save the feedback overhead and the computational complexity as compared with the time-reversal prefllter (TRP) technique [2]. Two ideal assumptions have been made in both systems; namely, the availability of accurate channel information and perfect synchronization of transmitted pulses. In this work, we examine the impact of timing jitter on the performance of TRP and CPP and show that the CPP-UWB system is more robust against the timing jitter than the TRP- UWB svstem.

The performance of a multiuser orthogonal frequency-division multiplexing (OFDM) transceiver, called the precoded multiuser OFDM (PMU-OFDM), with time asynchronous access is investigated in this paper. It is shown that multiaccess interference (MAI) due to time asynchronism is asymptotically zero as the number of parallel input symbolsNbecomes large in the PMU-OFDM system. Then, we show that PMU-OFDM with even or odd Hadamard-Walsh codewords can significantly suppress the MAI effect due to time asynchronism. For the half-loaded case, no sophisticated signal processing technique is needed by PMU-OFDM for MAI suppression. For the fully loaded situation, PMU-OFDM demands less complexity than does conventional OFDMA for interference suppression since PMU-OFDM deals with only one half of interferers.

A preceding technique applied to symbols transmitted in an ultra-wideband (UWB) system was proposed, which can concentrate the signal power at the receiver to facilitate data detection. The design of the optimal codeword for preceding is investigated in this work. After the problem is formulated, we examine its solution in the full-rank space as well as the reduced-rank subspace to get different tradeoff between detection performance and the amount of feedback messages. Furthermore, we propose two subspace selection schemes that are suitable for different channel conditions. The codeword obtained from this work improves the performance of the result significantly at the price of a larger amount of feedback messages.

The performance of MC-CDMA in the presence of carrier frequency offsets (CFO) can be severely degraded due to multiaccess interference (MAI). It is shown in this work that a properly chosen subset of real Hadamard-Walsh or exponential codes can achieve zero MAI in a CFO environment. For a channel of length L and a number G of power of 2 with G ges L, we prove that 1 + log2(iV/G) Hadamard-Walsh codewords or N/G exponential codewords can achieve zero MAI under any CFO level. Simulation results are given to corroborate derived theoretical results and evaluate the performance of MC-CDMA with a variable number of users in a CFO environment.

The optimal codeword length that generates the maximum output signal to interference ratio (SIR) for the channel-phase-precoded ultra-wideband (CPPUWB) system proposed in [1] is studied in this work to mitigate the interference in high data rate transmission. As compared with the proposed MMSE receiver in [1], this code length optimization technique demands no additional training symbols, which sacrifices the actual data rate, and maintains a simple receiver structure. This optimization problem is highly nonlinear in nature, and a fast search algorithm is developed to speed up the optimization process. The signal to interference plus noise ratio (SINR) performance of the proposed scheme degrades slightly when the input SNR is low, and it will converge to the maximum SINR as the input SNR becomes large.

A new indoor data communication scheme called the channel phase preceded ultra wideband (CPPUWB) system is proposed in this work. The proposed CPPUWB system is efficient in computational power saving by encoding the transmit symbol with a channelized codeword. The channelized codeword is determined by the channel phase information that is estimated at the receiver and then fed back to the transmitter. A method to estimate the channel phase information using training symbols is presented. For a given number of training symbols, we derive a lower bound for the average output SNR, which can be used to evaluate the system performance. Finally, an MMSE receiver is proposed to suppress the residual intersymbol interference (ISI) for the high data rate scenario

The performance of a multiuser OFDM transceiver, proposed by authors in S.H. Tsai, et al. (2004) and called the preceded multiuser OFDM (PMU-OFDM) in this work, with time asynchronous access is investigated. We show that the multiaccess interference (MAI) due to time asynchronous access can be reduced to a negligible amount by adopting a proper precoder that uses M/2 symmetric or M/2 anti-symmetric codewords of the M Hadamard-Walsh codes for the preceding task. The PMU-OFDM transceiver with the same precoder was shown to have approximately MAI-free property in a frequency asynchronous environment (S.H. Tsai, et al., 2005). Here, we analyze the approximately MAI-free property of the PMU-OFDM system with respect to time asynchronism. Finally, computer simulation is performed to confirm the derived property.

A scheme to eliminate completely the multiaccess interference (MAI) of the multicarrier code division multiple access (MC-CDMA) system in a frequency-selective fading environment using codeword selection is proposed. We show that, if the CDMA codewords are chosen to be a proper subset of the Hadamard-Walsh codes, the MC-CDMA system can be MAI-free so that the implementational complexity of the transceiver can be greatly reduced. This MAI-free property is analyzed theoretically and demonstrated via simulation results.

A maximum likelihood estimator (MLE) that jointly estimates the carrier frequency offset (CFO) and the channel response of each user in uplink OFDMA systems is investigated in this research. The proposed MLE distinguishes itself from existing methods by its applicability to more flexible carrier assignment schemes. It achieves high computational efficiency by transforming a multidimensional optimization problem into a one-dimensional optimization problem. A suboptimal method is developed to further reduce the computational complexity. It is demonstrated by simulation results that the proposed MLE can provide accurate CFO and channel estimation in both SISO and SIMO environments.

Abstract — A novel full rate space-time block code that diagonalizes the channel matrix without the channel knowledge at the transmitter is proposed in this research. The main advantage of channel diagonalization is that it allows a simple detection scheme at the receiver end. Our method for channel matrix diagonalization consists of two steps. First, we propose a code structure that can transform the channel matrix to a circulant one. Then, the circulant channel matrix can be diagonalized using DFT and IDFT in the transmitter and the receiver, respectively. The proposed scheme not only diagonalizes any channel matrix with an even number of transmit antennas, but also guarantees that the real and imaginary parts of each received symbol are attenuated equally. Simulation results show that the proposed code structure outperforms the G4 code about 2 dB in an environment of four transmit and four receive antennas with the bit error probability equal to 10−6. Keywords — Space-time block code (STBC), full rate, channel diagonalization and MIMO systems. I.

A new adaptation algorithm, to equalize the symbol error rates over all subchannels, is proposed, to overcome the slow time-varying noise environment existing in xDSL channels. The proposed algorithm combines the conventional bit swap algorithm with power gain adaptation. The overall transmission power before and after adjustment is kept the same. Simulation results demonstrate that the proposed algorithm has a faster convergence speed and a smaller dynamic range of the equalized noise variance than the bit swap algorithm.

This work proposes an approximately MAI-free OFDM transceiver, called the repetitively coded multicarrier CDMA (RCMC-CDMA), which uses the CDMA technique to separate different users so that every user can utilize the whole bandwidth and time slot simultaneously. We derive the capacity of the proposed system with multiple receive antennas, and then compare it to the capacity of the generalized MC-CDMA system. Furthermore, the proposal system is robust to timing mismatch between different users so that it is suitable for uplink transmission. Finally, we show that the system has a great potential to mitigate the carrier frequency offset (CFO) problem.