arXiv subjects

Mohamad Assaad

Publications and source records attributed to Mohamad Assaad.

At least 37 records · Page 2Linked to original sources

arXiv2024

Goal-Oriented Communication for Networked Control Assisted by Reconfigurable Meta-Surfaces

In this paper, we develop a theoretical framework for goal-oriented communication assisted by reconfigurable meta-surfaces in the context of networked control systems. The relation to goal-oriented communication stems from the fact that optimization of the phase shifts of the meta-surfaces is guided by the performance of networked control systems tasks. To that end, we consider a networked control system in which a set of sensors observe the states of a set of physical processes, and communicate this information over an unreliable wireless channel assisted by a reconfigurable intelligent surface with multiple reflecting elements to a set of controllers that correct the behaviors of the physical processes based on the received information. Our objective is to find the optimal control policy for the controllers and the optimal phase policy for the reconfigurable intelligent surface that jointly minimize a regulation cost function associated with the networked control system. We characterize these policies, and also propose an approximate solution based on a semi-definite relaxation technique.

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arXiv2024

Massive MIMO CSI Feedback using Channel Prediction: How to Avoid Machine Learning at UE?

In the literature, machine learning (ML) has been implemented at the base station (BS) and user equipment (UE) to improve the precision of downlink channel state information (CSI). However, ML implementation at the UE can be infeasible for various reasons, such as UE power consumption. Motivated by this issue, we propose a CSI learning mechanism at BS, called CSILaBS, to avoid ML at UE. To this end, by exploiting channel predictor (CP) at BS, a light-weight predictor function (PF) is considered for feedback evaluation at the UE. CSILaBS reduces over-the-air feedback overhead, improves CSI quality, and lowers the computation cost of UE. Besides, in a multiuser environment, we propose various mechanisms to select the feedback by exploiting PF while aiming to improve CSI accuracy. We also address various ML-based CPs, such as NeuralProphet (NP), an ML-inspired statistical algorithm. Furthermore, inspired to use a statistical model and ML together, we propose a novel hybrid framework composed of a recurrent neural network and NP, which yields better prediction accuracy than individual models. The performance of CSILaBS is evaluated through an empirical dataset recorded at Nokia Bell-Labs. The outcomes show that ML elimination at UE can retain performance gains, for example, precoding quality.

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arXiv2024

Optimal Denial-of-Service Attacks Against Status Updating

In this paper, we investigate denial-of-service attacks against status updating. The target system is modeled by a Markov chain and an unreliable wireless channel, and the performance of status updating in the target system is measured based on two metrics: age of information and age of incorrect information. Our objective is to devise optimal attack policies that strike a balance between the deterioration of the system's performance and the adversary's energy. We model the optimal problem as a Markov decision process and prove rigorously that the optimal jamming policy is a threshold-based policy under both metrics. In addition, we provide a low-complexity algorithm to obtain the optimal threshold value of the jamming policy. Our numerical results show that the networked system with the age-of-incorrect-information metric is less sensitive to jamming attacks than with the age-of-information metric. Index Terms-age of incorrect information, age of information, cyber-physical systems, status updating, remote monitoring.

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arXiv2023

On the Age of Information of Processor Sharing Systems

In this paper, we examine the Age of Information (AoI) of a source sending status updates to a monitor through a queue operating under the Processor Sharing (PS) discipline. In the PS queueing discipline, all the updates are served simultaneously and, therefore, none of of the jobs wait in the queue to get service. While AoI has been well studied for various queuing models and policies, less attention has been given so far to the PS discipline. We first consider the M/M/1/2 queue with and without preemption and provide closed-form expressions for the average AoI in this case. We overcome the challenges of deriving the AoI expression by employing the Stochastic Hybrid Systems (SHS) tool. We then extend the analysis to the M/M/1 queue with one and two sources and provide numerical results for these cases. Our results show that PS can outperform the M/M/1/1* queue in some cases.

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arXiv2023

When to pull data from sensors for minimum Distance-based Age of incorrect Information metric

The age of Information (AoI) has been introduced to capture the notion of freshness in real-time monitoring applications. However, this metric falls short in many scenarios, especially when quantifying the mismatch between the current and the estimated states. To circumvent this issue, in this paper, we adopt the age of incorrect information metric (AoII) that considers the quantified mismatch between the source and the knowledge at the destination while tracking the impact of freshness. We consider for that a problem where a central entity pulls the information from remote sources that evolve according to a Markovian Process. It selects at each time slot which sources should send their updates. As the scheduler does not know the actual state of the remote sources, it estimates at each time the value of AoII based on the Markovian sources' parameters. Its goal is to keep the time average of the AoII function as small as possible. For that purpose, We develop a scheduling scheme based on Whittle's index policy. To that extent, we use the Lagrangian Relaxation Approach and establish that the dual problem has an optimal threshold policy. Building on that, we compute the expressions of Whittle's indices. Finally, we provide some numerical results to highlight the performance of our derived policy compared to the classical AoI metric.

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arXiv2023

Initial Access Optimization for RIS-assisted Millimeter Wave Wireless Networks

Reconfigurable Intelligent Surfaces (RIS) are considered a key enabler to achieve the vision of Smart Radio Environments, where the propagation environment can be programmed and controlled to enhance the efficiency of wireless systems. These surfaces correspond to planar sheets comprising a large number of small and low-cost reflecting elements whose parameters are adaptively selected with a programmable controller. Hence, by optimizing these coefficients, the information signals can be directed in a customized fashion. On the other hand, the initial access procedure used in 5G is beam sweeping, where the base station sequentially changes the active beam direction in order to scan all users in the cell. This conventional protocol results in an initial access latency. The aim of this paper is to minimize this delay by optimizing the activated beams in each timeslot, while leveraging the presence of the RIS in the network. The problem is formulated as a hard optimization problem. We propose an efficient solution based on jointly alternating optimization and Semi Definite Relaxation (SDR) techniques. Numerical results are provided to assess the superiority of our scheme as compared to conventional beam sweeping.

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arXiv2022

Minimizing the Age of Incorrect Information for Unknown Markovian Source

The age of information minimization problems has been extensively studied in Real-time monitoring applications frameworks. In this paper, we consider the problem of monitoring the states of unknown remote source that evolves according to a Markovian Process. A central scheduler decides at each time slot whether to schedule the source or not in order to receive the new status updates in such a way as to minimize the Mean Age of Incorrect Information (MAoII). When the scheduler knows the source parameters, we formulate the minimization problem as an MDP problem. Then, we prove that the optimal solution is a threshold-based policy. When the source's parameters are unknown, the problem's difficulty lies in finding a strategy with a good trade-off between exploitation and exploration. Indeed, we need to provide an algorithm implemented by the scheduler that jointly estimates the unknown parameters (exploration) and minimizes the MAoII (exploitation). However, considering our system model, we can only explore the source if the monitor decides to schedule it. Then, applying the greedy approach, we risk definitively stopping the exploration process in the case where at a specific time, we end up with an estimation of the Markovian source's parameters to which the corresponding optimal solution is never to transmit. In this case, we can no longer improve the estimation of our unknown parameters, which may significantly detract from the performance of the algorithm. For that, we develop a new learning algorithm that gives a good balance between exploration and exploitation to avoid this main problem. Then, we theoretically analyze the performance of our algorithm compared to a genie solution by proving that the regret bound at time T is log(T). Finally, we provide some numerical results to highlight the performance of our derived policy compared to the greedy approach.

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arXiv2022

The Age of Incorrect Information: an Enabler of Semantics-Empowered Communication

In this paper, we introduce the Age of Incorrect Information (AoII) as an enabler for semantics-empowered communication, a newly advocated communication paradigm centered around data's role and its usefulness to the communication's goal. First, we shed light on how the traditional communication paradigm, with its role-blind approach to data, is vulnerable to performance bottlenecks. Next, we highlight the shortcomings of several proposed performance measures destined to deal with the traditional communication paradigm's limitations, namely the Age of Information (AoI) and the error-based metrics. We also show how the AoII addresses these shortcomings and captures more meaningfully the purpose of data. Afterward, we consider the problem of minimizing the average AoII in a transmitter-receiver pair scenario. We prove that the optimal transmission strategy is a randomized threshold policy, and we propose an algorithm that finds the optimal parameters. Furthermore, we provide a theoretical comparison between the AoII framework and the standard error-based metrics counterpart. Interestingly, we show that the AoII-optimal policy is also error-optimal for the adopted information source model. Concurrently, the converse is not necessarily true. Finally, we implement our policy in various applications, and we showcase its performance advantages compared to both the error-optimal and the AoI-optimal policies.

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arXiv2022

Zero-Order One-Point Estimate with Distributed Stochastic Gradient-Tracking Technique

In this work, we consider a distributed multi-agent stochastic optimization problem, where each agent holds a local objective function that is smooth and convex, and that is subject to a stochastic process. The goal is for all agents to collaborate to find a common solution that optimizes the sum of these local functions. With the practical assumption that agents can only obtain noisy numerical function queries at exactly one point at a time, we extend the distributed stochastic gradient-tracking method to the bandit setting where we don't have an estimate of the gradient, and we introduce a zero-order (ZO) one-point estimate (1P-DSGT). We analyze the convergence of this novel technique for smooth and convex objectives using stochastic approximation tools, and we prove that it converges almost surely to the optimum. We then study the convergence rate for when the objectives are additionally strongly convex. We obtain a rate of $O(\frac{1}{\sqrt{k}})$ after a sufficient number of iterations $k > K_2$ which is usually optimal for techniques utilizing one-point estimators. We also provide a regret bound of $O(\sqrt{k})$, which is exceptionally good compared to the aforementioned techniques. We further illustrate the usefulness of the proposed technique using numerical experiments.

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arXiv2022

Design of an Efficient CSI Feedback Mechanism in Massive MIMO Systems: A Machine Learning Approach using Empirical Data

Massive multiple-input multiple-output (mMIMO) regime reaps the benefits of spatial diversity and multiplexing gains, subject to precise channel state information (CSI) acquisition. In the current communication architecture, the downlink CSI is estimated by the user equipment (UE) via dedicated pilots and then fed back to the gNodeB (gNB). The feedback information is compressed with the goal of reducing over-the-air overhead. This compression increases the inaccuracy of acquired CSI, thus degrading the overall spectral efficiency. This paper proposes a computationally inexpensive machine learning (ML)-based CSI feedback algorithm, which exploits twin channel predictors. The proposed approach can work for both time-division duplex (TDD) and frequency-division duplex (FDD) systems, and it allows to reduce feedback overhead and improves the acquired CSI accuracy. To observe real benefits, we demonstrate the performance of the proposed approach using the empirical data recorded at the Nokia campus in Stuttgart, Germany. Numerical results show the effectiveness of the proposed approach in terms of reducing overhead, minimizing quantization errors, increasing spectral efficiency, cosine similarity, and precoding gain compared to the traditional CSI feedback mechanism.

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arXiv2022

Real-Time Massive MIMO Channel Prediction: A Combination of Deep Learning and NeuralProphet

Channel state information (CSI) is of pivotal importance as it enables wireless systems to adapt transmission parameters more accurately, thus improving the system's overall performance. However, it becomes challenging to acquire accurate CSI in a highly dynamic environment, mainly due to multi-path fading. Inaccurate CSI can deteriorate the performance, particularly of a massive multiple-input multiple-output (mMIMO) system. This paper adapts machine learning (ML) for CSI prediction. Specifically, we exploit time-series models of deep learning (DL) such as recurrent neural network (RNN) and Bidirectional long-short term memory (BiLSTM). Further, we use NeuralProphet (NP), a recently introduced time-series model, composed of statistical components, e.g., auto-regression (AR) and Fourier terms, for CSI prediction. Inspired by statistical models, we also develop a novel hybrid framework comprising RNN and NP to achieve better prediction accuracy. The proposed channel predictors (CPs) performance is evaluated on a real-time dataset recorded at the Nokia Bell-Labs campus in Stuttgart, Germany. Numerical results show that DL brings performance gain when used with statistical models and showcases robustness.

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arXiv2022

Age-Aware Stochastic Hybrid Systems: Stability, Solutions, and Applications

In this paper, we analyze status update systems modeled through the Stochastic Hybrid Systems (SHSs) tool. Contrary to previous works, we allow the system's transition dynamics to be polynomial functions of the Age of Information (AoI). This dependence allows us to encapsulate many applications and opens the door for more sophisticated systems to be studied. However, this same dependence on the AoI engenders technical and analytical difficulties that we address in this paper. Specifically, we first showcase several characteristics of the age processes modeled through the SHSs tool. Then, we provide a framework to establish the Lagrange stability and positive recurrence of these processes. Building on this, we provide an approach to compute the m-th moment of the age processes. Interestingly, this technique allows us to approximate the average age by solving a simple set of linear equations. Equipped with this approach, we also provide a sequential convex approximation method to optimize the average age by calibrating the parameters of the system. Finally, we consider an age-dependent CSMA environment where the backoff duration depends on the instantaneous age. By leveraging our analysis, we contrast its performance to the age-blind CSMA and showcase the age performance gain provided by the former.

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arXiv2022

Artificial Intelligence for 6G Networks: Technology Advancement and Standardization

With the deployment of 5G networks, standards organizations have started working on the design phase for sixth-generation (6G) networks. 6G networks will be immensely complex, requiring more deployment time, cost and management efforts. On the other hand, mobile network operators demand these networks to be intelligent, self-organizing, and cost-effective to reduce operating expenses (OPEX). Machine learning (ML), a branch of artificial intelligence (AI), is the answer to many of these challenges providing pragmatic solutions, which can entirely change the future of wireless network technologies. By using some case study examples, we briefly examine the most compelling problems, particularly at the physical (PHY) and link layers in cellular networks where ML can bring significant gains. We also review standardization activities in relation to the use of ML in wireless networks and future timeline on readiness of standardization bodies to adapt to these changes. Finally, we highlight major issues in ML use in the wireless technology, and provide potential directions to mitigate some of them in 6G wireless networks.

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arXiv2022

Semantic Communications in Networked Systems: A Data Significance Perspective

We present our vision for a departure from the established way of architecting and assessing communication networks, by incorporating the semantics of information for communications and control in networked systems. We define semantics of information, not as the meaning of the messages, but as their significance, possibly within a real time constraint, relative to the purpose of the data exchange. We argue that research efforts must focus on laying the theoretical foundations of a redesign of the entire process of information generation, transmission and usage in unison by developing: advanced semantic metrics for communications and control systems; an optimal sampling theory combining signal sparsity and semantics, for real-time prediction, reconstruction and control under communication constraints and delays; semantic compressed sensing techniques for decision making and inference directly in the compressed domain; semantic-aware data generation, channel coding, feedback, multiple and random access schemes that reduce the volume of data and the energy consumption, increasing the number of supportable devices.

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arXiv2021

Distributed Zeroth-Order Stochastic Optimization in Time-varying Networks

We consider a distributed convex optimization problem in a network which is time-varying and not always strongly connected. The local cost function of each node is affected by some stochastic process. All nodes of the network collaborate to minimize the average of their local cost functions. The major challenge of our work is that the gradient of cost functions is supposed to be unavailable and has to be estimated only based on the numerical observation of cost functions. Such problem is known as zeroth-order stochastic convex optimization (ZOSCO). In this paper we take a first step towards the distributed optimization problem with a ZOSCO setting. The proposed algorithm contains two basic steps at each iteration: i) each unit updates a local variable according to a random perturbation based single point gradient estimator of its own local cost function; ii) each unit exchange its local variable with its direct neighbors and then perform a weighted average. In the situation where the cost function is smooth and strongly convex, our attainable optimization error is $O(T^{-1/2})$ after $T$ iterations. This result is interesting as $O(T^{-1/2})$ is the optimal convergence rate in the ZOSCO problem. We have also investigate the optimization error with the general Lipschitz convex function, the result is $O(T^{-1/4})$.

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arXiv2021

Distributed Derivative-free Learning Method for Stochastic Optimization over a Network with Sparse Activity

This paper addresses a distributed optimization problem in a communication network where nodes are active sporadically. Each active node applies some learning method to control its action to maximize the global utility function, which is defined as the sum of the local utility functions of active nodes. We deal with stochastic optimization problem with the setting that utility functions are disturbed by some non-additive stochastic process. We consider a more challenging situation where the learning method has to be performed only based on a scalar approximation of the utility function, rather than its closed-form expression, so that the typical gradient descent method cannot be applied. This setting is quite realistic when the network is affected by some stochastic and time-varying process, and that each node cannot have the full knowledge of the network states. We propose a distributed optimization algorithm and prove its almost surely convergence to the optimum. Convergence rate is also derived with an additional assumption that the objective function is strongly concave. Numerical results are also presented to justify our claim.

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arXiv2021

Dealing with CSI Compression to Reduce Losses and Overhead: An Artificial Intelligence Approach

Motivated by the issue of inaccurate channel state information (CSI) at the base station (BS), which is commonly due to feedback/processing delays and compression problems, in this paper, we introduce a scalable idea of adopting artificial intelligence (AI) aided CSI acquisition. The proposed scheme enhances the CSI compression, which is done at the mobile terminal (MT), along with accurate recovery of estimated CSI at the BS. Simulation-based results corroborate the validity of the proposed scheme. Numerically, nearly 100\% recovery of the estimated CSI is observed with relatively lower overhead than the benchmark scheme. The proposed idea can bring potential benefits in the wireless communication environment, e.g., ultra-reliable and low latency communication (URLLC), where imperfect CSI and overhead is intolerable.

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arXiv2021

A Novel Algorithm to Report CSI in MIMO-Based Wireless Networks

In wireless communication, accurate channel state information (CSI) is of pivotal importance. In practice, due to processing and feedback delays, estimated CSI can be outdated, which can severely deteriorate the performance of the communication system. Besides, to feedback estimated CSI, a strong compression of the CSI, evaluated at the user equipment (UE), is performed to reduce the over-the-air (OTA) overhead. Such compression strongly reduces the precision of the estimated CSI, which ultimately impacts the performance of multiple-input multiple-output (MIMO) precoding. Motivated by such issues, we present a novel scalable idea of reporting CSI in wireless networks, which is applicable to both time-division duplex (TDD) and frequency-division duplex (FDD) systems. In particular, the novel approach introduces the use of a channel predictor function, e.g., Kalman filter (KF), at both ends of the communication system to predict CSI. Simulation-based results demonstrate that the novel approach reduces not only the channel mean-squared-error (MSE) but also the OTA overhead to feedback the estimated CSI when there is immense variation in the mobile radio channel. Besides, in the immobile radio channel, feedback can be eliminated, which brings the benefit of further reducing the OTA overhead. Additionally, the proposed method provides a significant signal-to-noise ratio (SNR) gain in both the channel conditions, i.e., highly mobile and immobile.

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