Lower Symbol Error Rate Research Articles

Holographic Read-Only Memory Fabricated by Deposition of Reflector after Writing Process with Aromatic Photopolymer Recording Layer

The deposition of reflector after writing (DRAW) process has been proposed for the fabrication of reflective-type holographic read-only memories. In the DRAW process, a reflector is deposited on a recording medium after signal writing, resulting in the reduction of noise holograms written by reflected beams from a reflector in the write process. Significant improvements are experimentally confirmed in read and write (R/W) performances in DRAW-processed holographic media. The combination of the DRAW process and an aromatic photopolymer recording material realizes low noise, high signal-to-noise ratio, and low symbol error rate characteristics at large multiplexing numbers up to 1020. In conventional reflective-type holographic media, ghost noise is superimposed on the readout signal, causing deterioration in R/W characteristics. The wave vector analyses clarify the mechanism by which the noise holograms are written and ghost noise is superimposed on the signal beam in the conventional media.

Optimally balancing data and pilot power for adaptive MIMO-OFDM systems

While pilot symbols facilitate channel estimation, they reduce the transmit energy for data symbols per OFDM symbol under a fixed total transmit power constraint. In this paper, we investigate the effect of the pilot-to-data power ratio (PDPR) on multilevel quadrature amplitude modulation (M-QAM) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with adaptive modulation in order to provide a basic framework for finding the optimal PDPR in current and emerging standards using MIMO-OFDM. In particular, we derive the optimal PDPR in terms of average symbol error rate (SER) and spectral efficiency according to different receiver types such as zero-forcing (ZF) and minimum mean square error (MMSE). Employing the optimal PDPR results in higher spectral efficiency and lower SER without using any additional resource.

Capacity-aware linear MMSE detector for OFDM-SDMA systems

This paper presents a capacity-aware linear minimum mean square error (MMSE) detector for orthogonal frequency-division multiplexing/space-division multiple access (OFDM-SDMA) applications. We derive closed-form expressions for the signal-to-interference plus noise ratio (SINR) performance of OFDM-SDMA systems with linear MMSE detection method, where capacity enhancements have been introduced in the transmit/receive weight computations. Using the derived expressions, we compare the performance of the proposed scheme with the regular multiple-input multiple-output (MIMO)-MMSE system in OFDM-SDMA applications. It is shown that in enhancing the system capacity, the capacity-aware MIMO-MMSE scheme also enhances the SINR, resulting in lower symbol error rate (SER) compared to the conventional MIMO-MMSE beamforming schemes.

Tomlinson-harashima precoder with tilted constellation for reducing transmission power

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.

Efficient fast blind equalization with two‐stage single/multilevel modulus and DD algorithm for 64/256/1024qam wired cable communications

In this paper, an efficient blind equalization with two‐stage single/multilevel modulus and decision directed (DD) least mean square (LMS)‐like algorithm is proposed for high‐order QAM (64/256/1024QAM) cable modem systems. In wire‐lined cable systems, it seems that the cable channel may display a nearly invariant multi‐path effect over a considerable time, and then the blind equalizer can have the capability to recover the received high‐order QAM symbols on wired cables. The proposed blind equalization algorithm applies a two‐stage convergence scheme, which results from the multi‐modulus algorithm (MMA), sometimes called the modified constant modulus algorithm (MCMA), and the DD LMS‐like method. In the first equalization stage, the joint MMA and DD LMS‐like methods are applied for the purpose of fast convergence. When the convergence process reaches an acceptably steady condition, the convergence detector changes the process into the second stage. In the second equalization stage, the enhanced DD LMS‐like method with the generalized multi‐modulus is applied to reduce the mean square error (MSE) of equalizations further. For high‐order QAM cable systems, the proposed method performs faster than the previous well‐known blind methods do. Simultaneously, the proposed algorithm also provides less MSE and lower symbol error rate (SER) than the other blind methods do at the same signal‐to‐noise ratio (SNR).

Two-Dimensional Modulation for Holographic Data Storage Systems

Page-oriented holographic data storage systems (HDSSs) generally use spatial light modulators (SLMs) to generate two-dimensional (2D) digital patterns, so-called data pages. These data pages are stored via interference patterns of the object and reference beam in the holographic medium and are retrieved from the medium by exposing it to the reference beam. The reconstructed data pages are then detected by a matrix detector. One important challenge in designing an HDSS is to develop a suitable modulation, which takes into account the specific characteristics of the transfer channel and enables high data capacity, high data transfer rate and low symbol error rate (SER). In this paper, we present a new method for modulating data in an HDSS. The main idea is to adapt the general concept of 2D run-length limited modulation (RLL) to a numerically more efficient implementation usable for HDSS. We demonstrate that this 2D block coding method with a constant weight and a sparse code increases the amount of user data per data page, while the SER remains low compared with standard sparse modulation coding. This results in a higher data rate and higher data density.

Performance of UWB $N$-Orthogonal PPM in AWGN and Multipath Channels

This paper studies pulse-based M-ary ultrawideband communications analyzing both the symbol error rate (SER) and the information theoretic capacity C for single-link communications over both additive white Gaussian noise and multipath channels (taking into account random variations in both energy and correlation values). In particular, we consider M-ary N-orthogonal pulse-position-modulated (PPM) signals with numerical pulse-position optimization to exploit the negative values in the pulse correlation function. On one hand, the N-orthogonal PPM (N-OPPM) signals can accommodate a larger value of M than the nonoverlapping orthogonal PPM (OPPM) signals for a constant frame size and pulse width, allowing increasing the symbol transmission rate and/or the use of error correcting codes. However, N-OPPM requires a receiver with N correlators, while the receiver for OPPM requires one correlator. On the other hand, the pulse positions of the N-OPPM signals can be manipulated to shape the power spectrum density and decrease the level of the discrete components. However, this PPM manipulation changes the N-OPPM correlation properties of the signal set. At the same time, N-OPPM signals have equal or better performance than OPPM signals. More specifically, simulations show that for low values of M, the N-OPPM has lower SER and higher C than OPPM for the same signal-to-noise ratio (SNR), and that for larger values of M, it achieves similar SER and C. Hence, N-OPPM signals allow a tradeoff between transmission rate, signal performance, and receiver complexity

Frequency-domain equalization for distributed terrestrial DTV transmission environments

The 8-VSB modulation, the transmission standard for the terrestrial digital television (DTV) of the Advanced Television Systems Committee (ATSC), suffers from multipath fading because it conveys information on the amplitude. To solve this problem, decision feedback equalizers (DFEs) have been commonly used in the terrestrial DTV receivers. However, under severe channels, such as a 0-dB ghost channel or a distributed transmission environment, the DFEs have shown unstable convergence due to the error propagation caused by decision errors. Instead of unstable time-domain DFE schemes, by proposing a frequency-domain direct-inversion equalization method, we achieve stable equalization and low symbol error rates. To secure the existence of a channel inverse, channel-matched filtering and noncausal filtering are carried out before equalization. Simulation results show that the proposed method performs much better than the existing DFE schemes in terms of both the stability and the symbol error rates.

Fully complex extreme learning machine

Recently, a new learning algorithm for the feedforward neural network named the extreme learning machine (ELM) which can give better performance than traditional tuning-based learning methods for feedforward neural networks in terms of generalization and learning speed has been proposed by Huang et al. In this paper, we first extend the ELM algorithm from the real domain to the complex domain, and then apply the fully complex extreme learning machine (C-ELM) for nonlinear channel equalization applications. The simulation results show that the ELM equalizer significantly outperforms other neural network equalizers such as the complex minimal resource allocation network (CMRAN), complex radial basis function (CRBF) network and complex backpropagation (CBP) equalizers. C-ELM achieves much lower symbol error rate (SER) and has faster learning speed.

Concurrent algorithm for blind adaptation of DFE

Based on a concurrent deconvolution method, a new algorithm for blind adaptation of decision feedback equalisers (DFEs) is proposed. It avoids degenerated solutions and achieves faster convergence and lower symbol error rate than the blind algorithms in the literature. Simulation results illustrate its good behaviour for a digital television channel.

A recursive QR approach to adaptive equalization of time-varying MIMO channels

This paper presents a novel adaptive equalization algorithm for time-varying MIMO systems with ISI channel conditions. The algorithm avoids channel estimation before equalization and leads to a direct QR-based procedure for updating the equalizer coefficients to track the time- varying channel characteristics. Our approach does not require precise channel estimation and needs relatively few pilot symbols for satisfactory equalization. The theoretical foundations of the pro- posed algorithm are rooted in signal recovery results derived from the generalized Bezout identity and the finite alphabet property inherent in digital communication schemes. Concerning the con- vergence behavior of the algorithm, we address the following three issues: existence of fixed points, exclusiveness of fixed points, and robustness under noise disturbance and parameter selection. The equalizer demonstrates promising capability in achieving low symbol error rates for a very broad range of SNRs. Simulation results are presented confirming that this approach outperforms the more traditional recursive least squares (RLS) adaptive equalizer for this application and rivals the performance of MMSE equalizers requiring channel knowledge.

Minimum Variance Linear Receivers for Multiaccess MIMO Wireless Systems with Space-Time Block Coding

We consider the problem of joint space-time decoding and multiaccess interference (MAI) rejection in multiuser multiple-input multiple-output (MIMO) wireless communication systems. We address the case when both the receiver and multiple transmitters are equipped with multiple antennas and when space-time block codes (STBCs) are used to send the data simultaneously from each transmitter to the receiver. A new linear receiver structure is developed to decode the data sent from the transmitter-of-interest while rejecting MAI, self-interference, and noise. The proposed receivers are designed by minimizing the output power subject to constraints that zero-force self-interference and/or preserve a unity gain for all symbols of the transmitter-of-interest. Simulation results show that in multiaccess scenarios, the proposed techniques have substantially lower symbol error rates as compared with the matched filter (MF) receiver, which is equivalent to the maximum likelihood (ML) space-time decoder in the point-to-point MIMO communication case.

Differential space time block codes using nonconstant modulus constellations

We propose differential space time block codes (STBC) using nonconstant modulus constellations, e.g., quadrature amplitude modulation (QAM), which cannot be utilized in the conventional differential STBC. Since QAM constellations have a larger minimum distance compared with the phase shift keying (PSK), the proposed method has the advantage of signal-to-noise ratio (SNR) gain compared with conventional differential STBC. The QAM signals are encoded in a manner similar to that of the conventional differential STBC. To decode nonconstant modulus signals, the received signals are normalized by the channel power estimated forgoing training symbols and then decoded with a conventional QAM decoder. Assuming the knowledge of the channel power at the receiver, the symbol error rate (SER) bound of the proposed method under independent Rayleigh fading assumption is derived, which shows better SER performance than the conventional differential STBC. When the transmission rate is more than 3 bits/channel use in time-varying channels, the simulation results demonstrate that the proposed method with the channel power estimation outperforms the conventional differential STBC. Specifically, the posed method using the channel power estimation obtains a 7.3 dB SNR gain at a transmission rate of 6 bits/channel use in slow fading channels. Although the performance gap between the proposed method and the conventional one decreases as the Doppler frequency increases, the proposed method still exhibits lower SER than the conventional one, provided the estimation interval L is chosen carefully.