arXiv subjects

Petar Popovski

Publications and source records attributed to Petar Popovski.

At least 163 records · Page 9Linked to original sources

arXiv2023

Uplink Multiplexing of eMBB/URLLC Services Assisted by Reconfigurable Intelligent Surfaces

This letter proposes a scheme assisted by a reconfigurable intelligent surface (RIS) for efficient uplink traffic multiplexing between enhanced mobile broadband (eMBB) and ultra-reliable-low-latency communication (URLLC). The scheme determines two RIS configurations based only on the eMBB channel state information (CSI) available at the base station (BS). The first optimizes eMBB quality of service, while the second reduces eMBB interference in URLLC traffic by temporarily silencing the eMBB traffic. Numerical results demonstrate that this approach, relying solely on eMBB CSI and without BS coordination, can outperform the state-of-the-art preemptive puncturing by 4.9 times in terms of URLLC outage probability.

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arXiv2023

On the Radio Stripe Deployment for Indoor RF Wireless Power Transfer

One of the primary goals of future wireless systems is to foster sustainability, for which, radio frequency (RF) wireless power transfer (WPT) is considered a key technology enabler. The key challenge of RF-WPT systems is the extremely low end-to-end efficiency, mainly due to the losses introduced by the wireless channel. Distributed antenna systems are undoubtedly appealing as they can significantly shorten the charging distances, thus, reducing channel losses. Interestingly, radio stripe systems provide a cost-efficient and scalable way to deploy a distributed multi-antenna system, and thus have received a lot of attention recently. Herein, we consider an RF-WPT system with a transmit radio stripe network to charge multiple indoor energy hotspots, i.e., spatial regions where the energy harvesting devices are expected to be located, including near-field locations. We formulate the optimal radio stripe deployment problem aimed to maximize the minimum power received by the users and explore two specific predefined shapes, namely the straight line and polygon-shaped configurations. Then, we provide efficient solutions relying on geometric programming to optimize the location of the radio stripe elements. The results demonstrate that the proposed radio stripe deployments outperform a central fully-digital square array with the same number of elements and utilizing larger radio stripe lengths can enhance the performance, while increasing the system frequency may degrade it.

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arXiv2023

Unsourced Multiple Access with Common Alarm Messages: Network Slicing for Massive and Critical IoT

We investigate the coexistence of massive and critical Internet of Things (IoT) services in the context of the unsourced multiple access (UMA) framework introduced by Polyanskiy (2017), where all users employ a common codebook and the receiver returns an unordered list of decoded codewords. This setup is suitably modified to introduce heterogeneous traffic. Specifically, to model the massive IoT service, a standard message originates independently from each IoT device as in the standard UMA setup. To model the critical IoT service, we assume the generation of alarm messages that are common for all devices. This setup requires a significant redefinition of the error events, i.e., misdetections and false positives. We further assume that the number of active users in each transmission attempt is random and unknown. We derive a random-coding achievability bound on the misdetection and false positive probabilities of both standard and alarm messages on the Gaussian multiple access channel. Using our bound, we demonstrate that orthogonal network slicing enables massive and critical IoT to coexist under the requirement of high energy efficiency. On the contrary, we show that nonorthogonal network slicing is energy inefficient due to the residual interference from the alarm signal when decoding the standard messages.

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arXiv2023

Multi-Sensor Multi-Scan Radar Sensing of Multiple Extended Targets

We propose an efficient solution to the state estimation problem in multi-scan multi-sensor multiple extended target sensing scenarios. We first model the measurement process by a doubly inhomogeneous-generalized shot noise Cox process and then estimate the parameters using a jump Markov chain Monte Carlo sampling technique. The proposed approach scales linearly in the number of measurements and can take spatial properties of the sensors into account, herein, sensor noise covariance, detection probability, and resolution. Numerical experiments using radar measurement data suggest that the algorithm offers improvements in high clutter scenarios with closely spaced targets over state-of-the-art clustering techniques used in existing multiple extended target tracking algorithms.

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arXiv2023

Timely and Massive Communication in 6G: Pragmatics, Learning, and Inference

5G has expanded the traditional focus of wireless systems to embrace two new connectivity types: ultra-reliable low latency and massive communication. The technology context at the dawn of 6G is different from the past one for 5G, primarily due to the growing intelligence at the communicating nodes. This has driven the set of relevant communication problems beyond reliable transmission towards semantic and pragmatic communication. This paper puts the evolution of low-latency and massive communication towards 6G in the perspective of these new developments. At first, semantic/pragmatic communication problems are presented by drawing parallels to linguistics. We elaborate upon the relation of semantic communication to the information-theoretic problems of source/channel coding, while generalized real-time communication is put in the context of cyber-physical systems and real-time inference. The evolution of massive access towards massive closed-loop communication is elaborated upon, enabling interactive communication, learning, and cooperation among wireless sensors and actuators.

cs.NI↗

arXiv2023

High-Power and Safe RF Wireless Charging: Cautious Deployment and Operation

The wired charging and the need for battery replacements are critical barriers to unlimited, scalable, and sustainable mobile connectivity, motivating the interest in radio frequency (RF) wireless power transfer (WPT) technology. However, the inherently low end-to-end power transfer efficiency (PTE) and health/safety-related apprehensions about the technology are critical obstacles. Indeed, RF-WPT implementation and operation require efficient and cautious strategies and protocols, especially when targeting high-power charging, which constitutes the scope of this work. Herein, we overview the main factors affecting the end-to-end PTE of RF-WPT systems and their multiplicative effect and interdependencies. Moreover, we discuss key electromagnetic field (EMF) exposure metrics, safety limits, and approaches for efficient and EMF-aware deployment and operation. Quantitatively, we show that near-field RF charging may significantly reduce EMF exposure, and thus must be promoted. We also present our vision of a cyber-physical system for efficient and safe wireless charging, specify key components and their interrelation, and illustrate numerically the PTE attained by two modern low-power multi-antenna architectures in a simple setup. Throughout the paper, we highlight the need for high end-to-end PTE architectures and charging protocols transparently complying with EMF exposure regulations and outline relevant challenges and research directions. This work expands the vision and understanding of modern RF-WPT technology and constitutes a step towards making the technology attractive for worldwide commercial exploitation.

cs.NI↗

arXiv2023

Performance Comparison Between A Simple Full-Duplex Multi-Antenna Relay And A Passive Reflecting Intelligent Surface

In this paper, we propose to investigate a single RF chain multi-antenna full-duplex (FD) relay built with $b$-bit analog phase shifters and passive self-interference cancellation. Next, assuming only passive self-interference cancellation at the FD relay, we derive the achievable data rate of a system comprised of a source, the proposed FD relay, and a destination. We then compare the achievable data rate of the proposed FD relaying system with the achievable data rate of the same system but with the FD relay replaced by an ideal passive RIS. Our results show that the proposed relaying system with 2-bit quantized analog phase shifters significantly outperforms the RIS-assisted system. In fact, the performance gains are so large, at least for small to intermediate numbers of antenna elements, that we believe it makes this result of interest to the wireless community. The proposed FD relay can also be built with reconfigurable holographic surfaces, one surface for the transmit-side and one for the receive-side. For such a scenario, we derive the energy efficiency of the relay-assisted system and compare it with the RIS-assisted system. Our numerical results show that the energy efficiency of the relay-assisted system built with reconfigurable holographic surfaces is significantly higher than the energy efficiency of the RIS-assisted system. Intuitively, the RIS system is at a disadvantage since there the total transmit power $P_T$ is used entirely by the source, whereas in the FD relaying system the total transmit power $P_T$ is shared by the source and the FD relay in addition to the noise-cleansing process performed by the decode-and-forwarding at the FD relay.

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arXiv2023

Hybrid Automatic Repeat Request for Downlink Rate-Splitting Multiple Access

This work investigates the design of Hybrid Automatic Repeat Request (HARQ) strategies for downlink Rate-Splitting Multiple Access (RSMA). The existence of private and common stream as well as their conditioning for Successive Interference Cancellation (SIC), gives rise to an expanded set of opportunities for retransmission of failed packets. Specifically, we devise a scheme in which the retransmissions are scheduled through the common stream, which offers a higher success probability. With this, the common stream needs to carry both new and retransmitted bits, which leads to a layered HARQ (L-HARQ) strategy which is capable of trading off throughput and reliability. Simulation results demonstrate that the devised HARQ scheme outperforms RSMA with conventional HARQ, where each retransmission is handled independently through its own stream. It also helps in closing the throughput gap between HARQ and Adaptive Modulation and Coding (AMC) in the high Signal-to-Noise Ratio (SNR) regime while also achieving a decreased Packet Error Rate (PER) and a lower latency.

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arXiv2023

Strategic Coalition for Data Pricing in IoT Data Markets

This paper considers a market for trading Internet of Things (IoT) data that is used to train machine learning models. The data, either raw or processed, is supplied to the market platform through a network and the price of such data is controlled based on the value it brings to the machine learning model. We explore the correlation property of data in a game-theoretical setting to eventually derive a simplified distributed solution for a data trading mechanism that emphasizes the mutual benefit of devices and the market. The key proposal is an efficient algorithm for markets that jointly addresses the challenges of availability and heterogeneity in participation, as well as the transfer of trust and the economic value of data exchange in IoT networks. The proposed approach establishes the data market by reinforcing collaboration opportunities between device with correlated data to avoid information leakage. Therein, we develop a network-wide optimization problem that maximizes the social value of coalition among the IoT devices of similar data types; at the same time, it minimizes the cost due to network externalities, i.e., the impact of information leakage due to data correlation, as well as the opportunity costs. Finally, we reveal the structure of the formulated problem as a distributed coalition game and solve it following the simplified split-and-merge algorithm. Simulation results show the efficacy of our proposed mechanism design toward a trusted IoT data market, with up to 32.72% gain in the average payoff for each seller.

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arXiv2023

On the Statistical Relation of Ultra-Reliable Wireless and Location Estimation

Location information is often used as a proxy to guarantee the performance of a wireless communication link. However, localization errors can result in a significant mismatch with the guarantees, particularly detrimental to users operating the ultra-reliable low-latency communication (URLLC) regime. This paper unveils the fundamental statistical relations between location estimation uncertainty and wireless link reliability, specifically in the context of rate selection for ultra-reliable communication. We start with a simple one-dimensional narrowband Rayleigh fading scenario and build towards a two-dimensional scenario in a rich scattering environment. The wireless link reliability is characterized by the meta-probability, the probability with respect to localization error of exceeding the outage capacity, and by removing other sources of errors in the system, we show that reliability is sensitive to localization errors. The $ε$-outage coherence radius is defined and shown to provide valuable insight into the problem of location-based rate selection. However, it is generally challenging to guarantee reliability without accurate knowledge of the propagation environment. Finally, several rate-selection schemes are proposed, showcasing the problem's dynamics and revealing that properly accounting for the localization error is critical to ensure good performance in terms of reliability and achievable throughput.

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arXiv2023

Satellite edge computing for real-time and very-high resolution Earth observation

In real-time and high-resolution Earth observation imagery, Low Earth Orbit (LEO) satellites capture images that are subsequently transmitted to ground to create an updated map of an area of interest. Such maps provide valuable information for meteorology or environmental monitoring, but can also be employed in near-real time operation for disaster detection, identification, and management. However, the amount of data generated by these applications can easily exceed the communication capabilities of LEO satellites, leading to congestion and packet dropping. To avoid these problems, the Inter-Satellite Links (ISLs) can be used to distribute the data among the satellites for processing. In this paper, we address an energy minimization problem based on a general satellite mobile edge computing (SMEC) framework for real-time and very-high resolution Earth observation. Our results illustrate that the optimal allocation of data and selection of the compression parameters increase the amount of images that the system can support by a factor of 12 when compared to directly downloading the data. Further, energy savings greater than 11% were observed in a real-life scenario of imaging a volcanic island, while a sensitivity analysis of the image acquisition process demonstrates that potential energy savings can be as high as 92%.

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arXiv2023

Network Slicing for eMBB, URLLC, and mMTC: An Uplink Rate-Splitting Multiple Access Approach

There are three generic services in 5G: enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). To guarantee the performance of heterogeneous services, network slicing is proposed to allocate resources to different services. Network slicing is typically done in an orthogonal multiple access (OMA) fashion, which means different services are allocated non-interfering resources. However, as the number of users grows, OMA-based slicing is not always optimal, and a non-orthogonal scheme may achieve a better performance. This work aims to analyse the performances of different slicing schemes in uplink, and a promising scheme based on rate-splitting multiple access (RSMA) is studied. RSMA can provide a more flexible decoding order and theoretically has the largest achievable rate region than OMA and non-orthogonal multiple access (NOMA) without time-sharing. Hence, RSMA has the potential to increase the rate of users requiring different services. In addition, it is not necessary to decode the two split streams of one user successively, so RSMA lets suitable users split messages and designs an appropriate decoding order depending on the service requirements. This work shows that for network slicing RSMA can outperform NOMA counterpart, and obtain significant gains over OMA in some region.

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arXiv2023

Proximal Policy Optimization for Integrated Sensing and Communication in mmWave Systems

In wireless communication systems, mmWave beam tracking is a critical task that affects both sensing and communications, as it is related to the knowledge of the wireless channel. We consider a setup in which a Base Station (BS) needs to dynamically choose whether the resource will be allocated for one of the three operations: sensing (beam tracking), downlink, or uplink transmission. We devise an approach based on the Proximal Policy Optimization (PPO) algorithm for choosing the resource allocation and beam tracking at a given time slot. The proposed framework takes into account the variable quality of the wireless channel and optimizes the decisions in a coordinated manner. Simulation results demonstrate that the proposed method achieves significant performance improvements in terms of average packet error rate (PER) compared to the baseline methods while providing a significant reduction in beam tracking overhead. We also show that our proposed PPO-based framework provides an effective solution to the resource allocation problem in beam tracking and communication, exhibiting a great generalization performance regardless of the stochastic behavior of the system.

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arXiv2023

Goal-Oriented Scheduling in Sensor Networks with Application Timing Awareness

Taking inspiration from linguistics, the communications theoretical community has recently shown a significant recent interest in pragmatic , or goal-oriented, communication. In this paper, we tackle the problem of pragmatic communication with multiple clients with different, and potentially conflicting, objectives. We capture the goal-oriented aspect through the metric of Value of Information (VoI), which considers the estimation of the remote process as well as the timing constraints. However, the most common definition of VoI is simply the Mean Square Error (MSE) of the whole system state, regardless of the relevance for a specific client. Our work aims to overcome this limitation by including different summary statistics, i.e., value functions of the state, for separate clients, and a diversified query process on the client side, expressed through the fact that different applications may request different functions of the process state at different times. A query-aware Deep Reinforcement Learning (DRL) solution based on statically defined VoI can outperform naive approaches by 15-20%.

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arXiv2023

Goal-Oriented Communications in Federated Learning via Feedback on Risk-Averse Participation

We treat the problem of client selection in a Federated Learning (FL) setup, where the learning objective and the local incentives of the participants are used to formulate a goal-oriented communication problem. Specifically, we incorporate the risk-averse nature of participants and obtain a communication-efficient on-device performance, while relying on feedback from the Parameter Server (\texttt{PS}). A client has to decide its transmission plan on when not to participate in FL. This is based on its intrinsic incentive, which is the value of the trained global model upon participation by this client. Poor updates not only plunge the performance of the global model with added communication cost but also propagate the loss in performance on other participating devices. We cast the relevance of local updates as \emph{semantic information} for developing local transmission strategies, i.e., making a decision on when to ``not transmit". The devices use feedback about the state of the PS and evaluate their contributions in training the learning model in each aggregation period, which eventually lowers the number of occupied connections. Simulation results validate the efficacy of our proposed approach, with up to $1.4\times$ gain in communication links utilization as compared with the baselines.

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arXiv2023

Deep Learning for Asynchronous Massive Access with Data Frame Length Diversity

Grant-free non-orthogonal multiple access has been regarded as a viable approach to accommodate access for a massive number of machine-type devices with small data packets. The sporadic activation of the devices creates a multiuser setup where it is suitable to use compressed sensing in order to detect the active devices and decode their data. We consider asynchronous access of machine-type devices that send data packets of different frame sizes, leading to data length diversity. We address the composite problem of activity detection, channel estimation, and data recovery by posing it as a structured sparse recovery, having three-level sparsity caused by sporadic activity, symbol delay, and data length diversity. We approach the problem through approximate message passing with a backward propagation algorithm (AMP-BP), tailored to exploit the sparsity, and in particular the data length diversity. Moreover, we unfold the proposed AMP-BP into a model-driven network, termed learned AMP-BP (LAMP-BP), which enhances detection performance. The results show that the proposed LAMP-BP outperforms existing methods in activity detection and data recovery accuracy.

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arXiv2023

EMF Exposure Mitigation in RIS-Assisted Multi-Beam Communications

This paper proposes a method for reducing {third-party} exposure to electromagnetic fields (EMF) by exploiting the capability of a reconfigurable intelligent surfaces' (RIS) to manipulate the electromagnetic environment. We consider users capable of multi-beam communication, such that a user can use a set of different propagation paths enabled by the RIS. The optimization objective is to find propagation alternatives that allow to maintain the target quality of service while minimizing the level of EMF at surrounding non-intended users (NUEs). We provide an evolutionary heuristic solution based on Genetic Algorithm (GA) for power equalization and multi-beam selection of a codebook at the Base Station. Our results show valuable insights into how RIS-assisted multi-beam communications can mitigate EMF exposure with minimal degradation of the spectral efficiency.

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arXiv2023

Federated Learning in Satellite Constellations

Federated learning (FL) has recently emerged as a distributed machine learning paradigm for systems with limited and intermittent connectivity. This paper presents the new context brought to FL by satellite constellations, where the connectivity patterns are significantly different from the ones observed in conventional terrestrial FL. The focus is on large constellations in low Earth orbit (LEO), where each satellites participates in a data-driven FL task using a locally stored dataset. This scenario is motivated by the trend towards mega constellations of interconnected small satellites in LEO and the integration of artificial intelligence in satellites. We propose a classification of satellite FL based on the communication capabilities of the satellites, the constellation design, and the location of the parameter server. A comprehensive overview of the current state-of-the-art in this field is provided and the unique challenges and opportunities of satellite FL are discussed. Finally, we outline several open research directions for FL in satellite constellations and present some future perspectives on this topic.

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