arXiv subjects

L. Landau

Publications and source records attributed to L. Landau.

13 recordsLinked to original sources

arXiv2025

Study of Symbol Error Probability Constrained Precoding with Zero-Crossing Modulation for Wireless Systems with 1-Bit ADCs

The next generation of wireless communications systems will employ new frequency bands such as those in the upper midband, millimeter-wave and sub-terahertz frequency bands. The high energy consumption of analog-to-digital converters resulting from their high resolution constituted a major limitation for future wireless communications systems, which will require low energy consumption and low-complexity devices at the transmitter and at the receiver. In this regard, we present a novel precoding method based on quality of service constraints for a multiuser multiple-input multiple-output downlink system with 1-bit quantization and oversampling. For this scenario, we consider the time-instance zero-crossing modulation, which conveys the information into the zero-crossings of the signals. Unlike prior works the proposed constraint is given in terms of the symbol error probability related to the minimum distance to the decision threshold and is included in the proposed optimization problem that is used in the design of the precoder. Simulation results illustrate the performance of the proposed precoding method evaluated under different parameters and scenarios.

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arXiv2025

Study of Constrained Precoding with Zero-Crossing Modulation for Channels with 1-Bit Quantization and Oversampling

Future wireless communications systems are expected to operate at bands above 100GHz. The high energy consumption of analog-to-digital converters, due to their high resolution represents a bottleneck for future wireless communications systems which require low-energy consumption and low-complexity devices at the receiver. In this work, we derive a novel precoding method based on quality of service constraints for a multiuser multiple-input multiple-output downlink system with 1-bit quantization and oversampling. For this scenario, the time-instance zero-crossing modulation which conveys the information into the zero-crossings is considered. Unlike prior studies, the constraint is given regarding the symbol error probability related to the minimum distance to the decision threshold. Numerical results illustrate the performance of the proposed precoding method evaluated under different parameters

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arXiv2024

Iterative Detection and Decoding for Clustered Cell-Free Massive MIMO Networks

In this letter, we propose an iterative soft interference cancellation scheme for intra-cluster (ICL) and out-of-cluster (OCL) interference mitigation in user-centric clustered cell-free massive multiple-antenna networks. We propose a minimum mean-square error receive filter with a novel modified parallel interference cancellation scheme to mitigate ICL and OCL interference. Unlike prior work, we model the OCL interference and devise a least squares estimator to perform OCL interference estimation. An iterative detection and decoding scheme that adopts low-density parity check codes and incorporates the OCL interference estimate is developed. Simulations assess the proposed scheme against existing techniques in terms of bit error rate performance.

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arXiv2024

Decentralized and Centralized IDD Schemes for Cell-Free Networks

In this paper, we propose iterative interference cancellation schemes with access points selection (APs-Sel) for cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Closed-form expressions for centralized and decentralized linear minimum mean square error (LMMSE) receive filters with APs-Sel are derived assuming imperfect channel state information (CSI). Furthermore, we develop a list-based detector based on LMMSE receive filters that exploits interference cancellation and the constellation points. A message-passing-based iterative detection and decoding (IDD) scheme that employs low-density parity-check (LDPC) codes is then developed. Moreover, log-likelihood ratio (LLR) refinement strategies based on censoring and a linear combination of local LLRs are proposed to improve the network performance. We compare the cases with centralized and decentralized processing in terms of bit error rate (BER) performance, complexity, and signaling under perfect CSI (PCSI) and imperfect CSI (ICSI) and verify the superiority of the distributed architecture with LLR refinements.

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arXiv2024

Iterative Detection and Decoding Schemes with LLR Refinements in Cell-Free Massive MIMO Networks

In this paper, we propose low-complexity local detectors and log-likelihood ratio (LLR) refinement techniques for a coded cell-free massive multiple input multiple output (CF- mMIMO) systems, where an iterative detection and decoding (IDD) scheme is applied using parallel interference cancellation (PIC) and access point (AP) selection. In particular, we propose three LLR processing schemes based on the individual processing of the LLRs of each AP, LLR censoring, and a linear combination of LLRs by assuming statistical independence. We derive new closed-form expressions for the local soft minimum mean square error (MMSE)-PIC detector and receive matched filter (RMF). We also examine the system performance as the number of iterations increases. Simulations assess the performance of the proposed techniques against existing approaches.

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arXiv2022

Iterative Detection and Decoding for Cell-Free Massive Multiuser MIMO with LDPC Codes

This paper proposes an iterative detection and decoding (IDD) scheme for a cell free massive multiple input multiple output (CF-mMIMO) system. Users send coded data to the access points (APs), which is jointly detected at central processing unit (CPU). The symbols are exchanged iteratively in the form of log likelihood ratios (LLRs) between the detector and the low-density parity check codes (LPDC) decoder, increasing the coded system's performance. We propose a list-based multi-feedback diversity with successive interference cancellation (MF-SIC) to improve the performance of the CF-mMIMO. Furthermore, the proposed detector is compared with the parallel interference cancellation (PIC) and MF-PIC schemes. Finally, the bit error rate (BER) performance of CF-mMIMO is compared with the co-located mMIMO (Col-mMIMO).

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arXiv2021

Dynamic Oversampling Tecniques for 1-Bit ADCs in Large-Scale MIMO Systems

In this work, we investigate dynamic oversampling techniques for large-scale multiple-antenna systems equipped with low-cost and low-power 1-bit analog-to-digital converters at the base stations. To compensate for the performance loss caused by the coarse quantization, oversampling is applied at the receiver. Unlike existing works that use uniform oversampling, which samples the signal at a constant rate, a novel dynamic oversampling scheme is proposed. The basic idea is to perform time-varying nonuniform oversampling, which selects samples with nonuniform patterns that vary over time. We consider two system design criteria: a design that maximizes the achievable sum rate and another design that minimizes the mean square error of detected symbols. Dynamic oversampling is carried out using a dimension reduction matrix $\mathbfΔ$, which can be computed by the generalized eigenvalue decomposition or by novel submatrix-level feature selection algorithms. Moreover, the proposed scheme is analyzed in terms of convergence, computational complexity and power consumption at the receiver. Simulations show that systems with the proposed dynamic oversampling outperform those with uniform oversampling in terms of computational cost, achievable sum rate and symbol error rate performance.

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arXiv2020

Study of Channel Estimation Algorithms for Large-Scale Multiple-Antenna Systems using 1-Bit ADCs and Oversampling

Large-scale multiple-antenna systems with large bandwidth are fundamental for future wireless communications, where the base station employs a large antenna array. In this scenario, one problem faced is the large energy consumption as the number of receive antennas scales up. Recently, low-resolution analog-to-digital converters (ADCs) have attracted much attention. Specifically, 1-bit ADCs are suitable for such systems due to their low cost and low energy consumption. This paper considers uplink large-scale multiple-antenna systems with 1-bit ADCs on each receive antenna. We investigate the benefits of using oversampling for channel estimation in terms of the mean square error and symbol error rate performance. In particular, low-resolution aware channel estimators are developed based on the Bussgang decomposition for 1-bit oversampled systems and analytical bounds on the mean square error are also investigated. Numerical results are provided to illustrate the performance of the proposed channel estimation algorithms and the derived theoretical bounds.

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arXiv2019

Investigation of Channel Estimation Techniques with 1-bit Quantization and Oversampling for Multiple-Antenna Systems

Large-scale multiple-antenna systems have been identified as a promising technology for the next generation of wireless systems. However, by scaling up the number of receive antennas the energy consumption will also increase. One possible solution is to use low-resolution analog-to-digital converters at the receiver. This paper considers large-scale multiple-antenna uplink systems with 1-bit analog-to-digital converters on each receive antenna. Since oversampling can partially compensate for the information loss caused by the coarse quantization, the received signals are firstly oversampled by a factor M. We then propose a low-resolution aware linear minimum mean-squared error channel estimator for 1-bit oversampled systems. Moreover, we characterize analytically the performance of the proposed channel estimator by deriving an upper bound on the Bayesian Cramér-Rao bound. Numerical results are provided to illustrate the performance of the proposed channel estimator.

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arXiv2019

Study of Channel Estimation with Oversampling for 1-bit Large-Scale MIMO Systems

In this paper, we propose an oversampling based low-resolution aware least squares channel estimator for large-scale multiple-antenna systems with 1-bit analog-to-digital converters on each receive antenna. To mitigate the information loss caused by the coarse quantization, oversampling is applied at the receiver, where the sampling rate is faster than the Nyquist rate. We also characterize analytical performances, in terms of the deterministic Cramér-Rao bounds, on estimating the channel parameters. Based on the correlation of the filtered noise, both the Fisher information for white noise and a lower bound of Fisher information for colored noise are provided. Numerical results are provided to illustrate the mean square error performances of the proposed channel estimator and the corresponding Cramér-Rao bound as a function of the signal-to-noise ratio.

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arXiv2018

Study of SIC and RLS Channel Estimation for Large-Scale Antenna Systems with 1-Bit ADCs

We propose a novel low-resolution-aware recursive least squares channel estimation algorithm for uplink multi-user multiple-input multiple-output systems. In order to reduce the energy consumption, 1-bit ADCs are used on each receive antenna. The loss of performance can be recovered by the large-scale antenna arrays at the receiver. The proposed adaptive channel estimator can mitigate the distortions due to the coarse quantization. Moreover, we propose a low-resolution-aware minimum mean square error based successive interference canceler to successively mitigate the multiuser interference. Simulation results show good performance of the system in terms of mean square error and bit error rate.

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arXiv2017

Study of Iterative Detection and Decoding for Large-Scale MIMO Systems with 1-Bit ADCs

We present a novel iterative detection and decoding scheme for the uplink of large-scale multiuser multiple-antenna systems. In order to reduce the receiver's energy consumption and computational complexity, 1-bit analog-to-digital converters are used in the front-end. The performance loss due to the 1-bit quantization can be mitigated by using large-scale antenna arrays. We propose a linear low-resolution-aware minimum mean square error detector for soft multiuser interference mitigation. Moreover, short block length low-density parity-check codes are considered for avoiding high latency. In the channel decoder, a quasi-uniform quantizer with scaling factors is devised to lower the error floor of LDPC codes. Simulations show good performance of the system in terms of bit error rate as compared to prior work.

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arXiv2013

Robust Adaptive Beamforming Algorithms Based on the Constrained Constant Modulus Criterion

We present a robust adaptive beamforming algorithm based on the worst-case criterion and the constrained constant modulus approach, which exploits the constant modulus property of the desired signal. Similarly to the existing worst-case beamformer with the minimum variance design, the problem can be reformulated as a second-order cone (SOC) program and solved with interior point methods. An analysis of the optimization problem is carried out and conditions are obtained for enforcing its convexity and for adjusting its parameters. Furthermore, low-complexity robust adaptive beamforming algorithms based on the modified conjugate gradient (MCG) and an alternating optimization strategy are proposed. The proposed low-complexity algorithms can compute the existing worst-case constrained minimum variance (WC-CMV) and the proposed worst-case constrained constant modulus (WC-CCM) designs with a quadratic cost in the number of parameters. Simulations show that the proposed WC-CCM algorithm performs better than existing robust beamforming algorithms. Moreover, the numerical results also show that the performances of the proposed low-complexity algorithms are equivalent or better than that of existing robust algorithms, whereas the complexity is more than an order of magnitude lower.

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