Orthogonal-time-frequency-space (OTFS) has been developed as a promising modulation scheme for high Doppler channels. In OTFS systems, channel estimation (CE) plays a key role. Most existing CE schemes put pilots in delay-Doppler (DD)-domain, often resulting in low spectrum efficiency and high peak-to-average-power-ratio (PAPR). In this paper, we propose three schemes to solve the problems. Proposed methods have distinct features being able to estimate fractional delays and fractional Dopplers. Different from existing methods, our approach places pilots in time-frequency (TF)-domain. The first scheme overlaps pilots and data in both TF- and DD-domain, while Scheme II overlaps them in TF- but not in DD-domain. To further enhance the performance, the third scheme makes pilots and data non-overlapped in both TF- and DD-domain. To conduct CE, time-domain pilot signals are extracted with a filtering operation and a two-dimensional frequency estimation problem is formulated consequently. Delays are first estimated with root-MUSIC. Then, Dopplers are estimated using a simple line-fitting method. Finally, channel gains are estimated by a least-squares method. To solve the interference between pilots and data, successive-interference-cancellation (SIC) is also applied. With the estimated channel, a minimum-mean-square-error (MMSE) equalizer is used for the compensation of channel effect. Simulation results show that the bit-error-rate (BER) performance with the channel estimated by proposed CE methods can approach to that with known channel. Also, spectrum efficiency can be improved by at most 25% and PAPR can be reduced by at least 19 dB.

Angle-of-arrival (AoA) information is vital for beamforming which is used to combat severe propagation loss in millimetre wave communications. Although blind AoA estimation has been extensively studied for digital array, few results were reported for hybrid array. Well known subspace-based methods such as MUSIC and ESPRIT have been successfully developed. However, their efficacy is reduced in hybrid array due to the loss of spatial degrees of freedom (DoFs). In this article, we propose a method to solve the problem. The proposed algorithm uses an augmented beamspace approach and a precoding scheme such that the lost spatial DoFs can be recovered. As a result, MUSIC and ESPRIT can be used with full capacity. Theoretical analysis is conducted to obtain lower bounds of the estimation-error variances. Simulations with an OFDM system show that the proposed method can achieve satisfactory performance and the theoretical bounds are tight.

Two major 5G services are known as enhanced-mobile- broadband (eMBB) and ultra-reliable low-latency communication (URLLC). In downlink eMBB and URLLC multiplexed systems, some eMBB data resources have to be preempted for the transmission of URLLC data. One main issue for eMBB receiver is to detect the resource blocks being preempted. A common approach for the problem is the use of indicators. However, this will result in significant overheads. In this paper, we propose an indicator-free method to solve the problem. The proposed method uses a precoding scheme adding correlation to URLLC data, and a simple detection method can then be applied at the receiver to distinguish eMBB or URLLC data, blindly. Theoretical URLLC data detection and eMBB symbol error rates are also derived such that the optimum system parameters can be determined. Finally, simulations show that the proposed method can yield satisfactory results.

This paper investigates nonlinear transceiver design for full-duplex multiple-input multiple-output (FD-MIMO) relay systems. A dual-hop amplify-and-forward relaying protocol is considered. At the destination, nonlinear successive-interference-cancellation (SIC) is used for signal detection. The goal is to find the source and relay precoders such that the symbol-vector error rate (SVER) can be minimized. Due to the loop interference (LI), optimizing the relay precoder in FD systems is much more involved. In this paper, we propose novel designs to solve this problem. Starting from the QR-SIC receiver, we theoretically show that the relay precoder can be solved with a closed-form expression even when the system incurs LI. Then, we consider the system with a minimum mean-squared-error SIC receiver, where the relay precoder design entails a different problem formulation and introduces new challenges. We propose a novel iterative method, with closed-form solutions in each iteration, to solve this problem. Simulations show that our designs can significantly improve the SVER performance for FD-MIMO relay systems.

Full-duplex (FD) relaying has been considered an effective scheme to increase the spectral efficiency of multiple- input multiple-output (MIMO) relay systems. As well-known, the main concern for the FD system is the cancellation of loop interference (LI). In this paper, we consider the joint source/relay precoding to mitigate the LI problem in FD-MIMO relay systems. In our system, spatial multiplexing is exploited for the signal transmission, and the QR successive-interference-cancellation (SIC) receiver is adopted at the destination. Linear precoders are considered at the source and relay, and the block error rate is used as the criterion for the precoders design. To facilitate the optimization, we propose using the primal decomposition, translating the original problem into a subproblem and a master problem. In the subproblem, the source precoder is first solved with the geometric mean decomposition (GMD) method. Then, the master problem can be formulated as a convex optimization so that the relay precoder can be solved with Karush-Kuhn-Tucker (KKT) conditions. The proposed precoders have closed-form expressions, facilitating real- world implementation. Simulation results show that the proposed method significantly improves the performance of FD-MIMO relay systems.

Spatial modulation (SM) combined with spatial multiplexing is a newly developed transmission scheme in multiple-input multiple-output (MIMO) systems. The resultant system, referred to as generalized SM (GSM), can use the maximum-likelihood (ML) detector jointly detecting the antenna-subset (AS) index and symbol vector. As known, the ML detector can achieve optimum performance; however, its computational complexity can be prohibitively high when the dimension of the GSM system is large. In this paper, we propose new methods to solve the problem. The main idea is to split the detection into two stages, one for the AS index and the other for the symbol vector. For the detection of the AS index, we develop two methods, referred to as Gaussian approximation and QR projection. Once the AS index is detected, conventional low-complexity ML detectors can be applied for the detection of the symbol vector. The diversity order for the proposed methods are further analyzed and an enhanced method is also proposed to achieve near-optimum performance. Finally, the proposed methods are extended to conduct soft detection of GSM systems. Simulations show that our methods significantly outperform existing ones while the detection complexity remains similar.

The energy efficiency of OFDM systems is known to be low due to the high peak-to-average power ratio problem. The linear-amplification-with-nonlinear-component (LINC) technique can solve the problem by decomposing the input signal into two constant-envelop component signals and applying high-efficient nonlinear amplifiers. However, the power combiner, a key component used to combine the amplified signals, is difficult to implement. Combinerless LINC systems employ two transmit antennas such that two component signals can be naturally combined at the receiver. Unfortunately, the performance of combinerless LINC-OFDM systems is seriously degraded if difference exists, even small, between the two channels that the two component signals propagate. In this paper, we propose an enhanced zero-forcing equalizer to solve this problem. The main idea is to reduce a self-interference induced by the channel difference. A closed-form expression for the optimum parameter of the proposed equalizer is also derived. Simulations show that the proposed equalizer can effectively enhance the performance of combinerless LINC-OFDM systems.

Spatial modulation (SM) combined with spatial multiplexing is a newly developed transmission technique in multiple-input multiple-output (MIMO) systems. The resultant system, referred to as SM-MIMO, can use the maximum-likelihood (ML) detector jointly detecting the antenna index and symbol vector. As known, the ML detector can achieve optimum performance; however, its computational complexity may become prohibitively high when the dimension of MIMO system or symbol constellation is large. In this paper, we propose a new method to solve the problem. The main idea is to conduct a two-stage detection, antenna index and symbol. We develop a Gaussian approximation method such that the antenna-index can be first reliably detected. An enhanced method is also proposed so that the near-optimum performance can be obtained. Simulations show that our methods outperform existing methods while the detection complexity remains low.

One problem in multiuser multiple-input multiple-output (MU-MIMO) systems is that the transmitted signal from a base station to a user is interfered by the signals to other users. MU-MIMO precoding has been proposed as an effective way to solve the problem. However, most existing precoding methods are mainly designed with zero-forcing (ZF) or minimum mean-square-error (MMSE) criterion and the performance is not optimal. In this paper, we consider a precoding scheme for MU-MIMO systems where maximum-likelihood (ML) detection is used for each user. We propose using a newly developed X-structured precoder which is specifically optimized for the ML detection. In our method, multiuser interference is first mitigated by using a regularized block diagonalization (RBD) technique. Then, an iterative method is used to construct the X-structured precoder so that the performance of the ML detection can be enhanced. Numerical results show that the proposed method can significantly outperform existing precoding methods.

In multiple-input multiple-output (MIMO) transmission, precoding has been considered a promising method to improve the system performance. In general, the precoder design criterion depends on the detector used at the receiver. For the maximum-likelihood (ML) detector, the optimum precoder design criterion is equivalent to maximizing the minimum distance of received signal constellations. Several precoding methods have been developed in the literature. However, most of them use numerical searches to derive the precoders and require table look-ups in realtime applications. In this paper, we propose a simple but effective method to solve the problem. The proposed precoder has a simple closed-form expression and no tables are required to store. Simulation results show that the proposed precoder can provide almost the same performance as existing precoders.

It is known that in cellular systems, the users in cell edge suffer from the interference from interfering cells. In LTE-A, coordinated multi-point (CoMP) processing was proposed to solve the problem. In CoMP, the transmitted/received signals of the users in neighbor cells can be jointly processed such that interference can be either avoided or estimated as desired signals. In the current CoMP operations, precoding is only conducted between individual base-stations and the served user. If we take all the base-stations and the served user into consideration, a network multiple-input-multiple-output (MIMO) system can be constructed. Precoding in such system will have better performance but require a high-dimension codebook which is not supported by current LTE-A systems. In this paper, we first propose a method to solve the problem. The idea is to use the antenna selection technique reducing the number of transmitted antennas such that the existing codebooks can still be used. Existing selection criteria for the maximum likelihood receivers often require sophisticated matrix operations such as QR-decomposition (QRD). Thus, the required computational complexity is high. We then propose some selection criteria without involving matrix operations. Simulations show that while the antenna-selection based precoding method can reduce the feedback overhead and at the same time improve the precoding performance. Also, the performance of the proposed low-complexity criteria is similar to that of the QRD-based criteria.

Most nonlinear transceiver designs in amplify-and-forward (AF) multiple-input-multiple-output (MIMO) relay systems assume that instantaneous perfect channel state informations (CSIs) are available. Transceiver design with imperfect CSIs have rarely been addressed. In this paper, we consider an AF MIMO system in which a Tomlinson-Harashima precoder (THP) is used at the source, a linear precoder at the relay, and a minimum-mean-squared-error (MMSE) receiver at the destination. With the imperfect CSIs, we propose a new robust transceiver design method. It is shown that the optimization problem for the design is difficult due to the fact that the objective function is a nonlinear function of the source and relay precoders and yet the constraints are coupled. To overcome the problem, we adopt the primal decomposition technique, decomposing the original optimization into a subproblem and a master problem. To facilitate the derivation of the solution, we propose cascading THP with a unitary precoder and using a lower bound of the objective function. In this way, we can translate the original matrix-valued optimization into the scalar-valued concave optimization. Using the Karush-Kuhn-Tucker (KKT) conditions, we can finally obtain the closed-form solutions for the relay and source precoders. Simulations show that the proposed design is effective against the imperfect CSIs.

Existing transceiver designs in amplify-and-forward (AF) multiple-input-multiple-output (MIMO) relay systems often assume the availability of perfect channel state informations (CSIs). Robust designs for imperfect CSI have less been considered. In this paper, we propose a robust nonlinear transceiver design for the system with a Tomlinson-Harashima precoder (THP), a linear relay precoder, and a minimum-mean-squared-error (MMSE) receiver. Since two precoders and imperfect CSIs are involved, the robust transceiver design is difficult. To overcome the difficulty, we first propose cascading an additional unitary precoder after the THP. The unitary precoder can not only simplify the optimization but also improve the performance of the MMSE receiver. We then adopt the primal decomposition dividing the original optimization problem into a subproblem and a master problem. With our formulation, the subproblem can be solved and the two-precoder problem can be transferred to a single relay precoder problem. The master problem, however, is not solvable. We then propose a lower bound for the objective function and transfer the master problem into a convex optimization problem. A closed-form solution can then be obtained by the Karush-Kuhn-Tucker (KKT) conditions. Simulations show that the proposed transceiver can significantly outperform existing linear transceivers with perfect or imperfect CSIs.

The hybrid of spatial multiplexing and transmit beamforming is a simple scheme to combat channel fading in multiple-input multiple-output (MIMO) transmission. In this scheme, a set of transmit antennas is used to transmit a bit stream and antenna grouping is then required. How to conduct the grouping optimally is the main concern. For maximum-likelihood (ML) receivers, the grouping criterion is equivalent to maximizing the minimum distance of receive signal constellations, referred to as free distance. However, finding the free distance needs an exhaustive search which is not desirable. In the literature, a singular-value-decomposition (SVD) based method was proposed to obtain a lower bound of the free distance. Using the lower bound as the grouping criterion, one can solve the problem much easier. In this paper, we first propose using a QR decomposition (QRD) based method to obtain another lower bound. It can be shown that the lower bound yielded by the QRD is tighter than that by the SVD. We then propose using a basis-transformation method such that the QRD-based lower bound can be further tightened. Simulations show that the basis-transformed QRD-based method can yield near-optimum performance.

Precoding is an effective method to improve the transmission quality in multiple-input multiple-output (MIMO) systems. In a real-world system, the precoder is selected from a codebook, and its index is fed back to the transmitter. For a maximum-likelihood (ML) receiver, the criterion for precoder selection is equivalent to maximizing the minimum distance of the received signal constellation. The derivation of the optimum solution, however, may be of high computational complexity due to the requirement of the exhaustive search. To reduce the computational complexity, a suboptimum solution based on singular value decomposition (SVD) has been proposed in literature. In this paper, we propose using a QR decomposition (QRD) based method for precoder selection. To further improve the system performance, we also propose an enhanced QRD-based selection method. With Givens rotations, the computational complexity of the enhanced QRD-based method can be effectively reduced. Finally, we combine precoding with receive antenna selection, and use the proposed QRD-based methods to solve this joint optimization problem. Simulation results show that the proposed approaches can significantly improve the system performance.

Existing precoding schemes in amplify-and-forward (AF) multiple-input-multiple-output (MIMO) relay systems use linear precoders. In this paper, we consider a precoding scheme in which a Tomlinson-Harashima (TH) precoder (THP) is used at the source and a linear precoder at the relay. With a minimum-mean-squared-error (MMSE) receiver at destination, we propose a new joint precoders design method. Since two precoders are involved, the transceiver design, formulated as an optimization problem, is difficult to solve. To overcome the problem, we propose cascading an additional unitary precoder with the TH precoder. The unitary precoder can not only simplify the optimization problem but also improve the MMSE performance. With the specially designed unitary precoder at the source, we can then adopt the primal decomposition method to solve this problem. With the method, the original optimization problem can first be decomposed into a master and a subproblem optimization problems, and then transferred to a relay precoder optimization problem. However, the optimization is not a convex problem and the solution is not obtainable. We then propose a method being able to transfer it to a convex optimization problem. A closed-form solution can then be obtained by the Karuch-Kuhn-Tucker (KKT) conditions. Simulations show that the proposed transceiver can significantly outperform existing linear transceivers.

This paper addresses the linear transceiver design with quality of service (QoS) constraints in an amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay system. Taking both the direct and the relay links into account and using the minimum-mean-squared-error (MMSE) receiver at the destination, we jointly design the source/relay precoders such that the transmitted power is minimized and QoS constraints are satisfied. The optimization problem such formulated, however, is a highly nonlinear formulation of the source and relay precoders, and the optimum solution is difficult to derive. To solve the problem, we then propose to use a constrained structure for the precoders, and derive mean-squared-error (MSE) upper bounds. Using the primal decomposition method, we can then translate the original optimization problem into two scalar-valued convex optimization problems. The closed-form solutions for the precoders can then be derived by corresponding Karush-Kuhn-Tucker (KKT) conditions. Using the solution, we further provide a sufficient condition to determine if the system is proper to be operated in the cooperative mode or not. Simulations show that the proposed precoded MIMO relay system can significantly reduce the transmission power, compared to a non-cooperated one.

Most precoding schemes in amplify-and-forward (AF) multiple-input-multiple-output (MIMO) relay systems only consider the relay precoder, and the receiver is often linear. In this paper, we propose a joint source/relay precoders design method for a nonlinear minimum-mean-squared-error successive interference cancellation (MMSE-SIC) receiver. It is shown that a direct minimization of the average bit-error-rate (BER) is not feasible. Using the uniform channel decomposition (UCD) method, we can design the source and relay precoders such that the signal-to-interference-plus-noise ratio (SINR) for each layer is equal and the block BER (BLER) can be minimized. Unlike UCD in conventional MIMO systems, two precoders have to be optimized simultaneously and the optimum solution is still difficult to derive. We then propose to adopt the primal decomposition method decomposing the original problem into a master and a subproblem optimizations. To facilitate the optimization, we propose a unitary source precoder such that the subproblem can be analytically solved, and the source precoder can be expressed as a function of the relay precoder. Then, with a proposed relay precoder structure, the master problem can be translated into a standard concave optimization problem. By Karush-Kuhn-Tucker (KKT) conditions, we can finally obtain the closed-form solutions for the relay and source precoders. Simulations show that the proposed design can outperform existing precoding methods in AF MIMO relay systems.

Existing precoder designs for an amplify-and-forward (AF) cooperative system often assume a linear receiver at the destination, and a precoder at the relay. The performance enhancement of such a system is then limited. In this paper, we consider a nonlinear successive interference cancellation (SIC) receiver, and at the same time take the source precoder into consideration. Using the geometric mean decomposition (GMD), we propose a joint source/relay precoders design method, fully exploring information provided by direct and relay links. With our method, the design problem can be transformed to a standard scalar concave optimization problem, and a closed-form solution can be obtained. Simulations show that the proposed design can significantly enhance the performance of a MIMO AF cooperative system.

Antenna selection is a simple but effective method to exploit the transmit diversity in multiple-input multiple-output (MIMO) wireless communications. For maximum-likelihood (ML) detectors, the criterion for the selection is to maximize the free distance of the MIMO system. Since the optimum selection is difficult to conduct, a lower bound of the free distance is typically used as the selection criterion instead. The singular-value-decomposition (SVD) based selection criterion is well known in the literature. In this paper, we propose a QR decomposition (QRD) based selection criterion for antenna selection with the ML detector. Using some matrix properties, we theoretically prove that the lower bound achieved with the QRD-based criterion is tighter than that with the SVD-based criterion. We also propose another QRD-based criterion that can further tighten the lower bound. The proposed algorithms can be directly applied to the receive, and joint transmit/receive antenna selection schemes. Simulations show that the performance of the proposed selection criteria can significantly outperform the SVD-based selection criterion.

Precoding is an effective technique enhancing the performance of MIMO-OFDM systems. In practical systems, the precoding matrix is computed at the receiver, and then fed back to the transmitter. To reduce the amount of the feedback data, only the index representing a quantized precoding matrix is fed back. The quantized matrix is selected from a set of predetermined matrices called a codebook. Since the number of matrices in the codebook may be large, the search for the optimum precoder requires a high computational complexity. In this paper, we propose a low-complexity precoder searching algorithm to solve the problem. The basic idea is to construct a tree-like search strategy such that the complexity can be reduced from O(L)is O(log2(L)) where L is the number of the codewords. Compared to the exhaustive search, the proposed searching method can reduce the searching complexity significantly while the performance loss is small.

This paper addresses the joint source/relay precoder design problem in amplify-and-forward (AF) cooperative communication systems where multiple antennas are equipped at the source, the relay, and the destination. Existing solutions to the problem only consider the relay link and, thus, do not fully exploit all the available link resource. Using a minimum-mean- squared-error (MMSE) criterion, we propose a joint precoder design method, taking both the direct and relay links into account. It is shown that the MMSE is a highly nonlinear function of the precoder matrices, and a direct minimization is not feasible. To facilitate analysis, we propose to design the precoders toward first diagonalizing the MSE matrix of the relay link. This imposes certain structural constraints on both precoders that allow us to derive an analytically tractable MSE upper bound. By conducting minimization with respect to this upper bound, the solution can be obtained by an iterative water- filling technique. Simulations show that the proposed design can significantly enhance the performance of MIMO AF cooperative systems.

The time-domain equaliser (TEQ) is a commonly used device to shorten the channel impulse response in a discrete multitone (DMT) receiver. Many methods have been proposed to design the TEQ with a capacity maximisation criterion. An implicit assumption used by existing methods is that circular convolution can be conducted for the noise signal and the TEQ. This assumption is not valid because the noise vector, observed in a DMT symbol, does not have a cyclic prefix. A similar assumption is also made for the residual inter-symbol interference (ISI) signal. Because of these invalid assumptions, the TEQ-filtered noise and residual ISI powers in each subcarrier were not properly evaluated. As a result, optimum solutions derived are not actually optimal. This paper attempts to resolve this problem. We first analyse the statistical properties of the TEQ-filtered noise signal and the residual ISI signal in detail, and derive precise formulae for the calculation of the TEQ-filtered noise and residual ISI powers. Then, we re-formulate the capacity maximisation criterion to design the true optimum TEQ. Simulations show that the proposed method outperforms the existing ones, and its performance closely approaches the theoretical upper bound.

This paper considers transmitter precoding in an amplify-and-forward cooperative system where multiple antennas are equipped at the source, the relay, and the destination. Existing methods for the problem only consider the design of the relay precoder. To further improve the performance, we include the source precoder into the design. Using a minimum-meansquare- error (MMSE) criterion, we propose a joint source/relay precoder design method, taking both the direct and relay links into account. It is shown that the MMSE is a highly nonlinear function of the precoding matrices, and a direct minimization is not feasible. To facilitate analysis, we propose to design the precoders toward first diagonalizing the MSE matrix of the relay link. This imposes certain structural constraints on both precoders that allow us to derive an analytically tractable MSE upper bound. By conducting minimization with respect to this bound, the solution can be obtained by an iterative water-filling technique.

Orthogonal frequency-division multiplexing multiple access (OFDMA) has been considered as a promising multiple access technique in OFDM-based communication systems. In an ideal OFDMA system, no intercarrier interference (ICI) occurs. However, in high-speed mobile environments, the channel may be time-variant during one OFDMA symbol period. In such a condition, the mobility-induced ICI will degrade the system performance seriously. To remove the ICI effect, the zero-forcing (ZF) method is one of the simple techniques. Unfortunately, the direct ZF method needs to invert an N × N ICI matrix, requiring a prohibitively high complexity when N is large. In this paper, we first derive a structured OFDMA uplink signal model consisting of FFTs/IFFTs. Exploring this structure and using Newton’s iteration for matrix inversion, we develop a low-complexity ZF method. With our formulation, fast Fourier transforms (FFTs) can be used to reduce the complexity. Thus, the proposed method can reduce the complexity from O(N3to O(N log2N). Simulations show that the proposed method can have the similar performance to the direct ZF method while requires much lower complexity.

Similar to the conventional orthogonal frequencydivision multiplexing (OFDM) system, an OFDM multiple access (OFDMA) system will have a carrier frequency offset (CFO) problem. Since CFOs of all users are different, CFO compensation in the OFDMA uplink system is much more involved. A simple, yet efficient, method is the zero-forcing (ZF) compensation method. However, it involves an inverse of anNtimesNCFO-induced ICI matrix, whereNis the number of subcarriers. Thus, the complexity can become very high whenNis large, a case commonly seen in OFDMA systems. In this work, we propose a low-complexity ZF method to overcome the problem. The main idea is to use Newton's method to solve matrix inversion iteratively. We explore the structure of the CFOinduced ICI matrix and develop a method that can implement Newton's method with fast Fourier transforms (FFTs). As a result, the required computational complexity is significantly reduced fromO(N3) toO(2Nlog2N). Simulations show that, with only three iterations, the proposed method can have similar performance to the direct ZF method.

Adaptive parallel interference cancellation (PIC) has been recently proposed for the signal detection in multiple- input multiple-output (MIMO) systems. However, it suffers from error propagation when operated in time-varying channels. In this letter, an adaptive two-stage PIC with the minimum variance (MV) criterion is proposed to solve the problem. Adaptation with the MV criterion is realized with a decision feedback generalized sidelobe canceller (DFGSC). In the first-stage cancellation, a special structure involving dual DFGSCs is developed. All adaptation operations are implemented with the least-mean-square (LMS) algorithm. Simulations show that the proposed adaptive PIC detection can significantly outperform the conventional adaptive PIC detection in time-varying MIMO channel environments.

Partial parallel-interference-cancelation (PIC) is a well-known multiuser detection technique for DS/CMDA systems. Conventionally, partial-cancelation-factors (PCFs) are optimally derived and kept constant in a designated interval. Recently, a special kind of partial PIC was proposed. In the approach, PCFs are adaptively trained (chip-by-chip) using the least-mean-square (LMS) algorithm, and PCFs are updated with a bit-by-bit fashion. It has been shown that the adaptive partial PIC detector outperforms conventional partial PIC. Despite its good performance, performance analysis of the approach has not been explored yet. This paper is aimed to fill the gap by conducting the performance analysis of an adaptive two-stage partial PIC detector. We first analyze optimal weights (i.e., PCFs), weight error means, and weight error variances. Simulation results indicates that our analytical results highly agree with empirical ones.

Adaptive parallel interference cancellation (PIC) has been recently introduced to the signal detection of multiple-input multiple-output (MIMO) systems. However, it suffers from error propagation when operated in time-varying channels. In this paper, an adaptive two-stage PIC with the minimum variance (MV) criterion is proposed to solve the problem. Adaptation with the MV criterion is realized with a decision feedback generalized sidelobe canceller (DFGSC). In the first-stage cancellation, a special structure involving dual DFGSCs is developed. All adaptation operations are implemented with the least-mean-square (LMS) algorithm. Simulation results show that the proposed two-stage PIC detection can significantly outperform the conventional adaptive PIC detection in fast time-varying channel environments.

Orthogonal frequency division multiplexing (OFDM) is a promising technique for frequency-selective fading channels. However, in high-speed mobile environments, the channel may be time-variant in one OFDM symbol. This will incur the intercarrier interference (ICI) degrading seriously the performance of an OFDM system. A simple remedy is to apply the zero-forcing (ZF) equalization method. Unfortunately, the direct ZF method requires an inverse of a huge matrix requiring a prohibitively high complexity. In this paper, we propose a low-complexity ZF method for ICI mitigation. The main idea is to explore the special structure of the ICI matrix and apply the Newton's iterative matrix inversion method. With our formulation, fast Fourier transforms (FFTs) can be used to reduce the complexity. The proposed method can reduce the complexity from the order of O(N/sub c//sup 3/) to that of O(N/sub c/log/sub 2/N/sub c/) where N/sub c/ is the number of subcarriers. Computer simulations show that the proposed method can cancel the ICI effectively and at the same time reduces the computational requirement dramatically.

Conventional array code acquisition uses a correlator bank and an adaptive beamformer. However, its performance degrades significantly while multiple access interference (MAI) is present. In this paper, we propose a low-complexity adaptive filtering scheme to solve the problem. The proposed scheme comprises two adaptive filters, an adaptive spatial and an adaptive temporal filter. With a special structure, the spatial filter can act as a beamformer suppressing interference, while the temporal filter can act as a code-delay estimator. A criterion is proposed for minimization such that these filters can simultaneously adjusted by a constrained least-mean-square (LMS) algorithm. The proposed system is simple and it can acquire both the code-delay and direction-of-arrival (DoA) of the desired user. The performance as well as the convergence behavior of the proposed algorithm is analyzed. Closed-form expressions for optimum filters and steady-state mean-squared errors (MSE) are derived. Computer simulations show that the mean acquisition time of the proposed system is much shorter than that of the conventional system.

Orthogonal frequency-division multiplexing multiple access (OFDMA) has been considered as a promising multiple access technique in OFDM-based communication systems. In the system, all users share subcarriers in a frequency-division multiplexing manner. Similar to OFDM systems, the carrier frequency offset (CFO) induces inter-carrier interference (ICI) degrading the system performance seriously. However, since the CFO for each user may be different, the compensation turns out much more complicated than that in OFDM systems. A simple method to this problem is the use of a direct zero-forcing (ZF) method which involves the inverse of a matrix. The required computational complexity becomes prohibitively high when the number of subcarriers is large. In this paper, we propose a low-complexity method to overcome the problem in an interleaved uplink OFDMA system. We explore the special structure of the CFO-induced interference matrix and develop a method to solve the matrix inversion problem with fast Fourier transform (FFT). The proposed method can reduce the required computational complexity from O(Nc3) to O(Nclog2Nc). From the simulations, we can see that the proposed method can perform as well as that of the direct ZF method while the required complexity is much lower

In this paper, we propose an adaptive decision feedback space-time equalizer (STE) to cope with the problems of co-channel interference (CCI) and inter-symbol interference (ISI) in wireless communications. Unlike the conventional STE, no training sequence is required for the adaptation of the proposed STE. Our scheme consists of an adaptive decision feedback generalized sidelobe canceller (DFGSC), a blind decision feedback equalizer (DFE) and a channel estimator. Due to the feedback operation, the proposed DFGSC is not only superior to the conventional GSC, but also robust to multipath channel propagation. With the special channel-aided architecture, the proposed blind DFE can reduce the error propagation effect. Simulation results show that the proposed STE is effective in mitigating both CCI and ISI even in severe channel environments

Conventional least-mean-square (LMS) based adaptive generalized sidelobe canceller (GSC) suffers from the slow convergence problem. When the environment is fast varying, the conventional GSC cannot perform satisfactorily. In this paper, we propose a new decision feedback (DF) technique to overcome this problem. The proposed algorithm introduces a feedback filter with one tap weight in the GSC structure. The Wiener solution for the proposed structure is derived and the convergence behavior of the adaptive DF-GSC is also analyzed. Simulation results show that the proposed structure can significantly accelerate the convergence and enhance the overall performance.

Parallel interference cancellation (PIC) is considered a simple yet effective multiuser detector for direct-sequence code-division multiple-access (DS-CDMA) systems. However, system performance may deteriorate due to unreliable interference cancellation in the early stages. Thus, a partial PIC detector in which partial cancellation factors (PCFs) are introduced to control the interference cancellation level has been developed as a remedy. Although PCFs are crucial, complete solutions for their optimal values are not available. We consider a two-stage decoupled partial PIC receiver. Using the minimum bit error rate (BER) criterion, we derive a complete set of optimal PCFs. This includes optimal PCFs for periodic and aperiodic spreading codes in additive white Gaussian channels and multipath channels. Simulation results show that our theoretical optimal PCFs agree closely with empirical ones. Our two-stage partial PIC using derived optimal PCFs outperforms not only a two-stage, but also a three-stage full PIC.

The Bayesian solution is known to be optimal for the symbol-by-symbol type of equalizer. However, the computational complexity for the Bayesian equalizer is usually very high. Signal space partitioning technique has been proposed for complexity reduction. It was shown the decision boundary of the equalizer consists of a set of hyperplanes. The disadvantage of the existing approaches is that the number of hyperplane cannot be controlled. Also, to find these hyperplanes, it requires a state searching process which is not efficient for time-varying channels. In this paper, we propose a new algorithm to remedy this problem. We propose an approximate Bayesian criterion such that the number of hyperplanes can be arbitrarily set. As a result, we can trade between performance and computational complexity. In many cases, we can make the performance loss being small while the computational complexity reduction is huge. The resultant equalizer is composed of a set of parallel linear discriminant functions and a maximum operation. An adaptive method using stochastic gradient descent is developed to identify the functions. The proposed algorithm is then inherently applicable to time-varying channels. Also, the computational complexity is low and suitable for realworld implementation.

The parallel interference cancellation (PIC) is considered as an effective yet simple detector for multiuser communications in direct-sequence code division multiple access (DS-CDMA) systems. However, the system performance may be deteriorated due to unreliable interference estimate in early canceling stages. Thus the partial cancellation factor (PCF) is introduced to control the interference canceling level resulting in the partial PIC approach. While the PCF is critical, the existing methods for determining optimum PCF is either not general or not accurate. In this paper, we propose a method deriving a close-form solution for the optimum PCF in a two-stage partial PIC receiver. Our results apply to either power balanced or unbalanced, synchronous or asynchronous systems. Simulation results show that our theoretical optimal PCF values are closed to the empirical ones. Also, the optimal two-stage partial PIC even outperforms three-stage full PIC.

This paper proposes a new texture classification algorithm that is invariant to rotation and gray-scale transformation. First, we convert two-dimensional (2-D) texture images to one-dimensional (1-D) signals by spiral resampling. Then, we use a quadrature mirror filter (QMF) bank to decompose sampled signals into subbands. In each band, we take high-order autocorrelation functions as features. Features in different bands, which form a vector sequence, are then modeled as a hidden Markov model (BMM). During classification, the unknown texture is matched against all the models and the best match is taken as the classification result. Simulations showed that the highest correct classification rate for 16 kinds of texture was 95.14%

The adaptive contrast enhancement (ACE) algorithm, which uses contrast gains (CG's) to adjust the high-frequency components of images, is a well-known technique for medical image processing. Conventionally, the CG is either a constant or inversely proportional to the local standard deviation (ILSD). However, it is known that conventional approaches entail noise overenhancement and ringing artifacts. In this paper, we present a new ACE algorithm that eliminates these problems. First, a mathematical model for the LSD distribution is proposed by extending Hunt's image model. Then, the CG is formulated as a function of the LSD. The function, which is nonlinear, is determined by the transformation between the LSD histogram and a desired LSD distribution. Using our formulation, it can be shown that conventional ACE's use linear functions to compute the new CG's. It is the proposed nonlinear function that produces an adequate CG resulting in little noise overenhancement and fewer ringing artifacts. Finally, simulations using some X-ray images are provided to demonstrate the effectiveness of our new algorithm.

In a TDMA mobile communication system, an adaptive decision feedback equalizer (DFE) is the common device to compensate the intersymbol interference caused by the multipath effect. Due to the fast fading characteristic of channels, most conventional DFEs use the recursive least square (RLS) algorithm. However, it is known that the RLS algorithm is computationally intensive and not easy to implement. The LMS is well known for its simple structure, however, due to its poor tracking property, it cannot be used. Recently, a multiple-training and multiple-tracking LMS (MTT-LMS) algorithm was developed. It was shown that the DFE trained by the MTT-LMS algorithm can yield a satisfactory result at moderate mobile speed. The MTT-LMS algorithm retains the simple structure of the LMS algorithm and is suitable for hardware implementation. In this paper, we consider the ASIC design of the MTT-LMS DFE for application to North American IS-54 systems. We have designed an efficient architecture which not only has small chip area but low clock rate. We also develop a new decision device to provide the desired signal for the MTT-LMS algorithm and the implementation of the device is simpler than the conventional one.

In a TDMA mobile communication system, the equalizer is a common device to compensate the intersymbol interference caused by the multipath effect. In this paper, we consider the decision feedback equalizer (DFE) for application to the North American IS-54 systems. Due to the fast fading characteristic of channels, most conventional DFEs use the recursive least square (RLS) algorithm. However, it is known that the RLS algorithm is computationally intensive and not easy to implement. In this paper, we employ the ideas of multiple-training and multiple-tracking to propose an LMS-based equalization scheme. We also incorporate the coded digital verification color code (CDVCC) of the TDMA slot as an additional training sequence. Simulation results show that the new scheme can achieve a bit error rate of 3% at the mobile speed of 80 km/hr and still have low complexity.

It has been shown that the narrowband (NB) interference suppression capability of a direct-sequence (DS) spread spectrum system can be enhanced considerably by processing the received signal via a prediction error filter. The conventional approach to this problem makes use of a linear filter. However, the binary DS signal, that acts as noise in the prediction process, is highly non-Gaussian. Thus, linear filtering is not optimal. Vijayan and Poor (1990) first proposed using a nonlinear approximate conditional mean (ACM) filter of the Masreliez (1975) type and obtained significant results. This paper proposes a number of new nonlinear algorithms. Our work consists of three parts. (1) We develop a decision-directed Kalman (DDK) filter, that has the same performance as the ACM filter but a simpler structure. (2) Using the nonlinear function in the ACM and the DDK filters, we develop other nonlinear least mean square (LMS) filters with improved performance. (3) We further use the nonlinear functions to develop nonlinear recursive least squares (RLS) filters that can be used independently as predictors or as interference identifiers so that the ACM or the DDK filter can be applied. Simulations show that our nonlinear algorithms outperform conventional ones.

This paper proposes a new texture classification algorithm that is invariant to rotation and gray-scale transformation. First, we convert two-dimensional (2-D) texture images to one-dimensional (1-D) signals by spiral resampling. Then, we use a quadrature mirror filter (QMF) bank to decompose sampled signals into subbands. In each band, we take high-order autocorrelation functions as features. Features in different bands, which form a vector sequence, are then modeled as a hidden Markov model (BMM). During classification, the unknown texture is matched against all the models and the best match is taken as the classification result. Simulations showed that the highest correct classification rate for 16 kinds of texture was 95.14%.

An algorithm is described for filtering streak noise in corrupted DPCM (differential pulse code modulated) images. In this scheme, the corrupted line is detected using the Wilcoxon-Mann-Whitney nonparametric test. This test mechanism requires no PCM-update multiplexing. The corrupted line is then filtered using a likelihood ratio test and data from the previous line. Typical experimental results are provided.>