Symbol Vector Research Articles

ML Symbol Detection for MIMO Systems in the Presence of Channel Estimation Errors

In wireless communication, the multiple-input multiple-output (MIMO) system is a well-known approach to improve the reliability as well as the data rate. In MIMO systems, channel state information (CSI) is typically required at the receiver to detect transmitted signals; however, in practical systems, the CSI is imperfect and contains errors, which affect the overall system performance. In this paper, we propose a novel maximum likelihood (ML) scheme for MIMO systems that is robust to the CSI errors. We apply an optimization method to estimate an instantaneous covariance matrix of the CSI errors in order to improve the detection performance. Furthermore, we propose the employment of the list sphere decoding (LSD) scheme to reduce the computational complexity, which is capable of efficiently finding a reduced set of the candidate symbol vectors for the computation of the covariance matrix of the CSI errors. An iterative detection scheme is also proposed to further improve the detection performance.

Reproducibility of Heart Rate Variability Characteristics Measured on Random 10-second ECG using Joint Symbolic Dynamics.

In this paper we employed joint symbolic dynamics (JSD) approach to study reproducibility of heart rate variability characteristics measured on 2 randomly selected 10-second segments within 3-minute resting orthogonal ECG in 170 healthy participants. First, the ECG R-peaks were detected using parabolic fitting. Second, the respiratory signal was derived from orthogonal ECG X-lead using QRS slopes. Third, time series of R-R intervals and respiratory phases (calculated using Hilbert transform), were transformed into tertiary symbol vectors based on their successive changes and words of length '3' were formed. Bland-Altman analysis was used to assess the agreement between measured log-transformed JSD characteristics of HRV, and their reproducibility. Traditional HRV measures such as RR' interval changes showed a very high reproducibility. However, agreement between two 10-second JSD indices of HRV was low. Interestingly, a significant decrease in low-high alterations of HRV dynamics measured using JSD was observed when respiratory phase transition intervals were excluded (10s: 4.7±9.4 vs. 24.8±21.0%, p<0.0001; 3min: 9.8±8.1 vs. 24.8±12.3%, p<0.0001).

Word Embedding 자질을 이용한 한국어 개체명 인식 및 분류

Named Entity Recognition and Classification (NERC) is a task for recognition and classification of named entities such as a person's name, location, and organization. There have been various studies carried out on Korean NERC, but they have some problems, for example lacking some features as compared with English NERC. In this paper, we propose a method that uses word embedding as features for Korean NERC. We generate a word vector using a Continuous-Bag-of-Word (CBOW) model from POS-tagged corpus, and a word cluster symbol using a K-means algorithm from a word vector. We use the word vector and word cluster symbol as word embedding features in Conditional Random Fields (CRFs). From the result of the experiment, performance improved 1.17%, 0.61% and 1.19% respectively for TV domain, Sports domain and IT domain over the baseline system. Showing better performance than other NERC systems, we demonstrate the effectiveness and efficiency of the proposed method.

An Efficient Bayesian PAPR Reduction Method for OFDM-Based Massive MIMO Systems

We consider the problem of peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM) based massive multiple-input multiple-output (MIMO) downlink systems. Specifically, given a set of symbol vectors to be transmitted to $K$ users, the problem is to find an OFDM-modulated signal that has a low PAPR and meanwhile enables multiuser interference (MUI) cancelation. Unlike previous works that tackled the problem using convex optimization, we take a Bayesian approach and develop an efficient PAPR reduction method by exploiting the redundant degrees of freedom of the transmit array. The sought-after signal is treated as a random vector with a hierarchical truncated Gaussian mixture prior, which has the potential to encourage a low PAPR signal with most of its samples concentrated on the boundaries. A variational expectation-maximization (EM) strategy is developed to obtain estimates of the hyperparameters associated with the prior model, along with the signal. In addition, the generalized approximate message passing (GAMP) is embedded into the variational EM framework, which results in a significant reduction in computational complexity of the proposed algorithm. Simulation results show our proposed algorithm achieves a substantial performance improvement over existing methods in terms of both the PAPR reduction and computational complexity.

On Vector Linear Solvability of Multicast Networks

Vector linear network coding (LNC) is a generalization of the conventional scalar LNC, such that the data unit transmitted on every edge is an $L$-dimensional vector of data symbols over a base field GF($q$). Vector LNC enriches the choices of coding operations at intermediate nodes, and there is a popular conjecture on the benefit of vector LNC over scalar LNC in terms of alphabet size of data units: there exist (single-source) multicast networks that are vector linearly solvable of dimension $L$ over GF($q$) but not scalar linearly solvable over any field of size $q' \leq q^L$. This paper introduces a systematic way to construct such multicast networks, and subsequently establish explicit instances to affirm the positive answer of this conjecture for \emph{infinitely many} alphabet sizes $p^L$ with respect to an \emph{arbitrary} prime $p$. On the other hand, this paper also presents explicit instances with the special property that they do not have a vector linear solution of dimension $L$ over GF(2) but have scalar linear solutions over GF($q'$) for some $q' < 2^L$, where $q'$ can be odd or even. This discovery also unveils that over a given base field, a multicast network that has a vector linear solution of dimension $L$ does not necessarily have a vector linear solution of dimension $L' > L$.

Dual-Layered MIMO Transmission for Increased Bandwidth Efficiency

A dual-layered downlink transmission scheme is proposed for intrinsically amalgamating multiple-input–multiple-output (MIMO) spatial multiplexing (SMX) with spatial modulation (SM). The proposed scheme employs a classic SMX transmission that is known to offer superior bandwidth efficiency (BE) compared with SM. We exploit receive-antenna-based SM (RSM) on top of this transmission as an enhancement of the BE. The RSM here is applied to the combined spatial and power-level domain not by activating and deactivating the RAs but rather by choosing between two power levels $\{P_1,P_2\}$ for the received symbols in these antennas. In other words, the combination of symbols received at a power level $P_1$ carries information in the spatial domain in the same manner as the combination of nonzero elements in the receive symbol vector carries information in the RSM transmission. This allows for the coexistence of RSM with SMX, and the results show increased BE for the proposed scheme compared with both SMX and SM. To characterize the proposed scheme, we carry out a mathematical analysis of its performance, and we use this to optimize the ratio between $P_1$ and $P_2$ for attaining the minimum error rates. Our analytical and simulation results demonstrate significant BE gains for the proposed scheme compared with conventional SMX and SM.

Rate‐varying space‐time coding scheme for multiple‐input multiple‐output fading channel with zero‐forcing receiver

In this study, a rate-varying space-time pre-coding scheme for a multiple-input multiple-output system in a fast-fading channel is proposed. In particular, unlike the traditional fixed rate space-time coding scheme, the transmitter which has no channel state information keeps choosing a pre-coding matrix randomly at each space-time symbol period to deliver the information until it is successfully decoded by the receiver. Since the number of received symbol vectors needed to successfully decode the information is determined by the channel realisation, the coding rate of the proposed scheme can adapt to the fast-fading channel. The exact expression of the equivalent signal-to-noise ratio gain after the post-combining and zero-forcing equalisation at the receiver side is derived, and a design criteria to achieve optimal performance in terms of average error probability and ergodic capacity under the mean power constraint is provided. Based on this, a minimal variance pre-coding (MVP) scheme is designed, which is compared with an intuitive Gaussian pre-coding scheme. It is shown that the MVP scheme has better performance and could adapt to different channel conditions.

Precoded spatial multiplexing MIMO system with spatial component interleaver.

In this paper, the performance of precoded bit-interleaved coded modulation (BICM) spatial multiplexing multiple-input multiple-output (MIMO) system with spatial component interleaver is investigated. For the ideal precoded spatial multiplexing MIMO system with spatial component interleaver based on singular value decomposition (SVD) of the MIMO channel, the average pairwise error probability (PEP) of coded bits is derived. Based on the PEP analysis, the optimum spatial Q-component interleaver design criterion is provided to achieve the minimum error probability. For the limited feedback precoded proposed scheme with linear zero forcing (ZF) receiver, in order to minimize a bound on the average probability of a symbol vector error, a novel effective signal-to-noise ratio (SNR)-based precoding matrix selection criterion and a simplified criterion are proposed. Based on the average mutual information (AMI)-maximization criterion, the optimal constellation rotation angles are investigated. Simulation results indicate that the optimized spatial multiplexing MIMO system with spatial component interleaver can achieve significant performance advantages compared to the conventional spatial multiplexing MIMO system.

Successive interference cancellation aided sphere decoder for multi-input multi-output systems

In this paper, sphere decoding algorithms are proposed for both hard detection and soft processing in multi-input multi-output (MIMO) systems. Both algorithms are based on the complex tree structure to reduce the complexity of searching the unique minimum Euclidean distance and multiple Euclidean distances, and obtain the corresponding transmit symbol vectors. The novel complex hard sphere decoder for MIMO detection is presented first, and then the soft processing of a novel sphere decoding algorithm for list generation is discussed. The performance and complexity of the proposed techniques are demonstrated via simulations in terms of bit error rate (BER), the number of nodes accessed and floating-point operations (FLOPS).

Low-Complexity Group Alternate Iterative List Detection for MIMO Systems

In this paper, we propose a low-complexity group alternate iterative list (GAIL) detection algorithm for MIMO systems. By utilizing the recursive interference suppression and successive interference cancellation techniques, the symbol vector can be partitioned into many subgroups. Subsequently, symbols in each subgroup are detected in terms of the K-best detector. The inter-group interference is effectively mitigated in the GAIL algorithm by creating a candidate list and iteratively correcting the unreliable symbols for the detection result. We provide the performance-complexity tradeoff based on different feasible parameter settings. The numerical results demonstrate that the GAIL algorithm can achieve close-to-optimal performance while maintaining low computational complexity. In addition, the running speed of the GAIL algorithm can be dramatically increased using parallel processing in real-time communication systems.

Link-Adaptable Vector-Perturbation ZFBF Precoder for Multi-Point 3D-Beamformers

A link adaptation scheme is devised for vector-perturbation (VP) zero-forcing beamforming (ZFBF) MIMO precoding and a link-adaptable VP-ZFBF precoder is applied to multi-point three-dimensional (3D) beamformers to be used in mmWave-band wireless access systems. Channel coding schemes used in current systems, e.g., turbo codes, possess systematic code structures. The VP gain can thus be predicted by searching for perturbation vectors for the symbol vectors mapped from information bits. On the basis of this principle, we constructed an efficient iterative modulation-and-coding-set (MCS) selection procedure for VP-ZFBF precoding. Simulation results demonstrate that our proposed scheme suitably passed on the VP gain to the selection of an appropriate higher-rate MCS index and thus achieved high throughputs by incorporating with multi-point 3D-beamformers.

A low-complexity hybrid algorithm based on particle swarm and ant colony optimization for large-MIMO detection

With rapid increase in demand for higher data rates, multiple-input multiple-output (MIMO) wireless communication systems are getting increased research attention because of their high capacity achieving capability. However, the practical implementation of MIMO systems rely on the computational complexity incurred in detection of the transmitted information symbols. The minimum bit error rate performance (BER) can be achieved by using maximum likelihood (ML) search based detection, but it is computationally impractical when number of transmit antennas increases. In this paper, we present a low-complexity hybrid algorithm (HA) to solve the symbol vector detection problem in large-MIMO systems. The proposed algorithm is inspired from the two well known bio-inspired optimization algorithms namely, particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm. In the proposed algorithm, we devise a new probabilistic search approach which combines the distance based search of ants in ACO algorithm and the velocity based search of particles in PSO algorithm. The motivation behind using the hybrid of ACO and PSO is to avoid premature convergence to a local solution and to improve the convergence rate. Simulation results show that the proposed algorithm outperforms the popular minimum mean squared error (MMSE) algorithm and the existing ACO algorithms in terms of BER performance while achieve a near ML performance which makes the algorithm suitable for reliable detection in large-MIMO systems. Furthermore, a faster convergence to achieve a target BER is observed which results in reduction in computational efforts.

Frequency-Domain Diversity Combining Receiver for ACO-OFDM System

This paper proposes a new receiver based on frequency-domain diversity combining (FDDC) for an asymmetrically clipped optical orthogonal frequency-division multiplexing (ACO-OFDM) system. Compared with its time-domain diversity combining (TDDC) counterpart, the FDDC receiver is capable of more effectively exploiting the frequency selectivity of the channel to further improve the detection performance, which is proved by postcombining signal-to-noise-ratio (SNR) analysis. An enhanced version of the FDDC receiver, which is called eFDDC, is also proposed subsequently. It allows for optimal selection among multiple sets of candidate symbol vectors based on signum matrix estimation. Simulation results show that the proposed FDDC and eFDDC receivers outperform the TDDC receiver. With a sufficient number of candidate symbol vector sets, the eFDDC receiver even outperforms a kind of iterative receiver, which is known to have superior performance in current literature, yet the complexity of eFDDC is lower due to the avoidance of matrix inversion. Those facts suggest that the FDDC and eFDDC receivers are strong receiver candidates for the ACO-OFDM system.

Robust MMSE Precoding Based on Switched Relaying and Side Information for Multiuser MIMO Relay Systems

This paper proposes a novel precoding scheme for multiuser multiple-input–multiple-output (MIMO) relay systems in the presence of imperfect channel state information (CSI). The base station (BS) and the MIMO relay station (RS) are both equipped with the same codebook of unitary matrices. According to each element of the codebook, we create a latent precoding matrix pair, namely, a BS precoding matrix and an RS precoding matrix. The RS precoding matrix is formed by multiplying the appropriate unitary matrix from the codebook by a power scaling factor. Based on the given CSI and a block of transmit symbols, the optimum precoding matrix pair, within the class of all possible latent precoding matrix pairs derived from the various unitary matrices, is selected by a suitable selection mechanism for transmission, which is designed to minimize the squared Euclidean distance between the pre-estimated received vector and the true transmit symbol vector. We develop a minimum mean-square-error (MMSE) design algorithm to construct the latent precoding matrix pairs. In the proposed scheme, rather than sending the complete processing matrix, only the index of the unitary matrix and its power scaling factor are sent by the BS to the RS. This significantly reduces the overhead. Simulation results show that, compared with other recently reported precoding algorithms, the proposed precoding scheme is capable of providing improved robustness against the effects of CSI estimation errors and multiuser interference.

Opportunistic vector perturbation with superposition signalling

In this letter, we first put forward a new basic problem to the famous vector perturbation (VP) precoding that whether the extended constellation of VP could transmit more information bits. Then, we propose an opportunistic vector perturbation (OVP) precoding with superposition signalling scheme, which is aimed at getting the performance of VP closer to capacity limit. The main idea is using subsequent recoverable data symbol vector to perturb currently transmitting data symbol vector opportunistically. Analysis and simulation results show that the proposed OVP can transmit more valid information bits than conventional VP at the same transmit power, modulation order and number of antennas.

Low-Complexity ML Detectors for Generalized Spatial Modulation 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.

Assessment of Joint Interactions between Respiration and Baroreflex Activity using Joint Symbolic Dynamics in Heart Failure Patients.

In this paper we employed a novel approach based on joint symbolic dynamics (JSD) to study interaction between respiratory phase and baroreflex activity. Electrocardiogram (ECG) and blood pressure recordings from six participants with history of heart failure were included in this study. First, the ECG R-peaks and systolic blood pressure indices were detected using parabolic fitting. Second, the respiratory signal was derived from Frank orthogonal ECG leads using QRS slopes and R-wave angles. Third, time series of R-R intervals and systolic blood pressure (SBP) were extracted, and respiratory phases were obtained using the Hilbert transform. Subsequently, each series was transformed into binary symbol vectors based on their successive changes and words of length '2' were formed. Baroreflex patterns were studied using word combinations representing baroreflex activity for specific changes in respiratory phases. Baroreflex activity was significantly higher for alternating low-high/high-low heart rate and SBP during inspiration as compared to continuous increase or decrease in heart rate and SBP ([Formula: see text], wRP=11: 39.1±9.3% vs. [Formula: see text], wRP=11: 6.4±3.9%, p<0.0001).

Compressive Soft Forwarding in Network-Coded Multiple-Access Relay Channels

In this paper, we design a novel estimate-and-forward (EF) protocol in multiple-access relay channels (MARCs) where two correlated sources transmit their information to the common destination with the help of a relay. With the application of network coding (NC) at the relay, the network-coded soft symbols contain redundant information due to the correlated sources, which can be compressed before being forwarded. Specifically, we first derive the value of the network-coded soft symbol by considering the correlation of the two sources. Due to this correlation, the majority of soft symbol values are close to one. Then, we convert network-coded soft symbol vector into a sparse vector, which is suitable for compression by using the compressive sensing (CS). Next, we analyze the performance of the proposed EF protocol by comparing the soft symbol value and received signal-to-noise ratio (SNR) with the conventional EF protocol. Simulations show that our protocol can achieve the same bit error rate (BER) as the conventional EF protocol with proper compression rates, and it consumes much less transmission time due to the compression.

Signal Design for Multiple Antenna Systems With Spatial Multiplexing and Noncoherent Reception

We consider signal design for multiple-input multiple-output (MIMO) systems with spatial multiplexing and noncoherent reception in a flat Rayleigh fading environment. We obtain the symbol vector error probability (SEP) of noncoherent detection as a function of the signal amplitude levels and the effective precoder matrix, which depends on the precoder structure and the channel transmit covariance matrix. It is found that at high values of average signal-to-noise ratio (SNR) per symbol vector per diversity branch, the SEP tends to reach saturation. To obtain the best error performance, we need to find the optimal precoder and constellation parameters that minimize the saturation value of the SEP. Optimization of the saturation value of the SEP is carried out using the binary signal constellations {0,1} and $\{1,-r\}$ , $r > 1$ , two transmit antennas, a real valued transmit covariance matrix with equal diagonal elements, and a diagonal precoder. Results show that the optimal values of the parameters are almost independent of the number of receive antennas when the number of receive antennas is large. We also observe that the error performance of MIMO noncoherent reception is better than that of MIMO coherent reception with imperfect channel state information for a certain range of average SNR.

Power Allocation Optimization: Linear Precoding Adapted to NB-LDPC Coded MIMO Transmission

In multiple-input multiple-output (MIMO) transmission systems, the channel state information (CSI) at the transmitter can be used to add linear precoding to the transmitted signals in order to improve the performance and the reliability of the transmission system. This paper investigates how to properly join precoded closed-loop MIMO systems and nonbinary low density parity check (NB-LDPC). Theqelements in the Galois field, GF(q), are directly mapped toqtransmit symbol vectors. This allows NB-LDPC codes to perfectly fit with a MIMO precoding scheme, unlike binary LDPC codes. The new transmission model is detailed and studied for several linear precoders and various designed LDPC codes. We show that NB-LDPC codes are particularly well suited to be jointly used with precoding schemes based on the maximization of the minimum Euclidean distance (max-dmin) criterion. These results are theoretically supported by extrinsic information transfer (EXIT) analysis and are confirmed by numerical simulations.