arXiv subjects

Aydin Sezgin

Publications and source records attributed to Aydin Sezgin.

At least 73 records · Page 4Linked to original sources

arXiv2023

Maximization of minimum rate in MIMO OFDM RIS-assisted Broadcast Channels

Reconfigurable intelligent surface (RIS) is a promising technology to enhance the spectral efficiency of wireless communication systems. By optimizing the RIS elements, the performance of the overall system can be improved. Yet, in contrast to single-carrier systems, in multi-carrier systems, it is not possible to independently optimize RIS elements at each sub-carrier, which may reduce the benefits of RIS in multi-user orthogonal frequency division multiplexing (OFDM) systems. To this end, we investigate the effectiveness of RIS in multiple-input, multiple-output (MIMO) OFDM broadcast channels (BC). We formulate and solve a joint precoding and RIS optimization problem. We show that RIS can significantly improve the system performance even when the number of RIS elements per sub-band is very low.

cs.IT↗

arXiv2023

Open Source Reconfigurable Intelligent Surface for the Frequency Range of 5 GHz WiFi

Reconfigurable Intelligent Surfaces (RIS) have been identified as a potential ingredient to enhance the performance of contemporary wireless communication and sensing systems. Yet, most of the existing devices are either costly or not available for reproduction. To close this gap, a Reconfigurable Intelligent Surface for the frequency range of 5 GHz WiFi is presented in this work. We describe the designed unit cell, which is optimized for the full frequency range of 5.15 to 5.875 GHz. Standard FR4 substrate is used for cost optimization. The measured reflection coefficient of a rectangular RIS prototype with 256 elements is used for RF performance evaluation. Fabrication data and firmware source code are made open source, which makes RIS more available in real measurement setups.

cs.NI↗

arXiv2023

Resilient Sparse Array Radar with the Aid of Deep Learning

In this paper, we address the problem of direction of arrival (DOA) estimation for multiple targets in the presence of sensor failures in a sparse array. Generally, sparse arrays are known with very high-resolution capabilities, where N physical sensors can resolve up to $\mathcal{O}(N^2)$ uncorrelated sources. However, among the many configurations introduced in the literature, the arrays that provide the largest hole-free co-array are the most susceptible to sensor failures. We propose here two machine learning (ML) methods to mitigate the effect of sensor failures and maintain the DOA estimation performance and resolution. The first method enhances the conventional spatial smoothing using deep neural network (DNN), while the second one is an end-to-end data-driven method. Numerical results show that both approaches can significantly improve the performance of MRA with two failed sensors. The data-driven method can maintain the performance of the array with no failures at high signal-tonoise ratio (SNR). Moreover, both approaches can even perform better than the original array at low SNR thanks to the denoising effect of the proposed DNN

cs.LG↗

arXiv2023

The Perfect Match: RIS-enabled MIMO Channel Estimation Using Tensor Decomposition

The deployment of reconfigurable intelligent surfaces (RISs) in a communication system provides control over the propagation environment, which facilitates the augmentation of a multitude of communication objectives. As these performance gains are highly dependent on the applied phase shifts at the RIS, accurate channel state information at the transceivers is imperative. However, not only do RISs traditionally lack signal processing capabilities, but their end-to-end channels also consist of multiple components. Hence, conventional channel estimation (CE) algorithms become incompatible with RIS-aided communication systems as they fail to provide the necessary information about the channel components, which are essential for a beneficial RIS configuration. To enable the full potential of RISs, we propose to use tensor-decomposition-based CE, which facilitates smart configuration of the RIS by providing the required channel components. We use canonical polyadic (CP) decomposition, that exploits a structured time domain pilot sequence. Compared to other state-of-the-art decomposition methods, the proposed Semi-Algebraic CP decomposition via Simultaneous Matrix Diagonalization (SECSI) algorithm is more time efficient as it does not require an iterative process. The benefits of SECSI for RIS-aided networks are validated with numerical results, which show the improved individual and end-to-end CE accuracy of SECSI.

eess.SP↗

arXiv2023

Integrated Communication and Control Systems: A Data Significance Perspective

The interconnected smart devices and industrial internet of things devices require low-latency communication to fulfill control objectives despite limited resources. In essence, such devices have a time-critical nature but also require a highly accurate data input based on its significance. In this paper, we investigate various coordinated and distributed semantic scheduling schemes with a data significance perspective. In particular, novel algorithms are proposed to analyze the benefit of such schemes for the significance in terms of estimation accuracy. Then, we derive the bounds of the achievable estimation accuracy. Our numerical results showcase the superiority of semantic scheduling policies that adopt an integrated control and communication strategy. In essence, such policies can reduce the weighted sum of mean squared errors compared to traditional policies.

cs.IT↗

arXiv2022

Rate-Splitting Enabled Multi-Connectivity in Mixed-Criticality Systems

The enormous quality of service (QoS) demands posed by mission-critical use-cases of future 5G/6G wireless communication raise the need for resource-efficient highly reliable and low latency connectivity solutions. Multi-connectivity is considered a promising yet resource demanding approach to enhance reliability. In this work, we study the potential of the rate-splitting multiple access (RSMA) framework as an efficient way to enable uplink multi-connectivity for data transmissions with particularly high reliability requirements. Mapping high-criticality data onto the common stream allows it to be decoded at multiple access points (APs), which enhances reliability, while the private stream is utilized to serve applications with less stringent requirements. We propose a criticality-aware RSMA-based transmission scheme with short blocklength coding and derive an iterative power allocation algorithm by means of successive convex approximation (SCA). The proposed scheme is shown to achieve an expanded stability rate region compared to two baseline schemes. Moreover, it turns out to be less impacted by short blocklength while leading to substantial rate gains, particularly in the high SNR regime.

cs.IT↗

arXiv2022

IRS-Assistance with Outdated CSI: Element subset selection for secrecy performance enhancement

In this work, we investigate the secrecy performance in an intelligent reflecting surface (IRS)-assisted downlink system. In particular, we consider a base station (BS)-side IRS and as such, the BS-IRS channel is assumed to be known perfectly. Of more importance, we consider the case, in which only outdated channel state information (CSI) of the IRS-user channel is available. We study the impact of outdated CSI on the secrecy performance numerically and analytically. Furthermore, we propose an element subset selection (ESS) method in order to improve the secrecy performance. A key observation is that minimal secrecy outage probability (SOP) can be achieved using a subset of the IRS, and the optimal number of selected reflecting elements can be effectively found by closed-form expressions.

cs.IT↗

arXiv2022

Optimizing the Age of Information in Mixed-Critical Wireless Communication Networks

Beyond fifth generation wireless communication networks (B5G) are applied in many use-cases, such as industrial control systems, smart public transport, and power grids. Those applications require innovative techniques for timely transmission and increased wireless network capacities. Hence, this paper proposes optimizing the data freshness measured by the age of information (AoI) in dense internet of things (IoT) sensor-actuator networks. Given different priorities of data-streams, i.e., different sensitivities to outdated information, mixed-criticality is introduced by analyzing different functions of the age, i.e., we consider linear and exponential aging functions. An intricate non-convex optimization problem managing the physical transmission time and packet outage probability is derived. Such problem is tackled using stochastic reformulations, successive convex approximations, and fractional programming, resulting in an efficient iterative algorithm for AoI optimization. Simulation results validate the proposed scheme's performance in terms of AoI, mixed-criticality, and scalability. The proposed non-orthogonal transmission is shown to outperform an orthogonal access scheme in various deployment cases. Results emphasize the potential gains for dense B5G empowered IoT networks in minimizing the AoI.

cs.IT↗

arXiv2022

RIS-enhanced Resilience in Cell-Free MIMO

More and more applications that require high reliability and fault tolerance are realized with wireless network architectures and thus ultimately rely on the wireless channels, which can be subject to impairments and blockages. Hence, these architectures require a backup plan in the physical layer in order to guarantee functionality, especially when safety-relevant aspects are involved. To this end, this work proposes to utilize the reconfigurable intelligent surface (RIS) as a resilience mechanism to counteract outages. The advantages of RISs for such a purpose derive from their inherent addition of alternative channel links in combination with their reconfigurability. The major benefits are investigated in a cell-free multiple-input and multiple-output (MIMO) setting, in which the direct channel paths are subject to blockages. An optimization problem is formulated that includes rate allocation with beamforming and phase shift configuration and is solved with a resilience-aware alternating optimization approach. Numerical results show that deploying even a randomly-configured RIS to a network reduces the performance degradation caused by blockages. This becomes even more pronounced in the optimized case, in which the RIS is able to potentially counteract the performance degradation entirely. Interestingly, adding more reflecting elements to the system brings an overall benefit for the resilience, even for time-sensitive systems, due to the contribution of the RIS reflections, even when unoptimized.

eess.SY↗

arXiv2022

Joint Communication and Computation in Hybrid Cloud/Mobile Edge Computing Networks

Facing a vast amount of connections, huge performance demands, and the need for reliable connectivity, the sixth generation of communication networks (6G) is envisioned to implement disruptive technologies that jointly spur connectivity, performance, and reliability. In this context, this paper proposes, and evaluates the benefit of, a hybrid central cloud (CC) computing and mobile edge computing (MEC) platform, especially introduced to balance the network resources required for joint computation and communication. Consider a hybrid cloud and MEC system, where several power-hungry multi-antenna unmanned aerial vehicles (UAVs) are deployed at the cell-edge to boost the CC connectivity and relieve part of its computation burden. While the multi-antenna base stations are connected to the cloud via capacity-limited fronthaul links, the UAVs serve the cell-edge users with limited power and computational capabilities. The paper then considers the problem of maximizing the weighted network sum-rate subject to per-user delay, computational capacity, and power constraints, so as to determine the beamforming vectors and computation allocations. Such intricate non-convex optimization problem is tackled using an iterative algorithm that relies on $\ell_0$-norm relaxation, successive convex approximation, and fractional programming, and has the compelling ability to be implemented in a distributed fashion across the multiple UAVs and the CC. The paper results illustrate the numerical prospects of the proposed algorithm for enabling joint communication and computation, and highlight the appreciable improvements of data processing delays and throughputs as compared to conventional system strategies.

cs.IT↗

arXiv2022

Event-Based Beam Tracking with Dynamic Beamwidth Adaptation in Terahertz (THz) Communications

Terahertz (THz) communication will be a key enabler for next-generation wireless systems. While THz frequency bands provide abundant bandwidth and extremely high data rates, their effective operation is inhibited by short communication ranges and narrow beams, thus, leading to major challenges pertaining to user mobility, beam alignment, and handover. In particular, there is a strong need for novel beam tracking methods that consider the tradeoff between enhancing the received signal strength via increasing beam directivity, and increasing the coverage probability by widening the beam. In this paper, a multi-objective optimization problem is formulated with the goal of jointly maximizing the expected rate and minimizing the outage probability subject to transmit power and overhead constraints. Subsequently, a novel parameterized beamformer with dynamic beamwidth adaptation is proposed. In addition to the precoder, an event-based beam tracking approach is introduced that efficiently prevents outages caused by beam misalignment and dynamic blockage while maintaining a low pilot overhead. Simulation results show that the proposed beamforming scheme improves average rate performance and reduces the amount of outages caused by the brittle THz misalignment process and the particularly severe path loss in the THz band. Moreover, the proposed event-triggered THz channel estimation approach enables connectivity with minimal overhead and reliable communication at THz bands.

cs.IT↗

arXiv2022

Approximation-based Threshold Optimization from Single Antenna to Massive SIMO Authentication

In a wireless sensor network, data from various sensors are gathered to estimate the system-state of the process system. However, adversaries aim at distorting the system-state estimate, for which they may infiltrate sensors or position additional devices in the environment. To authenticate the received process values, the integrity of the measurements from different sensors can be evaluated jointly with the temporal integrity of channel measurements from each sensor. For this purpose, we design a security protocol, in which Kalman filters are used to predict the system-state and the channel-state values, and the received data are authenticated by a hypothesis test. We theoretically analyze the adversarial success probability and the reliability rate obtained in the hypothesis test in two ways, based on a chi-square approximation and on a Gaussian approximation. The two approximations are exact for small and large data vectors, respectively. The Gaussian approximation is suitable for analyzing massive single-input multiple-output (SIMO) setups. To obtain additional insights, the approximation is further adapted for the case of channel hardening, which occurs in massive SIMO fading channels. As adversaries always look for the weakest point of a system, a time-constant security level is required. To provide such a service, the approximations are used to propose time-varying threshold values for the hypothesis test, which approximately attain a constant security level. Numerical results show that a constant security level can only be achieved by a time-varying threshold choice, while a constant threshold value leads to a time-varying security level.

cs.IT↗

arXiv2022

Robust Transceiver Design for IRS-Assisted Cascaded MIMO Systems

{Robust transceiver design against unresolvable system uncertainties is of crucial importance for reliable communication. We consider a MIMO multi-hop system, where the source, the relay, and the destination are equipped with multiple antennas. Further, an intelligent reconfigurable surface (IRS) is established to cancel the RSI as much as possible. The considered decode-and-forward (DF) hybrid relay can operate in either half-duplex or full-duplex mode, and the mode changes adaptively depending on the RSI strength. We investigate a robust transceiver design problem, which maximizes the throughput rate corresponding to the worst-case RSI under a self-interference channel uncertainty bound constraint. To the best of our knowledge, this is the first work that uses the IRS for RSI cancellation in MIMO full-duplex DF relay systems. The yielded problem turns out to be a non-convex optimization problem, where the non-convex objective is optimized over the cone of semidefinite matrices. We propose a closed-from lower bound for the IRS worst case RSI cancellation. Eventually, we show an important result that, for the worst case scenario, IRS can be helpful only if the number of IRS elements are at least as large as the size of the interference channel. Moreover, a novel method based on majorization theory is proposed to find the best response of the transmitters and relay against worst case RSI. Furthermore, we propose a multi-level water-filling algorithm to obtain a locally optimal solution iteratively. Finally, we obtain insights on the optimal antenna allocation at the relay input-frontend and output-frontend, for relay reception and transmission, respectively.

cs.IT↗

arXiv2022

Comeback Kid: Resilience for Mixed-Critical Wireless Network Resource Management

The future sixth generation (6G) of communication systems is envisioned to provide numerous applications in safety-critical contexts, e.g., driverless traffic, modular industry, and smart cities, which require outstanding performance, high reliability and fault tolerance, as well as autonomy. Ensuring criticality awareness for diverse functional safety applications and providing fault tolerance in an autonomous manner are essential for future 6G systems. Therefore, this paper proposes jointly employing the concepts of resilience and mixed criticality. In this work, we conduct physical layer resource management in cloud-based networks under the rate-splitting paradigm, which is a promising factor towards achieving high resilience. We recapitulate the concepts individually, outline a joint metric to measure the criticality-aware resilience, and verify its merits in a case study. We, thereby, formulate a non-convex optimization problem, derive an efficient iterative algorithm, propose four resilience mechanisms differing in quality and time of adaption, and conduct extensive numerical simulations. Towards this end, we propose a highly autonomous rate-splitting-enabled physical layer resource management algorithm for future 6G networks respecting mixed-critical quality of service (QoS) levels and providing high levels of resilience. Results emphasize the considerable improvements of incorporating a mixed criticality-aware resilience strategy under channel outages and strict QoS demands. The rate-splitting paradigm is particularly shown to overcome state-of-the-art interference management techniques, and the resilience and throughput adaption over consecutive outage events reveals the proposed schemes contribution towards enabling future 6G networks.

cs.IT↗

arXiv2022

Contact-less Material Probing with Distributed Sensors: Joint Sensing and Communication Optimization

The utilization of RF signals to probe material properties of objects is of huge interest both in academia as well as industry. To this end, a setup is investigated, in which a transmitter equipped with a two-dimensional multi-antenna array dispatches a signal, which hits objects in the environment and the reflections from the objects are captured by distributed sensors. The received signal at those sensors are then amplified and forwarded to a multiple antenna fusion center, which performs space-time post-processing in order to optimize the information extraction. In this process, optimal design of power allocation per object alongside sensors amplifications is of crucial importance. Here, the power allocation and sensors amplifications is jointly optimized, given maximum-ratio combining (MRC) at the fusion center. We formulate this challenge as a sum-power minimization under per-object SINR constraints, a sum-power constraint at the transmitter and individual power constraints at the sensors. Moreover, the advantage of deploying zero-forcing (ZF) and minimum mean-squared error (MMSE) at the fusion center is discussed. Asymptotic analysis is also provided for the case that large number of sensors are deployed in the sensing environment.

cs.IT↗

arXiv2022

IRShield: A Countermeasure Against Adversarial Physical-Layer Wireless Sensing

Wireless radio channels are known to contain information about the surrounding propagation environment, which can be extracted using established wireless sensing methods. Thus, today's ubiquitous wireless devices are attractive targets for passive eavesdroppers to launch reconnaissance attacks. In particular, by overhearing standard communication signals, eavesdroppers obtain estimations of wireless channels which can give away sensitive information about indoor environments. For instance, by applying simple statistical methods, adversaries can infer human motion from wireless channel observations, allowing to remotely monitor premises of victims. In this work, building on the advent of intelligent reflecting surfaces (IRSs), we propose IRShield as a novel countermeasure against adversarial wireless sensing. IRShield is designed as a plug-and-play privacy-preserving extension to existing wireless networks. At the core of IRShield, we design an IRS configuration algorithm to obfuscate wireless channels. We validate the effectiveness with extensive experimental evaluations. In a state-of-the-art human motion detection attack using off-the-shelf Wi-Fi devices, IRShield lowered detection rates to 5% or less.

cs.CR↗

arXiv2022

Sacrificing CSI for a Greater Good: RIS-enabled Opportunistic Rate Splitting

In reconfigurable intelligent surface (RIS)-assisted systems, the optimization of the phase shifts requires separate acquisition of the channel state information (CSI) for the direct and RIS-assisted channels, posing significant design challenges. In this paper, a novel scheme is proposed, which considers practical limitations like pilot overhead and channel estimation (CE) errors to increase the net performance. More specifically, at the cost of unpredictable interference, a portion of the CSI for the RIS-assisted channels is sacrificed in order to reduce the CE time. By alternating the CSI between coherence blocks and employing rate splitting, it becomes possible to mitigate the interference, thereby compensating the adverse effect of the sacrificed CSI. Numerical simulations validate that the proposed scheme exhibits better performance in terms of achievable net rate, resulting in gains of up to 160% compared non-orthogonal multiple access (NOMA), when CE time and CE errors are considered.

cs.IT↗

arXiv2022

IRS-enabled Breath Tracking with Colocated Commodity WiFi Transceivers

Intelligent reflecting surfaces (IRS) are a key enabler of various new applications in 6G smart radio environments. By utilizing an IRS prototype system, this paper aims to enhance self-interference (SI) cancellation for breath tracking with commodity WiFi devices. SI suppression is a crucial requirement for breath tracking with a single antenna site as the SI severely limits the radio sensing range by shadowing the received signal with its own transmit signal. To this end, we propose to assist SI cancellation by exploiting an IRS to form a suitable cancellation signal in the analog domain. Building upon a 256-element IRS prototype, we present results of breath tracking with IRS-assisted SI cancellation from a practical testbed. We use inexpensive WiFi hardware to extract the Channel State Information (CSI) in the 5~GHz band and analyze the phase change between a colocated transmitter and receiver with added local oscillator (LO) synchronization. We are able to track the breath of a test person regardless of the position in an indoor environment in a room-level range. The presented case study shows promising performance in both capturing the breath frequency as well as the breathing patterns.

eess.SP↗