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Alireza Vahid

Publications and source records attributed to Alireza Vahid.

At least 19 recordsLinked to original sources
arXiv2026

Secure Pseudonymetry with DSSS Watermarking for LEO Satellites

Low-Earth-orbit (LEO) constellations offer dense signals of opportunity (SOP) for positioning, navigation, and timing (PNT) without dedicated navigation infrastructure. However, using these signals passively, by only listening to the downlink, is difficult when beacon/pilot components are weak, signals from multiple satellites overlap, or the receiver cannot reliably identify which satellite generated a given observable. This paper proposes a low-power direct-sequence spread-spectrum (DSSS) watermarking framework that embeds a recoverable pseudonymous satellite identifier and keyed authentication field into a LEO downlink signal. The watermark carries a public satellite pseudonym for identification and a keyed HMAC-SHA256-based authentication field for authorized verification, enabling passive detection and verification without full downlink demodulation. We evaluate the framework in a multi-satellite simulation using Starlink two-line-element (TLE)-derived geometry, Doppler, and link-budget scaling with Sionna RT-based local propagation. The results demonstrate candidate-satellite detection, public-ID recovery, and keyed verification under co-observed satellite interference, with limited perturbation to the simulated beacon/pilot and primary downlink components. The framework targets future transmitter-enabled LEO systems while remaining transparent to legacy receivers that do not process the low-power watermark.

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arXiv2026

Geometry-Aware Decentralized Sinkhorn for Wasserstein Barycenters

Distributed systems require fusing heterogeneous local probability distributions into a global summary over sparse and unreliable communication networks. Traditional consensus algorithms, which average distributions in Euclidean space, ignore their inherent geometric structure, leading to misleading results. Wasserstein barycenters offer a geometry-aware alternative by minimizing optimal transport costs, but their entropic approximations via the Sinkhorn algorithm typically require centralized coordination. This paper proposes a fully decentralized Sinkhorn algorithm that reformulates the centralized geometric mean as an arithmetic average in the log-domain, enabling approximation through local gossip protocols. Agents exchange log-messages with neighbors, interleaving consensus phases with local updates to mimic centralized iterations without a coordinator. To optimize bandwidth, we integrate event-triggered transmissions and b-bit quantization, providing tunable trade-offs between accuracy and communication while accommodating asynchrony and packet loss. Under mild assumptions, we prove convergence to a neighborhood of the centralized entropic barycenter, with bias linearly dependent on consensus tolerance, trigger threshold, and quantization error. Complexity scales near-linearly with network size. Simulations confirm near-centralized accuracy with significantly fewer messages, across various topologies and conditions.

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arXiv2026

Near-Field User Location Inference From Far-Field Power Measurements

Near-field beamfocusing enabled by extremely large-aperture arrays (ELAA) is a promising 6G technique for massive connectivity and high spectrum efficiency. While beamfocusing concentrates energy at an intended user, the radiated field outside the focal point exhibits a structured leakage that varies with the focal-point coordinates. This paper shows that this leakage enables a new form of passive user localization in which distributed far-field sensors measuring only received power can infer the user's location by exploiting this location-dependent power signature. Using the induced noncentral chi-square statistics, we derive a Bayesian Cramér-Rao lower bound (BCRLB) that establishes the fundamental limits of this inference problem. We then evaluate a model-based grid-search estimator and an attention-based permutation-invariant deep learning regressor (DeepSet). Results under both line-of-sight (LoS) and multipath propagation confirm that reliable location inference is feasible, with accuracy improving as more sensors and snapshots are used.

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arXiv2026

Starlink Beacons for Passive LEO-Aided 9D Navigation

Global Navigation Satellite Systems (GNSS) underpin positioning, navigation, and timing (PNT), yet their low-power signals are easily blocked or disrupted, leaving gaps in PNT availability in contested environments (e.g. maritime settings) where interference, spoofing, or denial can occur. A key practical need is an independent, ubiquitous aiding signal that can be tracked passively and fused with inertial sensing to sustain full navigation-state estimation without dedicated or cooperative infrastructure. This paper presents an end-to-end LEO-aided hybrid framework that fuses GPS, Starlink downlink beacons, and an inertial measurement unit (IMU) in a 9D (3D position, 3D velocity, and 3D attitude) PNT system using an extended Kalman filter (EKF). We (i) extract Doppler-rate from Starlink downlink beacon tones by associating measurements with satellite IDs, (ii) benchmark beacon Doppler-rate against OFDM-derived range observables under a common processing/estimation pipeline, and (iii) integrate the resulting observable into inertial navigation. We evaluate GPS/IMU, Starlink/IMU, and GPS-Starlink-IMU using Fisher-information predictions, Monte Carlo simulations, and hardware measurements. Results show that Starlink Doppler-rate provides meaningful complementary PNT information, and can aid 9D estimation when GNSS is degraded or intermittently unavailable.

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arXiv2025

Capacity Results for Intermittent X-Channels with Delayed Channel State Feedback

We characterize the capacity region of noiseless X-Channels with intermittent connectivity and delayed channel state information at the transmitters. We consider the general case in which each transmitter has a common message for both receivers, and a private message for each one of them. We develop a new set of outer-bounds that quantify the interference alignment capability of each transmitter with delayed channel state feedback and when each receiver must receive a baseline entropy corresponding to the common message. We also develop a transmission strategy that achieves the outer-bounds under homogeneous channel assumption by opportunistically treating the X-Channel as a combination of a number of well-known problems such as the interference channel and the multicast channel. The capacity-achieving strategies of these sub-problems must be interleaved and carried on simultaneously in certain regimes in order to achieve the X-Channel outer-bounds. We also extend the outer-bounds to include non-homogeneous channel parameters.

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arXiv2024

Model-based Deep Learning for Wireless Resource Allocation in RSMA Communications Systems

Rate-splitting multiple access (RSMA) has been proven as an effective communication scheme for 5G and beyond. However, current approaches to RSMA resource management require complicated iterative algorithms, which cannot meet the stringent latency requirement by users with limited resources. Recently, data-driven methods are explored to alleviate this issue. However, they suffer from poor generalizability and scarce training data to achieve satisfactory performance. In this paper, we propose a fractional programming (FP) based deep unfolding (DU) approach to address resource allocation problem for a weighted sum rate optimization in RSMA. By carefully designing the penalty function, we couple the variable update with projected gradient descent algorithm (PGD). Following the structure of PGD, we embed a few learnable parameters in each layer of the DU network. Through extensive simulation, we have shown that the proposed model-based neural networks can yield similar results compared to the traditional optimization algorithm for RSMA resource management but with much lower computational complexity, less training data, and higher resilience to out-of-distribution (OOD) data.

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arXiv2024

Model-based Deep Learning for QoS-Aware Rate-Splitting Multiple Access Wireless Systems

Next generation communications demand for better spectrum management, lower latency, and guaranteed quality-of-service (QoS). Recently, Artificial intelligence (AI) has been widely introduced to advance these aspects in next generation wireless systems. However, such AI applications suffer from limited training data, low robustness, and poor generalization capabilities. To address these issues, a model-driven deep unfolding (DU) algorithm is introduced in this paper to bridge the gap between traditional model-driven communication algorithms and data-driven deep learning. Focusing on the QoS-aware rate-splitting multiple access (RSMA) resource allocation problem in multi-user communications, a conventional fractional programming (FP) algorithm is first applied as a benchmark. The solution is then refined by the application of projection gradient descent (PGD). DU is employed to further speed up convergence procedure, hence improving the efficiency of PGD. Moreover, the feasibility of results is guaranteed by designing a low-complexity projection based on scale factors, plus adding violation control mechanisms into the loss function that minimizes error rates. Finally, we provide a detailed analysis of the computational complexity and analysis design of the proposed DU algorithm. Extensive simulations are conducted and the results demonstrate that the proposed DU algorithm can reach the optimal communication efficiency with a mere $0.024\%$ violation rate for 4 layers DU. The DU algorithm also exhibits robustness in out-of-distribution tests and can be effectively trained with as few as 50 samples.

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arXiv2024

Mix-and-Conquer: Beamforming Design with Interconnected RIS for Multi-User Networks

We propose a new reconfigurable intelligent surface (RIS) structure, referred to as interconnected RIS (I-RIS), which allows the RIS elements to be interconnected and share the incident signals using simple binary radio frequency (RF) switches and mix them into the reflecting signals. This structure enables multi-user scaling and requires fewer elements (i.e., a compact structure) compared to standard RIS (S-RIS), which assumes no interconnection between the elements. The I-RIS compact design makes it practical for deployment on space-limited nodes, e.g., unmanned aerial vehicles (UAVs). Hence, in this work, we propose a beamforming design based on I-RIS in a multi-user network, where we use binary RF switches as RIS elements. We show that our switch-based I-RIS offers a higher gain compared to an S-RIS using phase shifters. Finally, we introduce two optimization methods, sigmoid filled function (SFF) and semi-definite binary optimization (SBO), to optimize the RIS elements and evaluate their performance in terms of sum-rate and complexity.

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arXiv2024

Recovering a Message from an Incomplete Set of Noisy Fragments

We consider the problem of communicating over a channel that breaks the message block into fragments of random lengths, shuffles them out of order, and deletes a random fraction of the fragments. Such a channel is motivated by applications in molecular data storage and forensics, and we refer to it as the torn-paper channel. We characterize the capacity of this channel under arbitrary fragment length distributions and deletion probabilities. Precisely, we show that the capacity is given by a closed-form expression that can be interpreted as F - A, where F is the coverage fraction ,i.e., the fraction of the input codeword that is covered by output fragments, and A is an alignment cost incurred due to the lack of ordering in the output fragments. We then consider a noisy version of the problem, where the fragments are corrupted by binary symmetric noise. We derive upper and lower bounds to the capacity, both of which can be seen as F - A expressions. These bounds match for specific choices of fragment length distributions, and they are approximately tight in cases where there are not too many short fragments.

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arXiv2024

Capacity Results for Non-Ergodic Multi-Modal Broadcast Channels with Controllable Statistics

Movable antennas and reconfigurable intelligent surfaces enable a new paradigm in which channel statistics can be controlled and altered. Further, the known trajectory and operation protocol of communication satellites results in networks with predictable statistics. The predictability of future changes results in a non-ergodic model for which the fundamentals are largely unknown. We consider the canonical two-user broadcast erasure channel in which channel statistics vary at a priori known points. We consider a multi-modal setting with two non-transient modes (whose lengths scale linearly with the blocklength) and an arbitrary number of transient modes. We provide a new set of outer-bounds on the capacity region of this problem when the encoder has access to causal ACK/NACK feedback. The outer-bounds reveal the significant role of the non-transient mode with higher erasure probability both on the outer and the inner bounds. We show the outer-bounds are achievable in non-trivial regimes, characterizing the capacity region for a wide range of parameters. We also discuss the regimes where the inner and outer bounds diverge and analyze the gap between the two. A key finding of this work is the significant gain of inter-modal coding over the separate treating of individual modes.

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arXiv2024

Capacity-Maximizing Dynamic User Association in Double RIS-Aided Broadcast Networks

We introduce an information-theoretic framework to dynamically pair up different reconfigurable intelligent surfaces (RISs) with wireless users with goal of maximizing the fundamental network capacity. We focus on a double RIS-aided broadcast packet network with two users. We show using a dynamic RIS-user association and an opportunistic protocol, the network capacity could be significantly enhanced and superior to other benchmarks with static associations. The results include new outer-bounds on network capacity and their achievability. We discuss the optimal RIS-user association.

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arXiv2023

Physical Layer Authentication and Security Design in the Machine Learning Era

Security at the physical layer (PHY) is a salient research topic in wireless systems, and machine learning (ML) is emerging as a powerful tool for providing new data-driven security solutions. Therefore, the application of ML techniques to the PHY security is of crucial importance in the landscape of more and more data-driven wireless services. In this context, we first summarize the family of bespoke ML algorithms that are eminently suitable for wireless security. Then, we review the recent progress in ML-aided PHY security, where the term "PHY security" is classified into two different types: i) PHY authentication and ii) secure PHY transmission. Moreover, we treat neural networks as special types of ML and present how to deal with PHY security optimization problems using neural networks. Finally, we identify some major challenges and opportunities in tackling PHY security challenges by applying carefully tailored ML tools.

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arXiv2022

Control and Placement of Finite-Resolution Intelligent Surfaces in IoT Systems with Imperfect CSI

In this paper, we study the advantages of using reconfigurable intelligent surfaces (RISs) for interference suppression in single-input single-output (SISO) distributed Internet of Things (IoT) networks. Implementing RIS-assisted networks confronts various problems, mostly related to the control and placement of the RIS. To tackle the control-related challenges, we consider noisy and local channel knowledge, based on which we devise algorithms to optimize the potentially distributed RISs to achieve an overall network objective, such as the sum-rate. We use a network with a centralized RIS as a benchmark for our comparisons. We further assume low-bit phase shifters at the RIS to capture real-world hardware limitations. We also study the placement of the RIS and analytically quantify the minimum required degrees-of-control for the RIS as a function of its location to guarantee a specific network performance metric and verify the results via simulations.

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arXiv2022

GreenMO: Virtualized User-proportionate MIMO

With the turn of new decade, wireless communications face a major challenge on connecting many more new users and devices, at the same time being energy efficient and minimizing its carbon footprint. However, the current approaches to address the growing number of users and spectrum demands, like traditional fully digital architectures for Massive MIMO, demand exorbitant energy consumption. The reason is that traditionally MIMO requires a separate RF chain per antenna, so the power consumption scales with number of antennas, instead of number of users, hence becomes energy inefficient. Instead, GreenMO creates a new massive MIMO architecture which is able to use many more antennas while keeping power consumption to user-proportionate numbers. To achieve this GreenMO introduces for the first time, the concept of virtualization of the RF chain hardware. Instead of laying the RF chains physically to each antenna, GreenMO creates these RF chains virtually in digital domain. This also enables GreenMO to be the first flexible massive MIMO architecture. Since GreenMO's virtual RF chains are created on the fly digitally, it can tune the number of these virtual chains according to the user load, hence always flexibly consume user-proportionate power. Thus, GreenMO paves the way for green and flexible massive MIMO. We prototype GreenMO on a PCB with eight antennas and evaluate it with a WARPv3 SDR platform in an office environment. The results demonstrate that GreenMO is 3x more power-efficient than traditional Massive MIMO and 4x more spectrum-efficient than traditional OFDMA systems, while multiplexing 4 users, and can save upto 40% power in modern 5G NR base stations.

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arXiv2022

Coded Shotgun Sequencing

Most DNA sequencing technologies are based on the shotgun paradigm: many short reads are obtained from random unknown locations in the DNA sequence. A fundamental question, studied in arXiv:1203.6233, is what read length and coverage depth (i.e., the total number of reads) are needed to guarantee reliable sequence reconstruction. Motivated by DNA-based storage, we study the coded version of this problem;i.e., the scenario where the DNA molecule being sequenced is a codeword from a predefined codebook. Our main result is an exact characterization of the capacity of the resulting shotgun sequencing channel as a function of the read length and coverage depth. In particular, our results imply that, while in the uncoded case, $O(n)$ reads of length greater than $2\log{n}$ are needed for reliable reconstruction of a length-$n$ binary sequence, in the coded case, only $O(n/\log{n})$ reads of length greater than $\log{n}$ are needed for the capacity to be arbitrarily close to $1$.

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arXiv2022

Reassembly Codes for the Chop-and-Shuffle Channel

We study the problem of retrieving data from a channel that breaks the input sequence into a set of unordered fragments of random lengths, which we refer to as the chop-and-shuffle channel. The length of each fragment follows a geometric distribution. We propose nested Varshamov-Tenengolts (VT) codes to recover the data. We evaluate the error rate and the complexity of our scheme numerically. Our results show that the decoding error decreases as the input length increases, and our method has a significantly lower complexity than the baseline brute-force approach. We also propose a new construction for VT codes, quantify the maximum number of the required parity bits, and show that our approach requires fewer parity bits compared to known results.

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arXiv2021

On the Stability Region of Intermittent Interference Networks

Recent information-theoretic studies have resulted in several interference management (IM) techniques that promise significant capacity improvements over interference avoidance techniques. However, in practice, the stable throughput region is a more relevant metric compared to the capacity region. In this work, we focus on the stable throughput region of a two-pair intermittent interference network with distributed transmitters and propose a queue-based transmission protocol in different regimes to handle the data between queues. In this context, we translate physical-layer IM protocols to accommodate stochastic message arrivals. To evaluate our proposed techniques, we compare the stable throughput region to the capacity region and show, through simulations, that the stable throughput region matches the capacity region when the latter is known. We show that in order to achieve the optimal stable throughput region, new ingredients are needed when compared to prior results. We quantify the trade-off between the encoding/decoding complexity of the proposed scheme (in terms of number of required algebraic operations), and the achievable rates. Finally, we study the lifetime of messages (i.e. the duration from arrival to successful delivery) vis-a-vis the total communication time, and we observe that the average lifetime scales as the square root of the total communication time.

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