About

Andreas Molisch received his degrees from the Technical University Vienna, Austria, including a Doctoral Degree in Mobile Communications and Electrical Engineering, as well as a Master's Degree in Electrical Engineering. He spent ten years in industry, most recently serving as Chief Wireless Standards Architect at Mitsubishi Electric Research Labs. In 2009, he joined the University of Southern California (USC) as a Professor in the Ming Hsieh Department of Electrical and Computer Engineering, where he founded WiDeS. In 2017, he was appointed to the Solomon Golomb – Andrew and Erna Viterbi Chair. His research interests revolve around wireless propagation channels, wireless systems design, and their interaction. He is particularly focused on wireless channel measurement and modeling for 5G and beyond 5G systems, wireless video distribution, hybrid beamforming, UWB/TOA based localization, caching at the wireless edge, and novel modulation/multiple access methods. Dr. Molisch has published extensively, including books, journal papers, and conference papers, and holds numerous patents. He has contributed to standards and has been involved in editorial and leadership roles in various international conferences and standardization groups. He is a Fellow of the National Academy of Inventors, AAAS, IEEE, and IET, and a member of the Austrian Academy of Sciences. His awards include the IEEE Communications Society Edwin H Armstrong Achievement Award, the IEEE Vehicular Technology Society Evans Avant-Garde Award, the IEEE Communications Society Edwin Howard Armstrong Award, and the IEEE Technical Field Award for Communications, among others. His work emphasizes bridging academic research with practical applications, especially in wireless communications and standards development.

Research topics

• Computer Science
• Telecommunications
• Computer Security
• Engineering
• Computer network
• Physics
• Optoelectronics
• Electronic engineering

Selected publications

• Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction

  IEEE Transactions on Intelligent Transportation Systems · 2026-01-01

  preprintOpen accessSenior author

  Achieving reliable multidimensional Vehicle-to-Vehicle (V2V) channel state information (CSI) prediction is both challenging and crucial for optimizing downstream tasks that depend on instantaneous CSI. This work extends traditional prediction approaches by focusing on four-dimensional (4D) CSI, which includes predictions over time, bandwidth, and antenna (TX and RX) space. Such a comprehensive framework is essential for addressing the dynamic nature of mobility environments within intelligent transportation systems, necessitating the capture of both temporal and spatial dependencies across diverse domains. To address this complexity, we propose a novel context-conditioned spatiotemporal predictive learning method. This method leverages causal convolutional long short-term memory (CA-ConvLSTM) to effectively capture dependencies within 4D CSI data, and incorporates context-conditioned attention mechanisms to enhance the efficiency of spatiotemporal memory updates. Additionally, we introduce an adaptive meta-learning scheme tailored for recurrent networks to mitigate the issue of accumulative prediction errors. We validate the proposed method through empirical studies conducted across three different geometric configurations and mobility scenarios. Our results demonstrate that the proposed approach outperforms existing state-of-the-art predictive models, achieving superior performance across various geometries. Moreover, we show that the meta-learning framework significantly enhances the performance of recurrent-based predictive models in highly challenging cross-geometry settings, thus highlighting its robustness and adaptability.

  PublisherOA PDFDOI

• Review of Wireless Propagation Research—Past, Current and Future Developments

  IEEE Transactions on Antennas and Propagation · 2026-03-27

  article

  Propagation- and channel models are required in almost all stages of the development, roll-out and operation of wireless communication systems. Although research in this area has been ongoing for decades, there are still numerous open issues, which need to be addressed. Propagation models are dependent upon carrier frequency and bandwidth. Thus new frequency bands require new studies. Furthermore, new applications such as integrated communications and sensing, vehicular communications, integration of non-terrestrial networks (NTN) etc. have to be considered. This paper provides an overview of propagation and channel modeling covering past, ongoing and future topics. Channel measurement techniques, site-specific and site general models are covered along with new applications and more ongoing activities such as millimeter and THz frequencies, NTN, reconfigurable intelligent surfaces and near-field effects.

  PublisherDOI

• mmWave Sensing for Detecting Movement Through Thermoplastic Masks During Radiation Therapy Treatment

  ArXiv.org · 2026-01-31

  articleOpen accessSenior author

  Precision in radiation therapy relies on immobilization systems that limit patient motion. Thermoplastic masks are commonly used for this purpose, but subtle voluntary and involuntary movements such as jaw shifts, deep breathing, or eye squinting may still compromise treatment accuracy. Existing motion tracking methods are limited: optical systems require a clear line of sight and only detect surface motion, while X-ray-based tracking introduces additional ionizing radiation. This study explores the use of low-power, non-ionizing millimeter-wave (mmWave) sensing for through-mask motion detection. We characterize the RF properties of thermoplastic mask material in the 28-38 GHz range and perform motion detection using a 1 GHz bandwidth centered at 28 GHz. We use a frequency-domain system with horn antennas in a custom-built anechoic chamber to capture changes in the amplitude and phase of transmitted RF waves in response to subtle head and facial movements. These findings lay groundwork for future real-time through-mask motion tracking and future integration with multi-antenna systems and machine learning for error correction during radiotherapy.

  PublisherOA PDF

• mmWave Sensing for Detecting Movement Through Thermoplastic Masks During Radiation Therapy Treatment

  Open MIND · 2026-01-31

  preprintSenior author

  Precision in radiation therapy relies on immobilization systems that limit patient motion. Thermoplastic masks are commonly used for this purpose, but subtle voluntary and involuntary movements such as jaw shifts, deep breathing, or eye squinting may still compromise treatment accuracy. Existing motion tracking methods are limited: optical systems require a clear line of sight and only detect surface motion, while X-ray-based tracking introduces additional ionizing radiation. This study explores the use of low-power, non-ionizing millimeter-wave (mmWave) sensing for through-mask motion detection. We characterize the RF properties of thermoplastic mask material in the 28-38 GHz range and perform motion detection using a 1 GHz bandwidth centered at 28 GHz. We use a frequency-domain system with horn antennas in a custom-built anechoic chamber to capture changes in the amplitude and phase of transmitted RF waves in response to subtle head and facial movements. These findings lay groundwork for future real-time through-mask motion tracking and future integration with multi-antenna systems and machine learning for error correction during radiotherapy.

  DOI

• Hybrid Codebook Design for Localization Using Electromagnetically Reconfigurable Fluid Antenna System

  IEEE Journal of Selected Topics in Signal Processing · 2026-01-01

  articleOpen accessSenior author

  Electromagnetically reconfigurable fluid antenna system (ER-FAS) introduces additional degrees of freedom in the electromagnetic (EM) domain by dynamically steering per-antenna radiation patterns, thereby enhancing power efficiency in wireless links. Unlike prior works on spatially reconfigurable FAS, which adjust element positions, ER-FAS provides direct control over each element's EM characteristics to realize on-demand beam-pattern shaping. While existing studies have exploited ER-FAS to boost spectral efficiency, this paper explores its application for downlink localization. We consider a multiple-input single-output (MISO) system in which a multi-antenna ER-FAS at the base station serves a single-antenna user equipment (UE). We consider two reconfigurability paradigms: (i) a synthesis model where each antenna generates desired beampatterns from a finite set of EM basis functions, and (ii) a finite-state selection model in which each antenna selects a pattern from a predefined set of patterns. For both paradigms, we formulate the joint baseband (BB) and EM precoder design to minimize the UE position error bound. In the synthesis case we derive low-dimensional closed-form expressions for both the BB and EM precoders. For the finite-state model we obtain closed-form BB structures and propose a low-complexity block-coordinate-descent algorithm for EM pattern selection. Analytical bounds and extensive simulations show that the proposed hybrid designs for ER-FAS substantially improve UE positioning accuracy over traditional non-reconfigurable arrays.

  PublisherDOI

• Near-Field RIS-Assisted Localization Under Mutual Coupling

  2025-06-08 · 1 citations

  articleSenior author

  Reconfigurable intelligent surfaces (RISs) have the potential to significantly enhance the performance of integrated sensing and communication (ISAC) systems, particularly in line-of-sight (LoS) blockage scenarios. However, as larger RISs are integrated into ISAC systems, mutual coupling (MC) effects between RIS elements become more pronounced, leading to a substantial degradation in performance, especially for localization applications. In this paper, we first conduct a misspecified and standard Cramér-Rao bound analysis to quantify the impact of MC on localization performance, demonstrating severe degradations in accuracy, especially when MC is ignored. Building on this, we propose a novel joint user equipment localization and RIS MC parameter estimation (JLMC) method in near-field wireless systems. Our two-stage MC-aware approach outperforms classical methods that neglect MC, significantly improving localization accuracy and overall system performance. Simulation results validate the effectiveness and advantages of the proposed method in realistic scenarios.

  PublisherDOI

• Hybrid Codebook Design for Localization Using Electromagnetically Reconfigurable Fluid Antenna System

  ArXiv.org · 2025-08-29

  preprintOpen accessSenior author

  Electromagnetically reconfigurable fluid antenna systems (ER-FAS) introduce additional degrees of freedom in the electromagnetic (EM) domain by dynamically steering per-antenna radiation patterns, thereby enhancing power efficiency in wireless links. Unlike prior works on spatially reconfigurable FAS, which adjust element positions, ER-FAS provides direct control over each element's EM characteristics to realize on-demand beam-pattern shaping. While existing studies have exploited ER-FAS to boost spectral efficiency, this paper explores its application for downlink localization. We consider a multiple-input single-output (MISO) system in which a multi-antenna ER-FAS at the base station serves a single-antenna user equipment (UE). We consider two reconfigurability paradigms: (i) a synthesis model where each antenna generates desired beampatterns from a finite set of EM basis functions, and (ii) a finite-state selection model in which each antenna selects a pattern from a predefined set of patterns. For both paradigms, we formulate the joint baseband (BB) and EM precoder design to minimize the UE position error bound. In the synthesis case we derive low-dimensional closed-form expressions for both the BB and EM precoders. For the finite-state model we obtain closed-form BB structures and propose a low-complexity block-coordinate-descent algorithm for EM pattern selection. Analytical bounds and extensive simulations show that the proposed hybrid designs for ER-FAS substantially improve UE positioning accuracy over traditional non-reconfigurable arrays.

  PublisherOA PDFDOI

• Cognitive Radio for Asymmetric Cellular Downlink with Multi-User MIMO

  2025-10-06

  articleSenior author

  Cognitive radio (CR) is an important technique for improving spectral efficiency, letting a secondary system operate in a wireless spectrum when the primary system does not make use of it. While it has been widely explored over the past 25 years, many common assumptions are not aligned with the realities of 5G networks. In this paper, we consider the CR problem for the following setup: (i) infrastructure-based systems, where downlink transmissions might occur to Receivers (Rxs) whose positions are not, or not exactly, known; (ii) multi-beam antennas at both primary and secondary base stations. We formulate a detailed protocol to determine when secondary transmissions into different beam directions can interfere with primary users at potential locations and create probability-based interference rules. We then analyze the "catastrophic interference" probability and the "missed transmission opportunity" probability, as well as the achievable throughput, as a function of the transmit powers of the primary and secondary base stations and the sensing window of the secondary base station. Results can serve to more realistically assess the spectral efficiency gains in 5G infrastructure-based cognitive systems.

  PublisherDOI

• Delay-Constrained Dynamic Network Control with Multi-Agent Deep Reinforcement Learning

  2025-06-08 · 1 citations

  article

  Timely delivery of delay-sensitive information over dynamic, heterogeneous networks is increasingly essential for a range of interactive applications, such as industrial automation, self-driving vehicles, and augmented reality. However, most existing network control solutions target only average delay performance, falling short of providing strict End-to-End (E2E) peak latency guarantees. This paper addresses the challenge of reliably delivering packets within application-imposed strict deadlines by leveraging recent advancements in Multi-Agent Deep Reinforcement Learning (MA-DRL). After introducing the Delay-Constrained Maximum-Throughput (DCMT) dynamic network control problem, and highlighting the limitations of current solutions, we present a novel MA-DRL network control framework that leverages a centralized routing and distributed scheduling architecture. Within the proposed Multi-Agent Deep Reinforcement Learning (MA-DRL) framework, we present a novel design approach that progressively incorporates critical networking domain insights to refine and optimize the action and state spaces of agents.Building on this approach and leveraging the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) technique, we design novel and highly efficient strategies in which centralized routing agents and distributed scheduling agents work in tandem to dynamically assign paths and manage packet transmissions based on their lifetimes, thereby optimizing timely delivery. Numerical results show that this framework significantly outperforms stochastic optimization-based approaches under high-congestion scenarios, showcasing the potential of MADDPG techniques for advanced network control.

  PublisherDOI

• Revenue Optimization in Wireless Video Caching Networks: A Privacy-Preserving Two-Stage Solution

  ArXiv.org · 2025-08-03

  preprintOpen accessSenior author

  Video caching can significantly improve delivery efficiency and enhance quality of video streaming, which constitutes the majority of wireless communication traffic. Due to limited cache size, caching strategies must be designed to adapt to and dynamic user demand in order to maximize system revenue. The system revenue depends on the benefits of delivering the requested videos and costs for (a) transporting the files to the users and (b) cache replacement. Since the cache content at any point in time impacts the replacement costs in the future, demand predictions over multiple cache placement slots become an important prerequisite for efficient cache planning. Motivated by this, we introduce a novel two-stage privacy-preserving solution for revenue optimization in wireless video caching networks. First, we train a Transformer using privacy-preserving federated learning (FL) to predict multi-slot future demands. Given that prediction results are never entirely accurate, especially for longer horizons, we further combine global content popularity with per-user prediction results to estimate the content demand distribution. Then, in the second stage, we leverage these estimation results to find caching strategies that maximize the long-term system revenue. This latter problem takes on the form of a multi-stage knapsack problem, which we then transform to a integer linear program. Our extensive simulation results demonstrate that (i) our FL solution delivers nearly identical performance to that of the ideal centralized solution and outperforms other existing caching methods, and (ii) our novel revenue optimization approach provides deeper system performance insights than traditional cache hit ratio (CHR)-based optimization approaches.

  PublisherOA PDFDOI

Recent grants

• WiFiUS: Device-to-Device Communications at Millimeter-Wave Frequencies

  NSF · $280k · 2015–2019

• RINGS: Resilient Delivery of Real-Time Interactive Services Over NextG Compute-Dense Mobile Networks

  NSF · $900k · 2022–2027

• CIF Small: Massive MIMO in the MM-Wave Range: The Theory of Making it Practical

  NSF · $496k · 2016–2021

• CIF: Small: Communication Theory Foundations and System Optimization of Femto-Caching and D2D Networks for Wireless Video

  NSF · $274k · 2014–2019

• NeTS: Small: Optimal Delivery of Augmented Information Services Over Next-Generation Cloud Networks

  NSF · $500k · 2018–2022

Frequent coauthors

• Fredrik Tufvesson

  162 shared

• Seun Sangodoyin

  Georgia Institute of Technology

  75 shared

• Neelesh B. Mehta

  Indian Institute of Science Bangalore

  72 shared

• Rui Wang

  67 shared

• Moe Z. Win

  Decision Systems (United States)

  66 shared

• Johan Kåredal

  Lund University

  63 shared

• Thomas Zemen

  53 shared

• Giuseppe Caire

  Technische Universität Berlin

  53 shared

Education

• Ph.D., Electrical Engineering

  University of California, Los Angeles

  1990

• M.S., Electrical Engineering

  University of California, Los Angeles

  1985

• B.S., Electrical Engineering

  University of Belgrade

  1980

Awards & honors

• 2018 IEEE Communications Society Edwin H Armstrong Achieveme…
• 2017 IEEE Communications Society IEEE Distinguished Lecturer…
• 2017 IET Achievement Medal for contributions to wireless com…
• 2015 Fellow of the National Academy of Inventors
• 2015 USC Viterbi Viterbi Award for Use-Inspired Research