arXiv subjects

Ali Rasteh

Publications and source records attributed to Ali Rasteh.

10 recordsLinked to original sources

arXiv2026

Efficient Upper Mid-Band Spectrum Sensing with Multiple Signals

Spectrum sensing is a fundamental problem in the upper mid-band, where spectrum resources are shared with incumbent systems. This paper considers frequency-domain occupancy estimation when the number of primary user signals, their bandwidths, and their signal-to-noise ratios are all unknown. We develop a generalized likelihood ratio test and a computationally efficient search procedure that combines binary search with dynamic programming to select the set of intervals maximizing the sum of log-likelihoods. The proposed method is validated through both simulation data and over-the-air experimental data using an upper mid-band software-defined radio (SDR), demonstrating its practical applicability.

eess.SP↗

arXiv2026

Low-rank Preconditioning in Beamspace Domain For Massive MU-MIMO Long-Term Beamforming

Long-term beamforming substantially reduces the channel estimation and inversion overhead of conventional massive MU-MIMO receivers; yet, its construction still hinges on the inversion of a large Hermitian matrix, whose condition number deteriorates with the per-user SNR dynamic range. When this inversion is approximated in hardware via the conjugate gradient (CG) algorithm, the deterioration directly inflates the iteration count and, consequently, the energy and latency budget. We propose a hardware-friendly low-rank preconditioning framework that targets exactly this bottleneck. The preconditioner is constructed from the top eigenpairs of the long-term covariance matrix through a randomized complex eigenvalue decomposition (RC-EVD), whose inner QR factorizations are realized via a Cholesky-based scheme (QRC), confining the dominant cost to generalized matrix multiplication (GEMM) and small triangular solves that map naturally onto systolic arrays. We further show that performing the preconditioned CG inversion in the beamspace domain induces sparsification of the system matrix and provides additional convergence acceleration at negligible transformation cost. Ray-tracing simulations confirm that the joint scheme reduces the required CG iteration count by two to three while matching the post-equalization SINR of the exact inversion.

eess.SP↗

arXiv2026

Interference Suppression for Massive MU-MIMO Long-Term Beamforming with Matrix Inversion Approximation

Long-term beamforming (LTBF) is a widely-used scalable alternative to instantaneous multi-user MIMO processing that leverages slowly varying spatial channel statistics. VLSI implementations require matrix inversion that become computationally challenging for massive MIMO systems with large number of antennas. In this work, we show that dominant interferers significantly degrade the numerical conditioning of the LTBF covariance matrix, leading to severe performance loss in finite-precision implementations of polynomial and conjugate gradient (CG) based inversion methods. To address this issue, we propose a subspace nulling approach that operates solely on long-term channel statistics and acts as an implicit preconditioning step for LTBF. By projecting the received signal onto the orthogonal complement of the dominant interference subspace, the proposed method reduces the eigenvalue spread of the covariance matrix and improves numerical stability. Through ray-tracing simulations in a realistic 5G scenario, we demonstrate that the proposed method substantially reduces the number of CG iterations required to achieve near-optimal performance across floating-point and fixed-point implementations while preserving the low-overhead nature of LTBF.

eess.SP↗

arXiv2025

A Spatial Array for Spectrally Agile Wireless Processing

Massive MIMO is a cornerstone of next-generation wireless communication, offering significant gains in capacity, reliability, and energy efficiency. However, to meet emerging demands such as high-frequency operation, wide bandwidths, co-existence, integrated sensing, and resilience to dynamic interference, future systems must exhibit both scalability and spectral agility. These requirements place increasing pressure on the underlying processing hardware to be both efficient and reconfigurable. This paper proposes a custom-designed spatial array architecture that serves as a reconfigurable, general-purpose core optimized for a class of wireless kernels that commonly arise in diverse communications and sensing tasks. The proposed spatial array is evaluated against specialized cores for each kernel using High-Level Synthesis (HLS). Both the reconfigurable and specialized designs are synthesized in a 32 nm process to assess latency, throughput, area, and power in realistic processes. The results identify conditions under which general-purpose systolic architectures can approach the efficiency of specialized cores, thereby paving the way toward more scalable and agile systems.

eess.SP↗

arXiv2025

Scalable Long-Term Beamforming for Massive Multi-User MIMO

Fully digital massive MIMO systems with large numbers (1000+) of antennas offer dramatically increased capacity gains from spatial multiplexing and beamforming. Designing digital receivers that can scale to these array dimensions presents significant challenges regarding both channel estimation overhead and digital computation. In the massive MIMO setting, long-term beamforming is widely-used since it offers significant reductions in both computation and channel estimation overhead. Long-term beamforming operates by projecting the data onto a low-dimensional subspace that can be tracked at a relatively slow time-scale from the long-term channel parameters. In this setting, we show how to optimally compute the projection matrix to maximize a capacity upper-bound using a matrix inverse square root. Computationally efficient methods are then presented to perform the matrix computation. The methods can be realized with matrix-matrix multiplies, making them amenable to systolic array implementations in hardware. Error analysis bounds on the degradation in the SINR for users are derived. Ray tracing simulations in a realistic rural uplink setting show minimal loss relative to complete instantaneous MMSE beamforming while offering significant overhead and computational gains.

eess.SP↗

arXiv2025

Computationally Efficient Signal Detection with Unknown Bandwidths

Signal detection in environments with unknown signal bandwidth and time intervals is a fundamental problem in adversarial and spectrum-sharing scenarios. This paper addresses the problem of detecting signals occupying unknown degrees of freedom from non-coherent power measurements, where the signal is constrained to an interval in one dimension or a hyper-cube in multiple dimensions. A GLRT is derived, resulting in a straightforward metric involving normalized average signal energy for each candidate signal set. We present bounds on false alarm and missed detection probabilities, demonstrating their dependence on SNR and signal set sizes. To overcome the inherent computational complexity of exhaustive searches, we propose a computationally efficient binary search method, reducing the complexity from O(N^2) to O(N) for one-dimensional cases. Simulations indicate that the method maintains performance near exhaustive searches and achieves asymptotic consistency, with interval-of-overlap converging to one under constant SNR as measurement size increases. The simulation studies also demonstrate superior performance and reduced complexity compared to contemporary neural network-based approaches, specifically outperforming custom-trained U-Net models in spectrum detection tasks.

eess.SP↗

arXiv2024

Near-Field Measurement System for the Upper Mid-Band

The upper mid-band (or FR3, spanning 6-24 GHz) is a crucial frequency range for next-generation mobile networks, offering a favorable balance between coverage and spectrum efficiency. From another perspective, the systems operating in the near-field in both indoor environment and outdoor environments can support line-of-sight multiple input multiple output (MIMO) communications and be beneficial from the FR3 bands. In this paper, a novel method is proposed to measure the near-field parameters leveraging a recently developed reflection model where the near-field paths can be described by their image points. We show that these image points can be accurately estimated via triangulation from multiple measurements with a small number of antennas in each measurement, thus affording a low-cost procedure for near-field multi-path parameter extraction. A preliminary experimental apparatus is presented comprising 2 transmit and 2 receive antennas mounted on a linear track to measure the 2x2 MIMO channel at various displacements. The system uses a recently-developed wideband radio frequency (RF) transceiver board with fast frequency switching, an FPGA for fast baseband processing, and a new parameter extraction method to recover paths and spherical characteristics from the multiple 2x2 measurements.

eess.SP↗

arXiv2024

Tracking Real-time Anomalies in Cyber-Physical Systems Through Dynamic Behavioral Analysis

Increased connectivity and remote reprogrammability/reconfigurability features of embedded devices in current-day power systems (including interconnections between information technology -- IT -- and operational technology -- OT -- networks) enable greater agility, reduced operator workload, and enhanced power system performance and capabilities. However, these features also expose a wider cyber-attack surface, underscoring need for robust real-time monitoring and anomaly detection in power systems, and more generally in Cyber-Physical Systems (CPS). The increasingly complex, diverse, and potentially untrustworthy software and hardware supply chains also make need for robust security tools more stringent. We propose a novel framework for real-time monitoring and anomaly detection in CPS, specifically smart grid substations and SCADA systems. The proposed method enables real-time signal temporal logic condition-based anomaly monitoring by processing raw captured packets from the communication network through a hierarchical semantic extraction and tag processing pipeline into time series of semantic events and observations, that are then evaluated against expected temporal properties to detect and localize anomalies. We demonstrate efficacy of our methodology on a hardware in the loop testbed, including multiple physical power equipment (real-time automation controllers and relays) and simulated devices (Phasor Measurement Units -- PMUs, relays, Phasor Data Concentrators -- PDCs), interfaced to a dynamic power system simulator. The performance and accuracy of the proposed system is evaluated on multiple attack scenarios on our testbed.

eess.SY↗

arXiv2022

Isoform Function Prediction Using a Deep Neural Network

Isoforms are mRNAs produced from the same gene site in the phenomenon called Alternative Splicing. Studies have shown that more than 95% of human multi-exon genes have undergone alternative splicing. Although there are few changes in mRNA sequence, They may have a systematic effect on cell function and regulation. It is widely reported that isoforms of a gene have distinct or even contrasting functions. Most studies have shown that alternative splicing plays a significant role in human health and disease. Despite the wide range of gene function studies, there is little information about isoforms' functionalities. Recently, some computational methods based on Multiple Instance Learning have been proposed to predict isoform function using gene function and gene expression profile. However, their performance is not desirable due to the lack of labeled training data. In addition, probabilistic models such as Conditional Random Field (CRF) have been used to model the relation between isoforms. This project uses all the data and valuable information such as isoform sequences, expression profiles, and gene ontology graphs and proposes a comprehensive model based on Deep Neural Networks. The UniProt Gene Ontology (GO) database is used as a standard reference for gene functions. The NCBI RefSeq database is used for extracting gene and isoform sequences, and the NCBI SRA database is used for expression profile data. Metrics such as Receiver Operating Characteristic Area Under the Curve (ROC AUC) and Precision-Recall Under the Curve (PR AUC) are used to measure the prediction accuracy.

q-bio.GN↗

arXiv2021

Encrypted Internet traffic classification using a supervised Spiking Neural Network

Internet traffic recognition is an essential tool for access providers since recognizing traffic categories related to different data packets transmitted on a network help them define adapted priorities. That means, for instance, high priority requirements for an audio conference and low ones for a file transfer, to enhance user experience. As internet traffic becomes increasingly encrypted, the mainstream classic traffic recognition technique, payload inspection, is rendered ineffective. This paper uses machine learning techniques for encrypted traffic classification, looking only at packet size and time of arrival. Spiking neural networks (SNN), largely inspired by how biological neurons operate, were used for two reasons. Firstly, they are able to recognize time-related data packet features. Secondly, they can be implemented efficiently on neuromorphic hardware with a low energy footprint. Here we used a very simple feedforward SNN, with only one fully-connected hidden layer, and trained in a supervised manner using the newly introduced method known as Surrogate Gradient Learning. Surprisingly, such a simple SNN reached an accuracy of 95.9% on ISCX datasets, outperforming previous approaches. Besides better accuracy, there is also a very significant improvement on simplicity: input size, number of neurons, trainable parameters are all reduced by one to four orders of magnitude. Next, we analyzed the reasons for this good accuracy. It turns out that, beyond spatial (i.e. packet size) features, the SNN also exploits temporal ones, mostly the nearly synchronous (within a 200ms range) arrival times of packets with certain sizes. Taken together, these results show that SNNs are an excellent fit for encrypted internet traffic classification: they can be more accurate than conventional artificial neural networks (ANN), and they could be implemented efficiently on low power embedded systems.

cs.LG↗