arXiv subjects

Robert Schober

Publications and source records attributed to Robert Schober.

At least 181 records · Page 10Linked to original sources

arXiv2023

Pulse Shaping for MC via Particle Size

In molecular communication (MC), combining different types of particles at the transmitter is a degree of freedom which can be utilized to improve performance. In this paper, we address the problem of pulse shaping to simplify time synchronization requirements by exploiting and combining the received signal characteristics of particles of different sizes. In particular, we optimize the mixture of particles of different sizes used for transmission in order to support a prescribed detection time period for on-off keying, guaranteeing on average 1) a sufficiently large received signal if a binary one is transmitted, and 2) a low enough received signal if a binary zero is transmitted even in the presence of inter-symbol interference. For illustration, we consider an optimization problem based on a free space diffusion channel model. It is shown that there is a tradeoff between the maximum feasible detection duration and the peak detection value for different particle sizes from the smallest particle size enabling the largest detection duration to the largest particle size minimizing the peak detection value at the expense of a limited detection duration.

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arXiv2023

Globally Optimal Resource Allocation Design for IRS-Assisted Multiuser Networks with Discrete Phase Shifts

Intelligent reflecting surfaces (IRSs) are envisioned as a low-cost solution to achieve high spectral and energy efficiency in future communication systems due to their ability to customize wireless propagation environments. Although resource allocation design for IRS-assisted multiuser wireless communication systems has been exhaustively investigated in the literature, the optimal design and performance of such systems are still not well understood. To fill this gap, in this paper, we study optimal resource allocation for IRS-assisted multiuser multiple-input single-output (MISO) systems. In particular, we jointly optimize the beamforming at the base station (BS) and the discrete IRS phase shifts to minimize the total transmit power. For attaining the globally optimal solution of the formulated non-convex combinatorial optimization problem, we develop a resource allocation algorithm with guaranteed convergence based on Schur's complement and the generalized Bender's decomposition. Our numerical results reveal that the proposed algorithm can significantly reduce the BS transmit power compared to the state-of-the-art suboptimal alternating optimization-based approach, especially for moderate-to-large numbers of IRS elements.

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arXiv2023

Reconfigurable Massive MIMO: Harnessing the Power of the Electromagnetic Domain for Enhanced Information Transfer

The capacity of commercial massive multiple-input multiple-output (mMIMO) systems is constrained by the limited array aperture at the base station, and cannot meet the ever-increasing traffic demands of wireless networks. Given the array aperture, holographic MIMO with infinitesimal antenna spacing can maximize the capacity, but is physically unrealizable. As a promising alternative, reconfigurable mMIMO is proposed to harness the unexploited power of the electromagnetic (EM) domain for enhanced information transfer. Specifically, the reconfigurable pixel antenna technology provides each antenna with an adjustable EM radiation (EMR) pattern, introducing extra degrees of freedom for information transfer in the EM domain. In this article, we present the concept and benefits of availing the EMR domain for mMIMO transmission. Moreover, we propose a viable architecture for reconfigurable mMIMO systems, and the associated system model and downlink precoding are also discussed. In particular, a three-level precoding scheme is proposed, and simulation results verify its considerable spectral and energy efficiency advantages compared to traditional mMIMO systems. Finally, we further discuss the challenges, insights, and prospects of deploying reconfigurable mMIMO, along with the associated hardware, algorithms, and fundamental theory.

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arXiv2023

A Framework for Transmission Design for Active RIS-Aided Communication with Partial CSI

Active reconfigurable intelligent surfaces (RISs) have recently been proposed to compensate for the severe multiplicative fading effect of conventional passive RIS-aided systems. Each reflecting element of active RISs is assisted by an amplifier such that the incident signal can be reflected and amplified instead of only being reflected as in passive RIS-aided systems. This work addresses the practical challenge that, on the one hand, in active RIS-aided systems the perfect individual CSI of the RIS-aided channels cannot be acquired due to the lack of signal processing power at the active RISs, but, on the other hand, this CSI is required to calculate the expected system data rate and RIS transmit power needed for transceiver design. To address this issue, we first derive closed-form expressions for the average achievable rate and the average RIS transmit power based on partial CSI of the RIS-aided channels. Then, we formulate an average achievable rate maximization problem for jointly optimizing the active beamforming at both the base station (BS) and the RIS. This problem is then tackled using the majorization--minimization (MM) algorithm framework, and, for each iteration, semi-closed-form solutions for the BS and RIS beamforming are derived based on the Karush-Kuhn-Tucker (KKT) conditions. To ensure the quality of service (QoS) of each user, we further formulate a rate outage constrained beamforming problem, which is solved using the Bernstein-Type inequality (BTI) and semidefinite relaxation (SDR) techniques. Numerical results show that the proposed algorithms can efficiently overcome the challenges imposed by imperfect CSI in active RIS-aided wireless systems.

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arXiv2023

Active RIS vs. Passive RIS: Which Will Prevail in 6G?

As a revolutionary paradigm for controlling wireless channels, reconfigurable intelligent surfaces (RISs) have emerged as a candidate technology for future 6G networks. However, due to the "multiplicative fading" effect, the existing passive RISs only achieve limited capacity gains in many scenarios with strong direct links. In this paper, the concept of active RISs is proposed to overcome this fundamental limitation. Unlike passive RISs that reflect signals without amplification, active RISs can amplify the reflected signals via amplifiers integrated into their elements. To characterize the signal amplification and incorporate the noise introduced by the active components, we develop and verify the signal model of active RISs through the experimental measurements based on a fabricated active RIS element. Based on the verified signal model, we further analyze the asymptotic performance of active RISs to reveal the substantial capacity gain they provide for wireless communications. Finally, we formulate the sum-rate maximization problem for an active RIS aided multi-user multiple-input single-output (MU-MISO) system and a joint transmit beamforming and reflect precoding scheme is proposed to solve this problem. Simulation results show that, in a typical wireless system, passive RISs can realize only a limited sum-rate gain of 22%, while active RISs can achieve a significant sum-rate gain of 130%, thus overcoming the "multiplicative fading" effect.

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arXiv2023

Impact of Channel Models on Performance Characterization of RIS-Assisted Wireless Systems

The performance characterization of communication systems assisted by large reconfigurable intelligent surfaces (RISs) significantly depends on the adopted models for the underlying channels. Under unrealistic channel models, the system performance may be over- or under-estimated which yields inaccurate conclusions for the system design. In this paper, we review five channel models that are chosen to progressively improve the modeling accuracy for large RISs. For each channel model, we highlight the underlying assumptions, its advantages, and its limitations. We compare the system performance under the aforementioned channel models using RIS configuration algorithms from the literature and a new scalable algorithm proposed in this paper specifically for the configuration of extremely large RISs.

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arXiv2023

Signal Reception With Generic Three-State Receptors in Synaptic MC

Synaptic communication is studied by communication engineers for two main reasons. One is to enable novel neuroengineering applications that require interfacing with neurons. The other reason is to draw inspiration for the design of synthetic molecular communication systems. Both of these goals require understanding of how the chemical synaptic signal is sensed and transduced at the synaptic receiver (Rx). While signal reception in synaptic molecular communication (SMC) depends heavily on the kinetics of the receptors employed by the synaptic Rxs, existing channel models for SMC either oversimplify the receptor kinetics or employ complex, high-dimensional kinetic schemes limited to specific types of receptors. Both approaches do not facilitate a comparative analysis of different types of natural synapses. In this paper, we propose a novel deterministic channel model for SMC which employs a generic three-state receptor model that captures the characteristics of the most important receptor types in SMC. The model is based on a transfer function expansion of Fick's diffusion equation and accounts for release, diffusion, and degradation of neurotransmitters as well as their reversible binding to finitely many generic postsynaptic receptors. The proposed SMC model is the first that allows studying the impact of the characteristic dynamics of the main postsynaptic receptor types on synaptic signal transmission. Numerical results indicate that the proposed model indeed exhibits a wide range of biologically plausible dynamics when specialized to specific natural receptor types.

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arXiv2023

Molecular Noise In Synaptic Communication

In synaptic molecular communication (MC), the activation of postsynaptic receptors by neurotransmitters (NTs) is governed by a stochastic reaction-diffusion process. This randomness of synaptic MC contributes to the randomness of the electrochemical downstream signal in the postsynaptic cell, called postsynaptic membrane potential (PSP). Since the randomness of the PSP is relevant for neural computation and learning, characterizing the statistics of the PSP is critical. However, the statistical characterization of the synaptic reaction-diffusion process is difficult because the reversible bi-molecular reaction of NTs with receptors renders the system nonlinear. Consequently, there is currently no model available which characterizes the impact of the statistics of postsynaptic receptor activation on the PSP. In this work, we propose a novel statistical model for the synaptic reaction-diffusion process in terms of the chemical master equation (CME). We further propose a novel numerical method which allows to compute the CME efficiently and we use this method to characterize the statistics of the PSP. Finally, we present results from stochastic particle-based computer simulations which validate the proposed models. We show that the biophysical parameters governing synaptic transmission shape the autocovariance of the receptor activation and, ultimately, the statistics of the PSP. Our results suggest that the processing of the synaptic signal by the postsynaptic cell effectively mitigates synaptic noise while the statistical characteristics of the synaptic signal are preserved. The results presented in this paper contribute to a better understanding of the impact of the randomness of synaptic signal transmission on neuronal information processing.

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arXiv2023

A Chemical Master Equation Model for Synaptic Molecular Communication

In synaptic molecular communication, the activation of postsynaptic receptors by neurotransmitters (NTs) is governed by a stochastic reaction-diffusion process and, hence, inherently random. It is currently not fully understood how this randomness impacts downstream signaling in the target cell and, ultimately, neural computation and learning. The statistical characterization of the reaction-diffusion process is difficult because the reversible bi-molecular reaction of NTs and receptors renders the system nonlinear. Consequently, existing models for the receptor occupancy in the synaptic cleft rely on simplifying assumptions and approximations which limit their practical applicability. In this work, we propose a novel statistical model for the reaction-diffusion process governing synaptic signal transmission in terms of the chemical master equation (CME). We show how to compute the CME efficiently and verify the accuracy of the obtained results with stochastic particle-based computer simulations (PBSs). Furthermore, we compare the proposed model to two benchmark models proposed in the literature and show that it provides more accurate results when compared to PBSs. Finally, the proposed model is used to study the impact of the system parameters on the statistical dependence between binding events of NTs and receptors. In summary, the proposed model provides a step forward towards a complete statistical characterization of synaptic signal transmission.

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arXiv2023

Statistical Modeling of Airborne Virus Transmission Through Imperfectly Fitted Face Masks

The rapid emergence and the disastrous impact of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic on public health, societies, and economies around the world has created an urgent need for understanding the pathways critical for virus transmission. Airborne virus transmission by asymptomatic SARS-CoV-2-infected individuals is considered to be a major contributor to the spread of SARS-CoV-2 and social distancing and wearing of face masks in public have been implemented as countermeasures in many countries. However, a comprehensive risk assessment framework for the airborne transmission of SARS-CoV-2 incorporating realistic assumptions on the filtration of infectious aerosols (IAs) by face masks is not available yet. In particular, in most end-to-end models for airborne virus transmission, it is neglected that the stochastic spread of IAs through imperfectly fitted face masks depends on the dynamics of the breathing of the wearer. In this paper, we consider airborne virus transmission from an infected but asymptomatic person to a healthy person, both wearing imperfectly fitted face masks, in an indoor environment. By framing the end-to-end virus transmission as a Molecular Communications (MC) system, we obtain a statistical description of the number of IAs inhaled by the healthy person subject to the respective configurations of the face masks of both persons. We demonstrate that the exhalation and inhalation air flow dynamics have a significant impact on the stochastic filtering of IAs by the face masks. Furthermore, we show that the fit of the face mask of the infected person can highly impact the infection probability. We conclude that the proposed MC model may contribute a valuable assessment tool to fight the spread of SARS-CoV-2 as it encompasses the randomness of the transmission process and enables comprehensive risk analysis beyond statistical averages.

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arXiv2022

Active RISs: Signal Modeling, Asymptotic Analysis, and Beamforming Design

Reconfigurable intelligent surfaces (RISs) have emerged as a candidate technology for future 6G networks. However, due to the "multiplicative fading" effect, the existing passive RISs only achieve a negligible capacity gain in environments with strong direct links. In this paper, the concept of active RISs is studied to overcome this fundamental limitation. Unlike the existing passive RISs that reflect signals without amplification, active RISs can amplify the reflected signals via amplifiers integrated into their elements. To characterize the signal amplification and incorporate the noise introduced by the active components, we verify the signal model of active RISs through the experimental measurements on a fabricated active RIS element. Based on the verified signal model, we formulate the sum-rate maximization problem for an active RIS aided multi-user multiple-input single-output (MU-MISO) system and a joint transmit precoding and reflect beamforming algorithm is proposed to solve this problem. Simulation results show that, in a typical wireless system, the existing passive RISs can realize only a negligible sum-rate gain of 3%, while the active RISs can achieve a significant sum-rate gain of 62%, thus overcoming the "multiplicative fading" effect. Finally, we develop a 64-element active RIS aided wireless communication prototype, and the significant gain of active RISs is validated by field test.

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arXiv2022

Accelerating Distributed Optimization via Over-the-Air Computing

Distributed optimization is ubiquitous in emerging applications, such as robust sensor network control, smart grid management, machine learning, resource slicing, and localization. However, the extensive data exchange among local and central nodes may cause a severe communication bottleneck. To overcome this challenge, over-the-air computing (AirComp) is a promising medium access technology, which exploits the superposition property of the wireless multiple access channel (MAC) and offers significant bandwidth savings. In this work, we propose an AirComp framework for general distributed convex optimization problems. Specifically, a distributed primaldual (DPD) subgradient method is utilized for the optimization procedure. Under general assumptions, we prove that DPDAirComp can asymptotically achieve zero expected constraint violation. Therefore, DPD-AirComp ensures the feasibility of the original problem, despite the presence of channel fading and additive noise. Moreover, with proper power control of the users' signals, the expected non-zero optimality gap can also be mitigated. Two practical applications of the proposed framework are presented, namely, smart grid management and wireless resource allocation. Finally, numerical results reconfirm DPDAirComp's excellent performance, while it is also shown that DPD-AirComp converges an order of magnitude faster compared to a digital orthogonal multiple access scheme, specifically, time division multiple access (TDMA).

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arXiv2022

The Internet of Bio-Nano Things in Blood Vessels: System Design and Prototypes

In this paper, we investigate the Internet of Bio-Nano Things (IoBNT) which relates to networks formed by molecular communications. By providing a means of communication through the ubiquitously connected blood vessels (arteries, veins, and capillaries), molecular communication-based IoBNT enables a host of new eHealth applications. For example, an organ monitoring sensor can transfer internal body signals through the IoBNT for health monitoring applications. We empirically show that blood vessel channels introduce a new set of challenges for the design of molecular communication systems in comparison to free-space channels. We then propose cylindrical duct channel models and discuss the corresponding system designs conforming to the channel characteristics. Furthermore, based on prototype implementations, we confirm that molecular communication techniques can be utilized for composing the IoBNT. We believe that the promising results presented in this work, together with the rich research challenges that lie ahead, are strong indicators that IoBNT with molecular communications can drive novel applications for emerging eHealth systems.

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arXiv2022

Deterministic Identification For MC ISI-Poisson Channel

Several applications of molecular communications (MC) feature an alarm-prompt behavior for which the prevalent Shannon capacity may not be the appropriate performance metric. The identification capacity as an alternative measure for such systems has been motivated and established in the literature. In this paper, we study deterministic identification (DI) for the discrete-time \emph{Poisson} channel (DTPC) with inter-symbol interference (ISI) where the transmitter is restricted to an average and a peak molecule release rate constraint. Such a channel serves as a model for diffusive MC systems featuring long channel impulse responses and employing molecule counting receivers. We derive lower and upper bounds on the DI capacity of the DTPC with ISI when the number of ISI channel taps $K$ may grow with the codeword length $n$ (e.g., due to increasing symbol rate). As a key finding, we establish that for deterministic encoding, the codebook size scales as $2^{(n\log n)R}$ assuming that the number of ISI channel taps scales as $K = 2^{κ\log n}$, where $R$ is the coding rate and $κ$ is the ISI rate. Moreover, we show that optimizing $κ$ leads to an effective identification rate [bits/s] that scales linearly with $n$, which is in contrast to the typical transmission rate [bits/s] that is independent of $n$.

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arXiv2022

Coupled Phase-Shift STAR-RISs: A General Optimization Framework

A general optimization framework is proposed for simultaneously transmitting and reflecting reconfigurable surfaces (STAR-RISs) with coupled phase shifts, which converges to the Karush-Kuhn-Tucker (KKT) optimal solution under some mild conditions. More particularly, the amplitude and phase-shift coefficients of STAR-RISs are optimized alternatively in closed form. To demonstrate the effectiveness of the proposed optimization framework, the throughput maximization problem is considered in a case study. It is rigorously proved that the KKT optimal solution is obtained. Numerical results confirm the effectiveness of the proposed optimization framework compared to baseline schemes.

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arXiv2022

Scalable Predictive Beamforming for IRS-Assisted Multi-User Communications: A Deep Learning Approach

Beamforming design for intelligent reflecting surface (IRS)-assisted multi-user communication (IRS-MUC) systems critically depends on the acquisition of accurate channel state information (CSI). However, channel estimation (CE) in IRS-MUC systems causes a large signaling overhead for training due to the large number of IRS elements. In this paper, taking into account user mobility, we adopt a deep learning (DL) approach to implicitly learn the historical line-of-sight (LoS) channel features and predict the IRS phase shifts to be adopted for the next time slot for maximization of the weighted sum-rate (WSR) of the IRS-MUC system. With the proposed predictive approach, we can avoid full-scale CSI estimation and facilitate low-dimensional CE for transmit beamforming design such that the signaling overhead is reduced by a scale of $\frac{1}{N}$, where $N$ is the number of IRS elements. To this end, we first develop a universal DL-based predictive beamforming (DLPB) framework featuring a two-stage predictive-instantaneous beamforming mechanism. As a realization of the developed framework, a location-aware convolutional long short-term memory (CLSTM) graph neural network (GNN) is developed to facilitate effective predictive beamforming at the IRS, where a CLSTM module is first adopted to exploit the spatial and temporal features of the considered channels and a GNN is then applied to empower the designed neural network with high scalability and generalizability. Furthermore, in the second stage, based on the predicted IRS phase shifts, an instantaneous CSI-aware fully-connected neural network is designed to optimize the transmit beamforming at the access point. Simulation results demonstrate that the proposed framework not only achieves a better WSR performance and requires a lower CE overhead compared with state-of-the-art benchmarks, but also is highly scalable in the numbers of users.

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arXiv2022

Codebook-Based User Tracking in IRS-Assisted mmWave Communication Networks

In this paper, we present a novel mobile user tracking (UT) scheme for codebook-based intelligent reflecting surface (IRS) aided millimeter wave (mmWave) systems. The proposed UT scheme exploits the temporal correlation of the direction from the IRS to the mobile user for selecting IRS phase shifts that provide reflection towards the user. To this end, the user's direction is periodically estimated based on a generalized likelihood ratio test (GLRT) and the user's movement trajectory is extrapolated from several past direction estimates. The efficiency of the proposed UT scheme is evaluated in terms of the average effective rate, which accounts for both the required signaling overhead and the achieved signal to noise ratio (SNR). Our results show that for medium to high SNR, the proposed codebook based UT scheme achieves a higher effective rate than two reference approaches based on full codebook search and optimization of the individual IRS unit cells, respectively.

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arXiv2022

Intelligent Reflecting Surface Enabled Multi-Target Sensing

Besides improving communication performance, intelligent reflecting surfaces (IRSs) are also promising enablers for achieving larger sensing coverage and enhanced sensing quality. Nevertheless, in the absence of a direct path between the base station (BS) and the targets, multi-target sensing is generally very difficult, since IRSs are incapable of proactively transmitting sensing beams or analyzing target information. Moreover, the echoes of different targets reflected via the IRS-established virtual links share the same directionality at the BS. In this paper, we study a wireless system comprising a multi-antenna BS and an IRS for multi-target sensing, where the beamforming vector and the IRS phase shifts are jointly optimized to improve the sensing performance. To meet the different sensing requirements, such as a minimum received power and a minimum sensing frequency, we propose three novel IRS-assisted sensing schemes: Time division (TD) sensing, signature sequence (SS) sensing, and hybrid TD-SS sensing. First, for TD sensing, the sensing tasks are performed in sequence over time. Subsequently, a novel signature sequence (SS) sensing scheme is proposed to improve sensing efficiency by establishing a relationship between directions and SSs. To strike a flexible balance between the beam pattern gain and sensing efficiency, we also propose a general hybrid TD-SS sensing scheme with target grouping, where targets belonging to the same group are sensed simultaneously via SS sensing, while the targets in different groups are assigned to orthogonal time slots. By controlling the number of groups, the hybrid TD-SS sensing scheme can provide a more flexible balance between beam pattern gain and sensing frequency. Moreover, ...

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