arXiv subjects

Masoud Ardakani

Publications and source records attributed to Masoud Ardakani.

At least 19 recordsLinked to original sources

arXiv2026

Mapped ADMM: A Robust Algorithm for 1-Bit mMIMO Detection

Recently, it has been reported that one-bit massive MIMO (mMIMO) detection is equivalent to a binary classification problem that can be solved efficiently using support vector machine (SVM). Inspired by this result, we first reformulate SVM in a decentralized form consisting of multiple classifiers. This enables the use of the consensus alternating direction method of multipliers (CADMM), a technique that can improve robustness and performance through its inherent consensus making. We further update CADMM to output only valid constellation points and achieve significantly improved detection performance. In our method, by changing the size of the grouped classifiers, we balance the number of classifiers for consensus accuracy with sufficient data per group to ensure classifier robustness. Ultimately, we demonstrate that our proposed method significantly outperforms existing practically feasible methods for one-bit mMIMO detection.

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arXiv2024

RIS with Coupled Phase Shift and Amplitude: Capacity Maximization and Configuration Set Selection

A reconfigurable intelligent surface (RIS) is a planar surface that can enhance the quality of communication by providing control over the communication environment. Reflection optimization is one of the pivotal challenges in RIS setups. While there has been lots of research regarding the reflection optimization of RIS, most works consider the independence of the phase shift and the amplitude of RIS reflection coefficients. In practice, the phase shift and the amplitude are coupled and according to a recent study, the relation between them can be described using a function. In our work, we consider a practical system model with coupled phase shift and amplitude. We develop an efficient method for achieving capacity maximization by finding the optimal reflection coefficients of the RIS elements. The complexity of our method is linear with the number of RIS elements and the number of discrete phase shifts. We also develop a method that optimally selects the configuration set of the system, where a configuration set means a discrete set of reflection coefficient choices that a RIS element can take.

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arXiv2024

Efficient Massive Machine Type Communication (mMTC) via AMP

We propose efficient and low-complexity multiuser detection (MUD) algorithms for Gaussian multiple access channel (G-MAC) for short-packet transmission in massive machine type communications. To do so, we first formulate the G-MAC MUD problem as a sparse signal recovery problem and obtain the exact and approximate joint prior distribution of the sparse vector to be recovered. Then, we employ the Bayesian approximate message passing (AMP) algorithms with the optimal separable and non-separable minimum mean squared error (MMSE) denoisers for soft decoding of the sparse vector. The effectiveness of the proposed MUD algorithms for a large number of devices is supported by simulation results. For packets of 8 information bits, while the state-of-the-art AMP with soft-threshold denoising achieves 8/100 of the upper bound at Eb/N0 = 4 dB, the proposed algorithms reach 4/7 and 1/2 of the upper bound.

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arXiv2024

Deep Learning Based Sphere Decoding

In this paper, a deep learning (DL)-based sphere decoding algorithm is proposed, where the radius of the decoding hypersphere is learned by a deep neural network (DNN). The performance achieved by the proposed algorithm is very close to the optimal maximum likelihood decoding (MLD) over a wide range of signal-to-noise ratios (SNRs), while the computational complexity, compared to existing sphere decoding variants, is significantly reduced. This improvement is attributed to DNN's ability of intelligently learning the radius of the hypersphere used in decoding. The expected complexity of the proposed DL-based algorithm is analytically derived and compared with existing ones. It is shown that the number of lattice points inside the decoding hypersphere drastically reduces in the DL-based algorithm in both the average and worst-case senses. The effectiveness of the proposed algorithm is shown through simulation for high-dimensional multiple-input multiple-output (MIMO) systems, using high-order modulations.

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arXiv2024

Fast Successive-Cancellation Decoding of 2 x 2 Kernel Non-Binary Polar Codes: Identification, Decoding and Simplification

Non-binary polar codes (NBPCs) decoded by successive cancellation (SC) algorithm have remarkable bit-error-rate performance compared to the binary polar codes (BPCs). Due to the serial nature, SC decoding suffers from large latency. The latency issue in BPCs has been the topic of extensive research and it has been notably resolved by the introduction of fast SC-based decoders. However, the vast majority of research on NBPCs is devoted to issues concerning design and efficient implementation. In this paper, we propose fast SC decoding for NBPCs constructed based on 2 x 2 kernels. In particular, we identify various non-binary special nodes in the SC decoding tree of NBPCs and propose their fast decoding. This way, we avoid traversing the full decoding tree and significantly reduce the decoding delay compared to symbol-by-symbol SC decoding. We also propose a simplified NBPC structure that facilitates the procedure of non-binary fast SC decoding. Using our proposed fast non-binary decoder, we observed an improvement of up to 95% in latency concerning the original SC decoding. This is while our proposed fast SC decoder for NBPCs incurs no error-rate loss.

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arXiv2023

Optimal Configuration of Reconfigurable Intelligent Surfaces with Arbitrary Discrete Phase Shifts

We address the reflection optimization problem for a reconfigurable intelligent surface (RIS), where the RIS elements feature a set of non-uniformly spaced discrete phase shifts. This is motivated by the actual behavior of practical RIS elements, where it is shown that a uniform phase shift assumption is not realistic. A problem is formulated to find the optimal refection amplitudes and reflection phase shifts of the RIS elements such that the channel capacity of the target user is maximized. We first prove that in the optimal configuration, each RIS element is either turned off or operates at maximum amplitude. We then develop a method that finds the optimal reflection amplitudes and phases with complexity linear in the number of RIS elements. Some new and interesting insight into the reflection optimization problem is also provided.

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arXiv2023

Low Complexity Time Synchronization for Zero-padding based Waveforms

The discussion on using zero padding (ZP) instead of a cyclic prefix (CP) for enhancing channel estimation and equalization performance is a recurring topic in waveform design for future wireless systems that high spectral efficiency and location awareness are the key factors. This is particularly true for orthogonal signals, such as orthogonal frequency-division multiplexing (OFDM). ZP-OFDM is appealing for joint communications and sensing (JCS) in 6G networks because it takes the advantage of both OFDM and pulse radar. In term of communication, ZP-OFDM compared to CP-OFDM, has higher power efficiency and lower bit error rate (BER). However, time synchronization is challenging in ZP-OFDM systems due to the lack of CP. In terms of sensing, ZP facilitates ranging methods, such as time-sum-of-arrival (TSOA). In this paper, we propose a moment-based timing offset (TO) estimator for multiple-input multiple-output (MIMO) ZP-OFDM system without the need for pilots. We then introduce the which significantly improves the estimation accuracy of the previous estimator. We show that the proposed method asymptotically reaches the maximum likelihood (ML) estimator. Simulation results show very high probability of lock-in for the proposed estimators under various practical scenarios.

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arXiv2023

Maximum Likelihood Time Synchronization for Zero-padded OFDM

Existing Orthogonal Frequency-Division Multiplexing (OFDM) variants based on cyclic prefix (CP) allow for efficient time synchronization, but suffer from lower power efficiency compared to zero-padded (ZP)-OFDM. Because of its power efficiency, ZP-OFDM is considered as an appealing solution for the emerging low-power wireless systems. However, in the absence of CP, time synchronization in ZP-OFDM is a very challenging task. In this paper, the non-data-aided (NDA) maximum-likelihood (ML) time synchronization for ZP-OFDM is analytically derived. We show that the optimal NDA-ML synchronization algorithm offers a high lock-in probability and can be efficiently implemented using Monte Carlo sampling (MCS) technique in combination with golden-section search. To obtain the optimal NDA-ML time synchronization algorithm, we first derive a closed-form expression for the joint probability density function (PDF) of the received ZP-OFDM samples in frequency-selective fading channels. The derived expression is valid for doubly-selective fading channels with mobile users as well. The performance of the proposed synchronization algorithm is evaluated under various practical settings through simulation experiments. It is shown that the proposed optimal NDA-ML synchronization algorithm and its MCS implementation substantially outperforms existing algorithms in terms of lock-in probability.

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arXiv2022

Activity Detection for Grant-Free NOMA in Massive IoT Networks

Recently, grant-free transmission paradigm has been introduced for massive Internet of Things (IoT) networks to save both time and bandwidth and transmit the message with low latency. In order to accurately decode the message of each device at the base station (BS), first, the active devices at each transmission frame must be identified. In this work, first we investigate the problem of activity detection as a threshold comparing problem. We show the convexity of the activity detection method through analyzing its probability of error which makes it possible to find the optimal threshold for minimizing the activity detection error. Consequently, to achieve an optimum solution, we propose a deep learning (DL)-based method called convolutional neural network (CNN)-activity detection (AD). In order to make it more practical, we consider unknown and time-varying activity rate for the IoT devices. Our simulations verify that our proposed CNN-AD method can achieve higher performance compared to the existing non-Bayesian greedy-based methods. This is while existing methods need to know the activity rate of IoT devices, while our method works for unknown and even time-varying activity rates

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arXiv2020

An Approximate Maximum Likelihood Time Synchronization Algorithm for Zero-padded OFDM in Channels with Impulsive Gaussian Noise

In wireless communication systems, Orthogonal Frequency-Division Multiplexing (OFDM) includes variants using either a cyclic prefix (CP) or a zero padding (ZP) as the guard interval to avoid inter-symbol interference. OFDM is ideally suited to deal with frequency-selective channels and additive white Gaussian noise (AWGN); however, its performance may be dramatically degraded in the presence of impulse noise. While the ZP variants of OFDM exhibit lower bit error rate (BER)and higher energy efficiency compared to their CP counterparts,they demand strict time synchronization, which is challenging in the absence of pilot and CP. Moreover, on the contrary to AWGN, impulse noise severely corrupts data. In this paper, a new low-complexity timing offset (TO) estimator for ZP-OFDM for practical impulsive-noise environments is proposed, where relies on the second-other statistics of the multipath fading channel and noise. Performance comparison with existing TO estimators demonstrates either a superior performance in terms of lock-in probability or a significantly lower complexity over a wide range of signal-to-noise ratio (SNR) for various practical scenarios.

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arXiv2020

Performance Analysis and Optimization of 3D Massive MIMO Multi-Pair Relaying with SWIPT

In this paper, we study a massive multiple-input multiple-output (mMIMO) relay network where multiple source-destination pairs exchange information through a common relay equipped with a massive antenna array. The source users perform simultaneous wireless information and power transfer (SWIPT) and the power-splitting (PS) scheme is used at the relay to first harvest energy from the received signals, and then, to transmit the decoded signals using the harvested energy. Relay performs maximum ratio combining/maximum ratio transmission (MRC/MRT) beam-forming on the received signal. Under the three-dimensional (3D) directional channel model, we derive a closed-form lower bound expression for the average signal-to-interference-plus-noise ratio using results from random matrix theory, which leads to an asymptotic approximation of the achievable sum-rate. Based on that, we study a joint optimization problem over the tilt and PS ratio to maximize the achievable sum-rate. Grid search algorithm is used to solve the non-convex problem. Simulation results verify our theoretical analysis and the efficiency of our optimized design. In particular, our optimized system outperforms a conventional system with $π/4$ tilt and PS of $0.5$, by at least $61\%$.

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arXiv2019

Performance Analysis of Massive MIMO Multi-Way Relay Networks with Low-Resolution ADCs

High power consumption and hardware cost are two barriers for practical massive multiple-input multiple-output (mMIMO) systems. A promising solution is to employ low-resolution analog-to-digital converters (ADCs). In this paper, we consider a general mMIMO multi-way relaying system with a multi-level mixed-ADC architecture, in which each antenna is connected to an ADC pair of an arbitrary resolution. By leveraging on Bussgang's decomposition theorem and Lloyd-Max algorithm for quantization, tight closed-form approximations are derived for the average achievable rates of zero-forcing (ZF) relaying considering both perfect and imperfect channel state information (CSI). To conquer the challenges caused by multi-way relaying, the complicated ZF beam-forming matrix, and the general mixed-ADC structure, we develop a novel method for the achievable rate analysis using the singular-value decomposition (SVD) for Gaussian matrices, distributions of the singular values of Gaussian matrices, and properties of Haar matrices. The results explicitly show the achievable rate behavior in terms of the user and relay transmit powers and the numbers of relay antennas and users. Most importantly, it quantifies the performance degradation caused by low-resolution ADCs and channel estimation error. We demonstrate that the average achievable rate has an almost linear relation with the square of the average of quantization coefficients pertaining to the ADC resolution profile.

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arXiv2019

Decision Directed Channel Estimation Based on Deep Neural Network k-step Predictor for MIMO Communications in 5G

We consider the use of deep neural network (DNN) to develop a decision-directed (DD)-channel estimation (CE) algorithm for multiple-input multiple-output (MIMO)-space-time block coded systems in highly dynamic vehicular environments. We propose the use of DNN for k-step channel prediction for space-time block code (STBC)s, and show that deep learning (DL)-based DD-CE can removes the need for Doppler spread estimation in fast time-varying quasi stationary channels, where the Doppler spread varies from one packet to another. Doppler spread estimation in this kind of vehicular channels is remarkably challenging and requires a large number of pilots and preambles, leading to lower power and spectral efficiency. We train two DNNs which learn real and imaginary parts of the MIMO fading channels over a wide range of Doppler spreads. We demonstrate that by those DNNs, DD-CE can be realized with only rough priori knowledge about Doppler spread range. For the proposed DD-CE algorithm, we also analytically derive the maximum likelihood (ML) decoding algorithm for STBC transmission. The proposed DL-based DD-CE is a promising solution for reliable communication over the vehicular MIMO fading channels without accurate mathematical models. This is because DNN can intelligently learn the statistics of the fading channels. Our simulation results show that the proposed DL-based DD-CE algorithm exhibits lower propagation error compared to existing DD-CE algorithms while the latters require perfect knowledge of the Doppler rate.

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arXiv2018

Partial Zero-Forcing for Multi-Way Relay Networks

The ever increasing demands for mobile network access have resulted in a significant increase in bandwidth usage. By improving the system spectral efficiency, multi-way relay networks (MWRNs) provide promising approaches to address this challenge. In this paper, we propose a novel linear beamforming design, namely partial zero-forcing (PZF), for MWRNs with a multiple-input-multiple-output (MIMO) relay. Compared to zero-forcing (ZF), PZF relaxes the constraints on the relay beamforming matrix such that only partial user-interference, instead of all, is canceled at the relay. The users eliminate the remaining interferences through self-interference and successive interference cancellation. A sum-rate maximization problem is formulated and solved to exploit the extra degrees-of-freedom resulted from PZF. Simulation results show that the proposed PZF relay beamforming design achieves significantly higher network sum-rates than the existing linear beamforming designs.

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arXiv2017

Fast Encoding and Decoding of Flexible-Rate and Flexible-Length Polar Codes

This work is on fast encoding and decoding of polar codes. We propose and detail 8-bit and 16-bit parallel decoders that can be used to reduce the decoding latency of the successive-cancellation decoder. These decoders are universal and can decode flexible-rate and flexible-length polar codes. We also present fast encoders that can be used to increase the throughput of serially-implemented polar encoders.

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arXiv2017

Storage Allocation for Multi-Class Distributed Data Storage Systems

Distributed storage systems (DSSs) provide a scalable solution for reliably storing massive amounts of data coming from various sources. Heterogeneity of these data sources often means different data classes (types) exist in a DSS, each needing a different level of quality of service (QoS). As a result, efficient data storage and retrieval processes that satisfy various QoS requirements are needed. This paper studies storage allocation, meaning how data of different classes must be spread over the set of storage nodes of a DSS. More specifically, assuming a probabilistic access to the storage nodes, we aim at maximizing the weighted sum of the probability of successful data recovery of data classes, when for each class a minimum QoS (probability of successful recovery) is guaranteed. Solving this optimization problem for a general setup is intractable. Thus, we find the optimal storage allocation when the data of each class is spread minimally over the storage nodes, i.e. minimal spreading allocation (MSA). Using upper bounds on the performance of the optimal storage allocation, we show that the optimal MSA allocation approaches the optimal performance in many practical cases. Computer simulations are also presented to better illustrate the results.

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arXiv2017

On the Average Locality of Locally Repairable Codes

A linear block code with dimension $k$, length $n$, and minimum distance $d$ is called a locally repairable code (LRC) with locality $r$ if it can retrieve any coded symbol by at most $r$ other coded symbols. LRCs have been recently proposed and used in practice in distributed storage systems (DSSs) such as Windows Azure storage and Facebook HDFS-RAID. Theoretical bounds on the maximum locality of LRCs ($r$) have been established. The \textit{average} locality of an LRC ($\overline{r}$) directly affects the costly repair bandwidth, disk I/O, and number of nodes involved in the repair process of a missing data block. There is a gap in the literature studying $\overline{r}$. In this paper, we establish a lower bound on $\overline{r}$ of arbitrary $(n,k,d)$ LRCs. Furthermore, we obtain a tight lower bound on $\overline{r}$ for a practical case where the code rate $(R=\frac{k}{n})$ is greater than $(1-\frac{1}{\sqrt{n}})^2$. Finally, we design three classes of LRCs that achieve the obtained bounds on $\overline{r}$. Comparing with the existing LRCs, our proposed codes improve the average locality without sacrificing such crucial parameters as the code rate or minimum distance.

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arXiv2016

Maximizing Data Rate for Multiway Relay Channels with Pairwise Transmission Strategy

In a multiway relay channel (MWRC), pairwise transmission strategy can be used to reduce the computational complexity at the relay and the users without sacrificing the data rate, significantly. The performance of such pairwise strategies, however, is affected by the way that the users are paired to transmit. In this paper, we study the effect of pairing on the common rate and sum rate of an MWRC with functional-decode-forward (FDF) relaying strategy where users experience asymmetric channel conditions. To this end, we first develop a graphical model for an MWRC with pairwise transmission strategy. Using this model, we then find the maximum achievable common rate and sum rate as well as the user pairings that achieve these rates. This marks the ultimate performance of FDF relaying in an MWRC setup. Further, we show that the rate enhancement achieved through the optimal user pairing becomes less pronounced at higher SNRs. Using computer simulations, the performance of the optimal pairing is compared with those of other proposed pairings in the literature.

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