This paper proposes an extremely large-scale movable antenna array (XL-MAA) enabled hybrid beamforming scheme for spatially non-stationary (SnS) channels. Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are expected to be one of the promising technologies in future wireless cellular and Internet-of-Things (IoT) networks. However, due to the enlarged aperture size, XL-MIMO systems experience new types of channel characteristics as near-field propagation and spatial non-stationarity. In order to deal with the channel impairments and variations, movable antenna array (MAA) is envisioned as a new promising tool to overcome the limitations of fixed-position arrays. In this paper, we propose a hybrid beamforming framework that leverages an MAA to enhance downlink multi-user communication in near-field SnS channels. By dynamically optimizing antenna element positions, the proposed MAA system flexibly adapts to user-specific visibility regions, achieving improved channel favorability and system performance. We formulate a joint optimization problem over the hybrid precoder and antenna positions, aiming to maximize the sum-rate under practical hardware constraints, including constant-modulus analog beamforming and inter-element spacing limits of MAA elements. To address the resulting non-convexity, a genetic algorithm (GA)-based optimization is developed to incorporate structure-preserving chromosome initialization, constraint-aware crossover and mutation, and efficient fitness evaluation based on zero-forcing precoding. Lastly, a low-complexity fitness evaluation scheme with quantized feedback scheme is devised in case of limited feedback capacity of IoT devices. Simulation results demonstrate that the proposed XL-MAA architecture significantly outperforms conventional fixed uniform linear arrays and other MAA design schemes in challenging SnS environments, highlighting the potential of MA-driven spatial reconfiguration for 6G wireless systems.

Movable antenna (MA) is a new technology capable of dynamically adjusting antenna positions, allowing the full exploitation of spatial degrees of freedom (DoFs). The successful realization of the spatial potential from MA systems critically depends on precise knowledge of channel components. However, existing MA channel estimation methods are primarily concerned with identifying these components from observations at arbitrarily placed MAs, without providing an efficient strategy for MA position optimization. Furthermore, conventional gridless refinement methods suffer from high computational complexity and lack explicit parameter pairing, limiting estimation accuracy. To overcome these issues, this paper proposes a learning-driven framework that jointly optimizes MA positions and the channel estimation function in MA-aided wideband systems. The core principle involves decomposing the system model into operations analogous to those in a deep neural network layer, wherein a non-linear function is applied after matrix multiplication. A neural network is trained to emulate this decomposed model. After training, the network is partitioned into three modules representing the optimized Tx-MA positions, Rx-MA positions, and the channel parameter estimation function, which collectively enable the estimation of paired channel angles and delays. In addition, a low-complexity refinement algorithm, termed stepwise alternating optimization (SWAO), is introduced to sequentially refine the initial angle and delay estimates. Simulation results confirm the superiority of the proposed method in estimation accuracy and computational complexity.

Terahertz (THz) communication systems are promising solutions for meeting the rapidly growing data traffic demand driven by an increasing number of Internet of Things (IoT) devices. Despite the potential of THz communication systems, they have the disadvantage of extremely high beam training overhead. Hierarchical beam training has been proposed as a technique to reduce this overhead. To perform beam alignment successfully while reducing overhead, it is important to design a wide beam, i.e., a low-resolution beam identical to the ideal beam, which has the shape of a rectangular function. While several conventional wide beams, obtained by solving optimization problems, closely approximate the ideal beam, it remains unclear whether designing the beam to closely resemble the ideal beam, without achieving an identical match, is truly effective in reducing beam misalignment. Motivated by this, we explore the feasibility of designing a beam identical to the ideal beam pattern and evaluate whether closely resembling the ideal beam pattern is effective in mitigating beam misalignment. Furthermore, we propose a novel codebook design scheme based on second-order cone programming (SOCP) for THz ultra-massive multiple input multiple output (UM-MIMO) systems to reduce beam misalignment in hierarchical beam training. This scheme introduces two additional criteria to the conventional codebook design problem, specifically addressing the considerations of worst-case detection gain and coverage-edge gain, which are directly aimed at mitigating beam misalignment. Additionally, we present a low-complexity codebook design approach for real-time implementation. Numerical results demonstrate that the proposed codebook reduces beam misalignment effectively.

Although rate-splitting multiple access (RSMA) has been widely investigated as one of the promising candidate schemes for 6-th generation wireless communication systems, detailed solutions on hybrid beamforming for RSMA have not been revealed so far. Given the situation that a number of commercial wireless networks are adopting massive MIMO systems across low to high frequency bands, a hybrid beamforming framework needs to be identified to enable RSMA in real networks. In this regard, we suggest a practical hybrid beamforming for RSMA comprising beam optimization procedures consisting of two phases. In the first phase, two-stage learning framework to design the RF precoder for RSMA systems is proposed. The first stage adopts supervised learning for private message transmission, while the second stage employs an unsupervised learning approach for common message transmission, utilizing a customized loss function to address a max-min fairness (MMF) criterion in the absence of a closed-form solution.In the second phase, baseband precoders for common and private messages can be derived based on the different criteria, and transmission power is properly allocated to common and private messages. The proposed hybrid beamforming framework does not require more feedback than conventional multiple access schemes, despite supporting common message transmission. Numerical results show that the proposed scheme guarantees high spectral and energy efficiency, even in the presence of imperfect channel state information (CSI) in both analog and digital domains.

Accurate channel state information is fundamental for fully unleashing the potential of millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, two primary challenges hinder its acquisition. The first challenge is the insufficient research on hybrid beamformer design during the channel estimation period, contrasted with the extensive focus on the data transmission period. Secondly, conventional channel estimation schemes overlook practical effects present in mmWave wideband systems, namely beam squint. To tackle these obstacles, this paper presents a learning-aided joint optimization framework for beamformers and the geometric parameter estimation function, customized for specific environments. Inspired by the analogous operations between a neural network and a reshaped signal model, the early stages of the network are trained to emulate the reshaped signal model. After completing training, the early weight matrices are extracted to shape the beamformers, while the remainder constitutes the function. Essentially, joint optimization is accomplished by decomposing a single trained network into multiple components under specific constraints. Following the initial search for geometric parameters facilitated by these components, they undergo beam squint-specialized refinement for precise channel reconstruction. Numerical results demonstrate the adaptability of the proposed beamformers to the environment through heightened effective SNR levels. Furthermore, the superior performance of the proposed method over existing methods in channel estimation is proven.

Movable antenna (MA) is an emerging technology with promising potential to enhance communication quality by dynamically adjusting antenna positions to locations with favorable channel conditions. The successful exploitation of spatial selectivity in MA systems depends on the precise knowledge of channel components, such as channel angles and gains. However, current state-of-the-art methods primarily focus on estimating channel components based on measurements from arbitrary MA positions, lacking a theoretical methodology for determining optimal MA positions during the channel estimation period. Additionally, these methods often adopt on-grid approaches, which suffer from inherent resolution limitations. This paper presents a new framework for the joint optimization of MA positions and the channel estimation function. A learning-aided approach driven by a deep neural network is employed. The key to accomplishing joint optimization lies in decomposing the received pilot model into operations identical to those in a neural network layer, where a specific non-linear function is applied after matrix multiplication. The neural network is trained to imitate this decomposed model. Upon completion of training, the entire trained network is divided into optimization solutions: MA positions and a channel angle estimation function. Subsequently, the angles obtained from the network modules are refined in a gridless manner through the proposed MA-alternating minimization refinement (MA-AMR), which are then used to derive channel gains. Numerical results demonstrate that the proposed method surpasses existing methods in terms of estimation accuracy and computational efficiency.

In this article, the millimeter wave (mmWave) cellular-connected unmanned aerial vehicle (UAV) equipped with conformal phased array antennas in cylindrical shape is investigated, where the UAV acts as an aerial user and served by the base station (BS). Although the cylindrical conformal array (CCA)-enabled UAV can achieve the full spatial coverage and fully exploit the beam gain with directional antenna elements, the curved carrier of CA forces the cellular-connected UAV to activate only a portion of antenna elements rather than activating the entire of them to mitigate the severe aerial-ground interference from other BSs. The subarray selection problem is formulated to maximize signal-to-interference-plus-noise ratio (SINR) with non-linear antenna radiation patterns. To handle the non-linearity of objective problem, we propose a low complexity subarray selection scheme based on genetic algorithm (GA), which utilizes only the information that UAV can acquire in practical scenarios. The simulation results demonstrate that the proposed subarray selection scheme always shows higher SINR and achievable rate compared to the benchmark schemes. Specifically, the proposed subarray selection scheme shows an achievable rate that is about 2% higher than the accelerated particle swarm optimization (APSO) algorithm, and its computational complexity is only about 25%. In addition, the proposed scheme has near optimal performance while having only about 10.4% complexity compared to the optimal subarray obtained through the full search.

Accurate channel state information is paramount for fully harnessing the benefits of massive antennas in communication systems. However, two primary challenges impede its acquisition. Firstly, conventional far-field assumption-based channel estimation schemes become impractical in near-field dominant future communication systems. In contrast to the far-field assumption, which relies solely on angle-dependent channels, the near-field introduces location-dependency. The second challenge involves limited research on hybrid beamformer design during the channel estimation period, unlike the extensive focus on the data transmission period. To overcome these hurdles, this paper presents a neural network-aided joint optimization of the beamformer and localization function for near-field channel estimation, customized to the specific environment. Inspired by the similarity between operations in a neural network and a signal model, the initial network weights emulate the beamforming matrix during training. Subsequently, these weights are extracted for beamformer design, while the remainder of the network serves as the localization function. Following localization, the location parameters undergo refinement, paving the way for precise channel reconstruction. Unlike prevailing near-field channel estimation methods that solely exploit range information from array response, our approach additionally leverages range-dependent frequency selectivity characteristics. Simulation results prove the adaptability of the proposed beamformer to given environments and demonstrate the superior performance of the proposed method in channel estimation.

We propose a low-complexity hybrid beamforming scheme with fast beam training based on collaborative learning to improve energy efficiency in multiuser millimeter wave (mmWave) systems. First, we address the non-stationary multi-armed bandit (MAB) problem by considering the codeword index and signal-to-noise ratio (SNR) as the action and reward, respectively, for beam training in time-varying channels. We develop a beam selection algorithm called the collaborative upper confidence bound (UCB) policy by clustering users according to their preferences for each codeword, using neural collaborative filtering. Second, we formulate a nonlinear least-squares problem using feedback SNR values to predict the beam gains for the unirradiated codewords. We then assign the radio frequency (RF) precoder using codewords with high expected beam gains for each user. Once the RF precoder is assigned, we estimate the equivalent baseband channel by receiving feedback on its direction information. Finally, we design the baseband precoder based on the acquired equivalent baseband channels. Simulation results demonstrate that the proposed hybrid beamforming scheme with fast beam training improves energy efficiency by reducing beam training overhead and increasing beam searching accuracy.

In this paper, we propose a technique by exploiting the two-stage convolutional neural network (CNN) to improve the ranging accuracy regardless of the pulse shape of the received signal in the ultra-wideband (UWB) system. UWB system could be degraded localization accuracy according to pulse shapes between transceiver. In particular, the capability of the receiver to detect a small signal level has been improved recently, and pulse shape compatibility will become even more important for UWB devices. There are no mandatory specifications or interoperability testing, as of now, so that future compatibility issues can exacerbate positioning accuracy. The proposed method shows that precise ranging even if an unspecified pulse shape-based signal is received.

We propose a beamforming scheme called dynamic energy beamforming with a frequency diverse array (DEB-FDA) for enhancing the energy harvesting performance of multiple Internet of Things (IoT) devices in radio frequency-based wireless power transfer systems. We adopt the frequency diverse array (FDA) architecture, where the steering vector is a function of the distance and angle, to synthesize beam patterns that depend on both these parameters. We formulate an optimization problem for designing the transmit-beamforming vector and the frequency components of the FDA so that constructive interference occurs simultaneously at the locations of multiple IoT devices. We then develop an algorithm for dynamic energy beamforming based on alternating optimization using the minorize-maximization algorithm. Simulation results show that the proposed DEB-FDA scheme improves the energy harvesting performance of IoT devices in multiple locations.

Although intelligent reflecting surface (IRS) is regarded as a promising solution to enhance the efficiency of wireless energy transfer (WET), the acquiring of channel state information is a crucial challenge for the system in which a training sequence for channel estimation is sent by low-power Internet-of-Things (IoT) devices. In this article, an IRS-aided multidevice WET system is considered. To overcome the limitation in channel estimation, we propose a received power-based channel estimation scheme that can be easily implemented and scalable in wirelessly empowered IoT devices. Specifically, at every single time slot, each device measures the received power of a randomly generated radio-frequency signal and feeds it back to the transmitter. We formulate a channel estimation problem to use the history of received power measurements based on the maximum-likelihood estimation using the phase retrieval framework and temporal channel evolution model. Moreover, we propose an algorithm that can be employed to obtain the stationary solution for the channel estimation problem, which is based on the inexact block coordinate descent method. We also perform algorithm modification to deal with the special case in which the transmitter-IRS channel is available. The simulation results show that the performance of the proposed algorithm approaches the upper bound as the channel slowly changes, although the proposed channel estimation protocol requires only one scalar value feedback.

We propose a time synchronization method which is applicable to both offset quadrature amplitude modulation based filter-bank multicarrier (FBMC-OQAM) system and quadrature amplitude modulation based filter-bank multicarrier (FBMC-QAM) system. Based on the relation between the transmitted data symbols and FBMC signal, we design the scattered symbols to make a specific time domain region empty. Then, we insert the preamble in that region so as to transmit it perfectly. The performance of the proposed synchronization method is compared with the conventional time synchronization methods for the FBMC systems in terms of root mean square error (RMSE) of symbol timing offset (STO), bit error ratio (BER) and correctly detected data bit ratio per frame. Since the proposed method can reduce the resource usage for the synchronization, the resources for data transmission can be increased, thereby improving the spectral efficiency of the FBMC systems.

In this paper, we evaluate the performance of a filter-bank multicarrier-quadrature amplitude modulation (FBMC-QAM) system with two prototype filters. We analyze the effective channel and derive the signal-to-interference ratio for additive white Gaussian noise channels and multipath fading channels. Based on the analysis, the characteristics of the effective channel according to various prototype filter sets and channel models are investigated. Utilizing the effective channels, we design two types of per-tone channel equalizers and evaluate the bit error rate performances by Monte Carlo simulations. The simulation results show that the FBMC-QAM system with two prototype filters can achieve almost the same bit error rate performance as orthogonal frequency division multiplexing with cyclic prefix by adopting the signal-level per-tone channel equalizer. Finally, the computational complexity of per-tone channel equalizers is analyzed.

In this paper, we propose a deep learning based algorithm to estimate the position of an user by utilizing reference signal received power (RSRP) and the location of base stations. To obtain reliable results in a real communication environment, parameters were measured using commercially available base stations and mobile phones within a LTE network. Since the structure of the measured data changes in accordance with the number of connected base stations, it is necessary to work on data uniformity processing before running the deep learning network. Therefore, we extract only the case in which three base stations are connected, using it as a feature of deep learning network. The experimental results reveal that the performance of the proposed algorithm is much better than that of the conventional fingerprint method. The average distance error is reduced from 71.04 meters for the fingerprint-based method to 43.51 meters for the proposed deep learning-based method.

To obtain the beamforming gain of a frequency-division duplex massive multiple-input multiple-output system, extensive channel state information (CSI) should be accurately quantized and fed back to the base station. Trellis-coded quantization (TCQ) can reduce codebook size and codebook-searching complexity that are the main drawbacks of vector-quantization-based CSI feedback schemes. However, the beamforming gain of the conventional TCQ cannot be fully obtained in a spatially correlated channel that is inevitably involved in massive transmit arrays. In this letter, we propose a differential TCQ that exploits the channel correlation by using a compressive constellation and phase difference quantization. We present an algorithm for generating a phase difference constellation and a decoding process for the proposed differential TCQ. The proposed scheme can reduce the overhead of the codebook size problem by trellis decoding and improve the quantization performance at the same feedback rate compared to the conventional TCQ scheme.

Owing to the high cost of radio frequency (RF) chains and power consumption of broadband signals, it is suitable to include both analogue and digital processing for millimetre wave (mmWave) beamforming. In this study, hybrid beamforming (HBF) schemes are considered for multi‐user downlink mmWave systems. RF beamforming schemes are proposed for HBF transmission with perfect channel state information at transmitter (CSIT) and limited feedback information in the multi‐user mmWave channel. Simulation results show that proposed HBF schemes enhance sum rate performance than conventional HBF schemes in both cases of perfect CSIT and limited feedback.

Licensed band communication systems have recently begun trying to utilize unlicensed bands. Due to lack of coordination, however, this spectrum utilization results in strong interference to other unlicensed band equipment (UBE). This problem highlights the need for coexistence scenario that will prevent UBE performance degradation. In this paper, we propose a new access control mechanism for downlink cellular networks using the unlicensed band. First, we analyze the impact of the cellular networks on the UBE outage performance. Based on the analysis, we then obtain a decision threshold for the unlicensed band access control. In the proposed control mechanism, the decision threshold is adaptively controlled by considering the density of the UBE. The numerical results demonstrate that the proposed access control mechanism guarantees the UBE's target performance under various network circumstances.

We analyze the performance of a cooperative multi-hop network with an incremental hop selection (IHS) scheme. Generally, multiple-relay networks based on half-duplex-mode relays undergo spectral efficiency loss due to the use of multiple time phases. The IHS scheme can prevent this problem because it increases the number of hops only when the currently selected path cannot support the target performance. Moreover, we apply an adaptive modulation technique to the IHS scheme to determine the additional spectral efficiency gain while maintaining the target performance. We evaluate the average spectral efficiency and the outage probability performances over the Nakagami-m fading channel. Also, the diversity and coding gains are derived through the asymptotic outage probability in the high transmit SNR region.

We propose a time reversal and minimum inter-symbol interference (TR-MISI) serial prefilter for indoor wireless communication system. Proposed TR-MISI serial prefilter minimizes inter-symbol interference (ISI) and makes received peak power be robust to noise. Since the TR prefilter does not minimize the ISI and the joint zero-forcing and TR prefilter does not guarantee the performance in low signal-to-noise ratio (SNR) environment, we consider these two facts in designing an optimization problem for proposed TR-MISI serial prefilter. We use the semidefinite relaxation to solve the optimization problem and get a near-optimal solution. The simulation results show that proposed TR-MISI serial prefilter improves the bit error rate performance at overall SNR.

This paper analyzes the performance of an amplify-and-forward (AF) protocol based cooperative multi-hop relay networks (CMHNs), where signals from the multi-hop relay network (MHN) and the direct path are considered in the detection, over the flat Nakagami-m fading channels. The analysis is accomplished by using the Beaulieu series that obtains a cumulative distributed function (CDF) through a characteristic function (CHF). Since the received SNR of MHN is mainly affected by the weakest channel in the network, we propose a hop selection scheme that prevents the performance degradation of the MHNs. We expand the CDF analysis to the networks employing proposed scheme by a tight bound of the received SNR. Also, in order to evaluate the diversity and coding gains of the proposed hop selection scheme, an asymptotic outage probability at high SNR region is derived. We show that the simulation results are closely matched to the analyzed results.

This paper deals with the global optimality of the channel capacity of the multiple-input multiple-output (MIMO) cooperative system which is equipped with precoders at source and relay, and exploits the direct channel between source and destination. Each precoder of the system is individually designed by the Lagrangian method and the final precoders are decided by an iterative structure. To prove the global optimality for the channel capacity of the system with these joint precoders, we show that the channel capacity function of the system is a concave function and the constraints of the system are convex sets.

We analyze the power loss of Tomlinson-Harashima precoding with constellation tilting (THP-CT) method using M-QAM modulation in flat fading MIMO channels. To derive a new approximation of the power loss of THP-CT method, the effective channel components, the information symbols, the precoded symbols, and some of their combinations are analyzed stochastically. From the analysis of the signal components, the transmission power of the conventional THP is firstly approximated as the mean of the block average power for a fixed tilting angle. Then, the variance for a fixed angle and the dependence between the block average power terms with two tilting angles are approximated. Also, we modify the Clark's algorithm that approximates the expected minimum of dependent samples. By using the modified algorithm and also the mean, variance, and correlation factors, we provide a more accurate power loss approximation for THP-CT. Simulation results show that the proposed algorithm exploiting the analyzed dependence expression can reduce the approximation error up to about 60% and 70% for the case of 4-QAM and 16-QAM case, respectively.

This paper deals with the power allocation problem in a wireless relay network employing differential modulation technique and Alamouti distributed space-time code, where signals from both the direct and relay paths are taken into account in the detection process. We propose a sub-optimal power allocation scheme based on the maximization of an approximate receive covariance expression. The proposed scheme jointly allocates the transmit power of both the source and relay nodes under a total transmit power and per-node transmit power constraints. We can increase the effectiveness of the proposed power allocation algorithm by grouping the relays with respect to their individual covariances. Numerical experiments show that the proposed covariance maximization algorithm combined with the relay grouping algorithm offers a respectable BER performance improvement, compared to the conventional schemes. Finally, we demonstrate that the proposed scheme requires only a small number of iterations to achieve convergence, making it attractive to practical systems.

In this letter, simple power control techniques are proposed to minimize error probability of a two-stream multiple-input multiple-output (MIMO) system with two transmit antennas and a maximum likelihood (ML) receiver, which is known as an optimal receiver but has a high computational complexity problem. The proposed techniques using Euclidean distances of a few error vectors have the advantages of exactly same error performance and very low computational complexity, compared with an optimal power control technique.

Tomlinson-Harashima precoder (THP) is being noted for an efficient alternative to dirty paper coder. THP increases the transmission power due to the use of the modulo operator. In this paper, we propose a method that reduces the transmission power by tilting the constellations of the transmit data symbols. The symbol error rate (SER) is improved for a fixed SNR by reducing the transmission power. Average transmission power is calculated asymptotically by using an extreme value distribution. We have shown that the transmission power can be significantly reduced when we use the tilted constellation THP. As the transmission power determines the SER slope, at high SNRs we achieve lower SER than in the case of a transmission over an AWGN channel. The tilted angle can be estimated at the receiver. An angle estimation algorithm is proposed that optimizes angle estimation error and SER.We use the approximation of the angle estimation error and SER to find an optimum number of tilted angles. By using the proposed angle estimation algorithm and the optimum number of tilted angles, the tilted constellation THP always outperforms the conventional THP algorithm.

Tomlinson-Harashima precoder (THP) is an efficient interference pre-canceler, however, the transmission power is increased over the symbol power due to the modulo operation. In this paper, we propose an improved THP that reduces the transmission power of the conventional THP by tilting a group of symbols by an angle toward minimizing the transmission power. The tilted angle can be either estimated by the receiver or notified by the transmitter using a feed-forward information. Assuming a large interference, the average transmission power of the proposed THP is derived by the central limit theorem and Gumbel extreme value distribution. The symbol error rate (SER) and its asymptotic behavior is also derived and compared. The more tilted angles or the smaller size of groups are used, the more power the proposed THP saves. The transmission power can even be less than the symbol power. Accordingly, in an environment that the power gain overwhelms the other losses, the SER can be less than the SER of AWGN channel.

In this paper, we consider a 2 times 2 multiple-input multiple-output (MIMO) system using full-diversity full-rate (FDFR) code. To obtain full diversity gain from FDFR code, ML receiver should be inevitably used. However, ML receiver has very high complexity problem. Therefore, we proposed an efficient receiver for a 2 times 2 MIMO system with FDFR code. The proposed receiver has very low complexity compared to ML receiver and provides full diversity gain. In the threshold parameter, gammao= 2.2, there is the reduction effect of about 46 - 48% from the view point of the number of multiplication and very small SNR gain loss within 0.2 dB compared to ML receiver.

Opportunistic beamforming is a powerful technique increasing system throughput in a static channel environment. Since the opportunistic beamforming technique requires quite large user population to exploit sufficient multiuser diversity, a codebook-based opportunistic beamforming (COBF) technique is proposed. The COBF technique uses a unitary matrix, which changes with time, to induce larger and faster channel fluctuations in the static channel. Also, the COBF technique uses a codebook to provide further selection diversity to the conventional opportunistic beamforming technique to overcome the problem of limited multiuser diversity in a small population. Compared to the recently proposed multiple-pilot based opportunistic beamforming technique in I.M. Kim et al. (2005), the COBF technique reduces required number of pilots. Moreover, since the size of codebook, not the number of pilots, determines the amount of supplementary selection diversity, the system throughput can be increased without limitation from the number of pilots. Some computer simulation results show that system throughput of the COBF technique is higher than that of other conventional techniques and required number of unitary matrices, which needs to be kept as a memory at mobile stations, is only ten in a simulation environment.

In this letter, we consider a transmit antenna selection scheme that provides selection diversity gain by exploiting a limited feedback information, and we propose a selection criterion appropriate for iterative receivers. Since the conventional selection criterion does not consider the convergence behavior of iterative receivers, a two-step antenna selection criterion is proposed, taking into account performance improvement as a function of iteration. Based on simulation results, the proposed scheme demonstrates superior performance compared to the conventional scheme as the number of iteration increases.

For multiple antenna systems, we consider the hybrid iterative detection of the maximum a posteriori probability (MAP) detection and the linear detection such as the minimum mean squared error (MMSE) filtering with soft cancellation. We devise methods to obtain both the lower complexity of the linear detection and the superior performance of the MAP detection. Using the a prior probability of the coded bit which is extrinsic of the outer decoder, we compute the threshold of grouping and determine the detection scheme symbol by symbol. Through the simulation results, it is shown that the proposed receiver obtains the superior performance to the MMSE detector and the lower complexity than the MAP detector

In this paper, we consider double space time transmit diversity (D-STTD) with an adaptive modulation (AM) algorithm. The simple criteria to use AM algorithm and antenna shuffling pattern are proposed according to linear receivers such as zero-forcing and minimum mean square error receiver. The AM algorithms are derived to minimize bit error rate (BER) under the fixed total data rate and total transmit power. The D-STTD system using the proposed algorithms requires low computational complexity and small feedback information bits. The performance of D-STTD system using the proposed algorithm is improved by about 5.8 dB or more in signal to noise ratio (SNR) corresponding to 10-3BER. Additionally, using antenna shuffling, BER performance is more improved, but more feedback information bits are also required.

A scheduling scheme for MIMO systems which has the capability of obtaining multiuser diversity gain and controlling the link connection ratio among users simultaneously is proposed. Based on the Gaussian assumption of MIMO channel capacity which is fed back to the base station from each mobile, a Gaussian cost function with an appropriate mean and variance is used as a scheduling criteria. The mean and variance of the cost function permits the resources for the proposed scheduler to be controlled. We first consider the optimal scheduler, which maximizes overall system capacity with the desired link connection ratio(a priori desired ratio), and a method is proposed to obtain weights for the optimal scheduler using some properties of the Gaussian statistics. Although the optimal scheduler has the major impact on system capacity, the complexity associated with weight calculation is high. Thus, two types of suboptimal schedulers, weights of which are not dependent on the channel statistics, denoted as the Control Variance(CV) scheduler and the Control Mean(CM) scheduler, are proposed. The overall system capacity of the proposed schedulers, which is significantly enhanced by multiuser diversity, was evaluated analytically and verified by computer simulation. The simulation results show that the overall system capacity of the CM and the CV scheduler is above 99% that of the optimal scheduler. In addition, another result show that the proposed schedulers manage resources accurately, for a relatively small number of antennas.

This paper analyzes the output signal-to-interference-plus-noise ratio (SINR) for a multiple-input multiple-output (MIMO) multicarrier code division multiple access (MC-CDMA) system with minimum mean square error receivers. Previous work on a single antenna MC-CDMA system is based on convergence of the limiting spectral distribution of large dimensional sample covariance matrices and the asymptotic freeness between random matrices. These concepts cannot directly be applied to a MIMO MC-CDMA system because some assumptions for single antenna do not match the case of multiple antenna. Therefore, this paper expands the concept of convergence and freeness to MIMO system by using extended Haar unitary random matrix and the Marcěnko-Pastur law. The analysis shows that the output SINR asymptotically converges to a deterministic value with a closed form expression. The analysis is used to obtain improved bit error rate calculation as verified by simulations.

The performance of ML detection for the given channel is analyzed in spatially multiplexed MIMO system. In order to obtain the vector symbol error rate, we define error vectors which represent the geometrical relation between lattice points. The properties of error vectors are analyzed to show that all lattice points in an infinite lattice almost surely have four nearest neighbors after random channel transformation. Using this information and the minimum distance obtained by the modified sphere decoding algorithm, we formulate the analytical performance of vector symbol error rate(VSER) over the given channel. To verify the results, we simulate ML performance over the various random channels which are classified into three categories: unitary channel, dense channel, and sparse channel. From the simulation results, it is verified that the derived analytical result gives a good approximation for the performance of ML detector over all random MIMO channels.

This letter studies feature selection in speaker recognition from an information-theoretic view. We closely tie the performance, in terms of the expected classification error probability, to the mutual information between speaker identity and features. Information theory can then help us to make qualitative statements about feature selection and performance. We study various common features used for speaker recognition, such as mel-warped cepstrum coefficients and various parameterizations of linear prediction coefficients. The theory and experiments give valuable insights in feature selection and performance of speaker-recognition applications.

In MIMO-OFDM systems, a conventional channel estimation technique using comb type training symbol structure has high mean squared error (MSE) at edge subcarriers. To reduce the MSE at those subcarriers, we propose the cyclic comb type training structure and the weighted-averaging method. In the proposed technique, all kinds of comb type training symbols are transmitted cyclically at each transmitter. At the receiver, channel frequency responses estimated using each training symbol are averaged with weight which is obtained from the corresponding MSE. By computer simulations, we have shown that the proposed cyclic comb type training symbols gives higher SNR gain than the conventional training structure.

We propose a method to minimize the loss of information during the feature extraction stage in speech recognition by optimizing the parameters of the mel-cepstrum transformation, a transform which is widely used in speech recognition. Typically, the mel-cepstrum is obtained by critical band filters whose characteristics play an important role in converting a speech signal into a sequence of vectors. First, we analyze the performance of the mel-cepstrum by changing the parameters of the filters such as shape, center frequency, and bandwidth. Then we propose an algorithm to optimize the parameters of the filters using the simplex method. Experiments with Korean digit words show that the recognition rate improved by about 4-7%.

A residual echo cancellation scheme based on the autoregressive (AR) analysis is proposed for hands-free telephony. Residual echo is whitened by the inverse filter using the estimated AR coefficients. Removing the speech characteristics of the residual echo signal, the noise reduction system successfully reduces the power of residual echo as well as that of ambient noise. In a car cabin, the proposed method improved the echo return loss enhancement by 8.7/spl sim/9.9 dB as compared with the conventional combined system.

An integrated acoustic echo and noise cancellation system for hands-free telephony is presented. The proposed system includes a new residual echo cancellation scheme based on spectral analysis and a new double-talk detector suitable for real-time implementation. Residual echo is whitened via AR analysis during no near-end-talk period and is cancelled by noise reduction. Removing speech characteristics of the residual echo signal, noise reduction successfully reduces the power of the residual echo as well as the ambient noise. For further integration with commercial low-bit rate speech coders, noise reduction in IS-127 (EVRC) was considered. For the hands-free situation in the moving car, the proposed system attenuated the interferences more than 30 dB at a constant speed of 90 km/h. The proposed system was implemented on a low-cost DSP with 16-bit fixed-point arithmetic.

This paper proposes an LMS (least mean squares) based forward-link beamforming method. The proposed forward-link beamforming system compensates the phase distortion of reverse-link channel states for reverse-link LMS weight. Therefore, no additional beamforming module for forward-link beamforming is required for the proposed system. While performance of the proposed system is comparable with the conventional forward-link beamforming system, complexity of the proposed system is much less than the conventional system.

A duration modeling scheme and a speaking rate compensation technique are presented for the HMM based connected digit recognizer. The proposed duration modeling technique uses a cumulative duration probability. The cumulative duration probability also can be used to obtain the duration bounds for the bounded duration modeling. One of the advantages of proposed technique is that the cumulative duration probability can be applied directly to the Viterbi decoding procedure without additional postprocessing. Therefore, it rules the state and word transition at each frame. To alleviate the problems due to fast or slow speech, a modification to the bounded duration modeling which accounts for speaking rate is described. The experimental results on Korean connected digit recognition show the effectiveness of the proposed duration modeling scheme and the speaking rate compensation technique.

An iterative method for reducing noise in contaminated chaotic signals is proposed. This method estimates the deviation of the observed signal from the nearest noise-free signal satisfying the system dynamics in order to get a noise-reduced (or enhanced) signal. To calculate the deviation we minimize a cost function composed of two parts: one containing information that represents how close the enhanced signal is to the observed signal and another including constraints that fit the dynamics of the system. This method has a simple structure and is flexible in the choice of the parts of the cost function. The proposed method is compared with Farmer's method which is known to have good performance in mild signal-to-noise ratios but has a more complex structure.