arXiv subjects

Moslem Noori

Publications and source records attributed to Moslem Noori.

16 recordsLinked to original sources

arXiv2026

Performance of QUBO-Formulated MIMO Detection Under Hardware Precision Constraints

The evolution of multiple-input, multiple-output (MIMO) systems requires the efficient detection algorithms to overcome the exponential computational complexity of optimal maximum likelihood detection. Reformulating MIMO detection as a quadratic unconstrained binary optimization (QUBO) problem enables the use of highly parallel, physics-inspired, hardware-accelerated solvers and non-von Neumann architectures. However, embedding continuous-valued QUBO coefficients into hardware introduces quantization noise due to finite precision, which can severely degrade detection accuracy. This paper presents a rigorous analysis of the performance impact of finite-precision, hardware-accelerated QUBO solvers in MIMO detection. We analytically derive the probability distribution functions of the QUBO matrix entries and introduce novel homogeneous and heterogeneous quantization schemes based on either instantaneous channel state information or its statistical features. We further derive a sufficient condition on the precision required to maintain the optimal solution after quantization. Extensive numerical experiments, across various MIMO system sizes and modulation orders (up to 256-QAM), show that heterogeneous quantization matches the full-precision baseline bit error rate using significantly fewer bits than homogeneous approaches. We provide hardware-aware guidelines for selecting the optimal quantization strategy.

cs.IT↗

arXiv2025

Accelerating Hybrid XOR$-$CNF Boolean Satisfiability Problems Natively with In-Memory Computing

The Boolean satisfiability (SAT) problem is a computationally challenging decision problem central to many industrial applications. For SAT problems in cryptanalysis, circuit design, and telecommunication, solutions can often be found more efficiently by representing them with a combination of exclusive OR (XOR) and conjunctive normal form (CNF) clauses. We propose a hardware accelerator architecture that natively embeds and solves such hybrid XOR--CNF problems using in-memory computing hardware. To achieve this, we introduce an algorithm and demonstrate, both experimentally and through simulations, how it can be efficiently implemented with memristor crossbar arrays. Compared to the conventional approaches that translate XOR--CNF problems to pure CNF problems, our simulations show that the accelerator improves computation speed, energy efficiency, and chip area utilization of in-memory accelerators by $\sim$10$\times$ for a set of hard cryptographic benchmarking problems. Moreover, the accelerator achieves a $\sim$10$\times$ speedup and a $\sim$1000$\times$ gain in energy efficiency over state-of-the-art SAT solvers running on CPUs.

cs.ET↗

arXiv2025

A Statistical Analysis for Per-Instance Evaluation of Stochastic Optimizers: Avoiding Unreliable Conclusions

A key trait of stochastic optimizers is that multiple runs of the same optimizer in attempting to solve the same problem can produce different results. As a result, their performance is evaluated over several repeats, or runs, on the problem. However, the accuracy of the estimated performance metrics depends on the number of runs and should be studied using statistical tools. We present a statistical analysis of the common metrics, and develop guidelines for experiment design to measure the optimizer's performance using these metrics to a high level of confidence and accuracy. To this end, we first discuss the confidence interval of the metrics and how they are related to the number of runs of an experiment. We then derive a lower bound on the number of repeats in order to guarantee achieving a given accuracy in the metrics. Using this bound, we propose an algorithm to adaptively adjust the number of repeats needed to ensure the accuracy of the evaluated metric. Our simulation results demonstrate the utility of our analysis and how it allows us to conduct reliable benchmarking as well as hyperparameter tuning and prevent us from drawing premature conclusions regarding the performance of stochastic optimizers.

cs.LG↗

arXiv2025

Hardware-Compatible Single-Shot Feasible-Space Heuristics for Solving the Quadratic Assignment Problem

Research into the development of special-purpose computing architectures designed to solve quadratic unconstrained binary optimization (QUBO) problems has flourished in recent years. It has been demonstrated in the literature that such special-purpose solvers can outperform traditional complementary metal--oxide--semiconductor architectures by orders of magnitude with respect to timing metrics on synthetic problems. However, they face challenges with constrained problems such as the quadratic assignment problem (QAP), where mapping to binary formulations such as QUBO introduces overhead and limits parallelism. In-memory computing (IMC) devices, such as memristor-based analog Ising machines, offer significant speed-ups and efficiency gains over traditional CPU-based solvers, particularly for solving combinatorial optimization problems. In this work, we present a novel hardware-aware QAP optimization framework designed for IMC hardware. By co-designing the local search heuristic with the underlying hardware, we exploit the intrinsic massive parallelism that allows for computing of full neighbourhoods simultaneously to make update decisions. We ensure binary solutions remain feasible by selecting local moves that lead to neighbouring feasible solutions, leveraging feasible-space search heuristics and the underlying structure of a given problem. Our approach is compatible with both digital computers and analog hardware. We demonstrate its effectiveness in CPU implementations by comparing it with state-of-the-art heuristics for solving the QAP.

math.OC↗

arXiv2024

Parallel Tempering-Inspired Distributed Binary Optimization with In-Memory Computing

In-memory computing (IMC) has been shown to be a promising approach for solving binary optimization problems while significantly reducing energy and latency. Building on the advantages of parallel computation, we propose an IMC-compatible parallelism framework based on the physics-inspired parallel tempering (PT) algorithm, enabling cross-replica communication to improve the performance of IMC solvers. This framework enables an IMC solver not only to improve performance beyond what can be achieved through parallelization, but also affords greater flexibility for the search process with low hardware overhead. We justify that the framework can be applied to almost any IMC solver. We demonstrate the effectiveness of the framework for the Boolean satisfiability (SAT) problem, using the WalkSAT heuristic as a proxy for existing IMC solvers. The resulting PT-inspired cooperative WalkSAT (PTIC-WalkSAT) algorithm outperforms the standard WalkSAT heuristic in terms of the iterations-to-solution in 84.0% of the tested problem instances and its na\"ive parallel variant (PA-WalkSAT) does so in 64.9% of the instances, and with a higher success rate in the majority of instances. An estimate of the energy overhead of the PTIC framework for two hardware accelerator architectures indicates that in both cases the overhead of running the PTIC framework would be less than 1% of the total energy required to run each accelerator.

cs.ET↗

arXiv2021

Distributed Storage Allocations for Optimal Service Rates

Redundant storage maintains the performance of distributed systems under various forms of uncertainty. This paper considers the uncertainty in node access and download service. We consider two access models under two download service models. In one access model, a user can access each node with a fixed probability, and in the other, a user can access a random fixed-size subset of nodes. We consider two download service models. In the first (small file) model, the randomness associated with the file size is negligible. In the second (large file) model, randomness is associated with both the file size and the system's operations. We focus on the service rate of the system. For a fixed redundancy level, the systems' service rate is determined by the allocation of coded chunks over the storage nodes. We consider quasi-uniform allocations, where coded content is uniformly spread among a subset of nodes. The question we address asks what the size of this subset (spreading) should be. We show that in the small file model, concentrating the coded content to a minimum-size subset is universally optimal. For the large file model, the optimal spreading depends on the system parameters. These conclusions hold for both access models.

cs.IT↗

arXiv2020

Quantum Multiple Kernel Learning

Kernel methods play an important role in machine learning applications due to their conceptual simplicity and superior performance on numerous machine learning tasks. Expressivity of a machine learning model, referring to the ability of the model to approximate complex functions, has a significant influence on its performance in these tasks. One approach to enhancing the expressivity of kernel machines is to combine multiple individual kernels to arrive at a more expressive combined kernel. This approach is referred to as multiple kernel learning (MKL). In this work, we propose an MKL method we refer to as quantum MKL, which combines multiple quantum kernels. Our method leverages the power of deterministic quantum computing with one qubit (DQC1) to estimate the combined kernel for a set of classically intractable individual quantum kernels. The combined kernel estimation is achieved without explicitly computing each individual kernel, while still allowing for the tuning of individual kernels in order to achieve better expressivity. Our simulations on two binary classification problems---one performed on a synthetic dataset and the other on a German credit dataset---demonstrate the superiority of the quantum MKL method over single quantum kernel machines.

quant-ph↗

arXiv2019

Adiabatic Quantum Kitchen Sinks for Learning Kernels Using Randomized Features

Quantum information processing is likely to have far-reaching impact in the field of artificial intelligence. While the race to build an error-corrected quantum computer is ongoing, noisy, intermediate-scale quantum (NISQ) devices provide an immediate platform for exploring a possible quantum advantage through hybrid quantum--classical machine learning algorithms. One example of such a hybrid algorithm is "quantum kitchen sinks", which builds upon the classical algorithm known as "random kitchen sinks" to leverage a gate model quantum computer for machine learning applications. We propose an alternative algorithm called "adiabatic quantum kitchen sinks", which employs an adiabatic quantum device to transform data features into new features in a non-linear manner, which can then be employed by classical machine learning algorithms. We present the effectiveness of our algorithm for performing binary classification on both a synthetic dataset and a real-world dataset. In terms of classification accuracy, our algorithm significantly enhances the performance of a classical linear classifier on the studied binary classification tasks and can potentially be implemented on a current adiabatic quantum device to solve practical problems.

quant-ph↗

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.

eess.SP↗

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.

cs.IT↗

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.

cs.IT↗

arXiv2016

On the Capacity Region of ALOHA Protocol for the Internet of Things

Accommodating the needs of a large number of diverse users in the Internet of Things (IoT), notably managing how the users access the common channel, has posed unique challenges to the network designers. In this paper, we study a heterogeneous IoT network consisting of multiple classes of users who may have different service requirements. For this network, we consider the application of irregular repetition slotted ALOHA (IRSA) that is shown to offer large throughput for single-class networks. Then, we focus on finding the network performance boundaries by studying the set of feasible throughput values for each class, called the capacity region. To this end, we first introduce the concept of dual network of a multi-class network meaning a homogeneous network with the same number of users. We then prove that finding the capacity region of the assumed multi-class network boils down to finding the maximum achievable throughput of its dual network. Using this finding, we then discuss how any given point of the capacity region can be achieved. Further, a delay performance study is conducted to evaluate the average and maximum packet transmission delay experienced by the users of each class.

cs.NI↗

arXiv2016

On Storage Allocation for Maximum Service Rate in Distributed Storage Systems

Storage allocation affects important performance measures of distributed storage systems. Most previous studies on the storage allocation consider its effect separately either on the success of the data recovery or on the service rate (time) where it is assumed that no access failure happens in the system. In this paper, we go one step further and incorporate the access model and the success of data recovery into the service rate analysis. In particular, we focus on quasi-uniform storage allocation and provide a service rate analysis for both fixed-size and probabilistic access models at the nodes. Using this analysis, we then show that for the case of exponential waiting time distribution at individuals storage nodes, minimal spreading allocation results in the highest system service rate for both access models. This means that for a given storage budget, replication provides a better service rate than a coded storage solution.

cs.DC↗

arXiv2014

On the Achievable Rates of Pairwise Multiway Relay Channels

In this paper, we study the effect of users' transmission ordering on the common rate and sum rate of pairwise multiway relay channels (MWRCs) with functional-decode-forward strategy. To this end, we first develop a graphical model for the data transmission in a pairwise MWRC. Using this model, we then find the optimal orderings that achieve the maximum common rate and sum rate of the system. The achieved maximum common and sum rate are also found. Moreover, we show that the performance gap between optimal orderings and a random ordering vanishes when SNR increases. Computer simulations are presented for better illustration of the results.

cs.IT↗

arXiv2013

Low-Latency Data Sharing in Erasure Multi-Way Relay Channels

We consider an erasure multi-way relay channel (EMWRC) in which several users share their data through a relay over erasure links. Assuming no feedback channel between the users and the relay, we first identify the challenges for designing a data sharing scheme over an EMWRC. Then, to overcome these challenges, we propose practical low-latency and low-complexity data sharing schemes based on fountain coding. Later, we introduce the notion of end-to-end erasure rate (EEER) and analytically derive it for the proposed schemes. EEER is then used to calculate the achievable rate and transmission overhead of the proposed schemes. Using EEER and computer simulations, the achievable rates and transmission overhead of our proposed schemes are compared with the ones of one-way relaying. This comparison implies that when the number of users and the channel erasure rates are not large, our proposed schemes outperform one-way relaying. We also find an upper bound on the achievable rates of EMWRC and observe that depending on the number of users and channel erasure rates, our proposed solutions can perform very close to this bound.

cs.IT↗

arXiv2007

A Probability Model for Lifetime of Wireless Sensor Networks

Considering a wireless sensor network whose nodes are distributed randomly over a given area, a probability model for the network lifetime is provided. Using this model and assuming that packet generation follows a Poisson distribution, an analytical expression for the complementary cumulative density function (ccdf) of the lifetime is obtained. Using this ccdf, one can accurately find the probability that the network achieves a given lifetime. It is also shown that when the number of sensors, $N$, is large, with an error exponentially decaying with $N$, one can predict whether or not a certain lifetime can be achieved. The results of this work are obtained for both multi-hop and single-hop wireless sensor networks and are verified with computer simulation. The approaches of this paper are shown to be applicable to other packet generation models and the effect of the area shape is also investigated.

cs.NI↗