arXiv subjects

Lajos Hanzo

Publications and source records attributed to Lajos Hanzo.

At least 181 records · Page 10Linked to original sources

arXiv2023

Joint Beamforming Design for Dual-Functional MIMO Radar and Communication Systems Guaranteeing Physical Layer Security

The dual-functional radar and communication (DFRC) technique constitutes a promising next-generation wireless solution, due to its benefits in terms of power consumption, physical hardware, and spectrum exploitation. In this paper, we propose sophisticated beamforming designs for multi-user DFRC systems by additionally taking the physical layer security (PLS) into account. We show that appropriately designed radar waveforms can also act as the traditional artificial noise conceived for drowning out the eavesdropping channel and for attaining increased design degrees of freedom (DoF). The joint beamforming design is formulated as a non-convex optimization problem for striking a compelling trade-off amongst the conflicting design objectives of radar transmit beampattern, communication quality of service (QoS), and the PLS level. Then, we propose a semidefinite relaxation (SDR)-based algorithm and a reduced-complexity version to tackle the non-convexity, where the globally optimal solutions are found. Moreover, a robust beamforming method is also developed for considering realistic imperfect channel state information (CSI) knowledge. Finally, simulation results are provided for corroborating our theoretical results and show the proposed methods' superiority.

eess.SP↗

arXiv2022

Robust Linear Hybrid Beamforming Designs Relying on Imperfect CSI in mmWave MIMO IoT Networks

Linear hybrid beamformer designs are conceived for the decentralized estimation of a vector parameter in a millimeter wave (mmWave) multiple-input multiple-output (MIMO) Internet of Things network (IoTNe). The proposed designs incorporate both total IoTNe and individual IoTNo power constraints, while also eliminating the need for a baseband receiver combiner at the fusion center (FC). To circumvent the non-convexity of the hybrid beamformer design problem, the proposed approach initially determines the minimum mean square error (MMSE) digital transmit precoder (TPC) weights followed by a simultaneous orthogonal matching pursuit (SOMP)-based framework for obtaining the analog RF and digital baseband TPCs. Robust hybrid beamformers are also derived for the realistic imperfect channel state information (CSI) scenario, utilizing both the stochastic and norm-ball CSI uncertainty frameworks. The centralized MMSE bound derived in this work serves as a lower bound for the estimation performance of the proposed hybrid TPC designs. Finally, our simulation results quantify the benefits of the various designs developed.

eess.SP↗

arXiv2022

The Internet of Senses: Building on Semantic Communications and Edge Intelligence

The Internet of Senses (IoS) holds the promise of flawless telepresence-style communication for all human `receptors' and therefore blurs the difference of virtual and real environments. We commence by highlighting the compelling use cases empowered by the IoS and also the key network requirements. We then elaborate on how the emerging semantic communications and Artificial Intelligence (AI)/Machine Learning (ML) paradigms along with 6G technologies may satisfy the requirements of IoS use cases. On one hand, semantic communications can be applied for extracting meaningful and significant information and hence efficiently exploit the resources and for harnessing a priori information at the receiver to satisfy IoS requirements. On the other hand, AI/ML facilitates frugal network resource management by making use of the enormous amount of data generated in IoS edge nodes and devices, as well as by optimizing the IoS performance via intelligent agents. However, the intelligent agents deployed at the edge are not completely aware of each others' decisions and the environments of each other, hence they operate in a partially rather than fully observable environment. Therefore, we present a case study of Partially Observable Markov Decision Processes (POMDP) for improving the User Equipment (UE) throughput and energy consumption, as they are imperative for IoS use cases, using Reinforcement Learning for astutely activating and deactivating the component carriers in carrier aggregation. Finally, we outline the challenges and open issues of IoS implementations and employing semantic communications, edge intelligence as well as learning under partial observability in the IoS context.

cs.AI↗

arXiv2022

Space-Terrestrial Cooperation Over Spatially Correlated Channels Relying on Imperfect Channel Estimates: Uplink Performance Analysis and Optimization

A whole suite of innovative technologies and architectures have emerged in response to the rapid growth of wireless traffic. This paper studies an integrated network design that boosts system capacity through cooperation between wireless access points (APs) and a satellite for enhancing the network's spectral efficiency. We first mathematically derive an achievable throughput expression for the uplink (UL) data transmission over spatially correlated Rician channels. Our generic achievable throughput expression is applicable for arbitrary received signal detection techniques under realistic imperfect channel estimates. A closed-form expression is then obtained for the ergodic UL data throughput when maximum ratio combining is utilized for detecting the desired signals. As for our resource allocation contributions, we formulate the max-min fairness and total transmit power optimization problems relying on the channel statistics for performing power allocation. The solution of each optimization problem is derived in form of a low-complexity iterative design, in which each data power variable is updated relying on a closed-form expression. Our integrated hybrid network concept allows users to be served that may not otherwise be accommodated due to the excessive data demands. The algorithms proposed to allow us to address the congestion issues appearing when at least one user is served at a rate below the target. The mathematical analysis is also illustrated with the aid of our numerical results that show the added benefits of considering the space links in terms of improving the ergodic data throughput. Furthermore, the proposed algorithms smoothly circumvent any potential congestion, especially in face of high rate requirements and weak channel conditions.

cs.IT↗

arXiv2022

Cooperative Hybrid Transmit Beamforming in Cell-free mmWave MIMO Networks

Hybrid precoders and combiners are designed for cooperative cell-free multi-user millimeter wave (mmWave) multiple-input multiple-output (MIMO) cellular networks for low complexity interference mitigation. Initially, we derive an optimal hybrid transmit beamformer (HTBF) for a broadcast scenario considering both total and per access point (AP) power constraints. Next, an optimal successive hybrid beamformer technique is proposed for unicast and multicast scenarios which relies on the optimal minimum variance distortionless response (MVDR). We demonstrate that it mitigates both the interuser and intergroup interference, while successively ensuring orthogonality to the previously scheduled users/user groups. Furthermore, it is shown theoretically that the proposed schemes are capable of supporting a large number of users. Subsequently, a Bayesian learning (BL) based method is conceived for jointly designing the RF and baseband precoders/combiners for the various scenarios considered. Furthermore, we also conceive the uplink counterpart of our HTBF scheme, which is based on maximizing the signal-tointerference-plus noise ratio (SINR) of each individual user. Finally, the efficacy of the proposed schemes is characterized by our extensive simulation results in terms of cancelling the interuser/intergroup interference, which improves the spectral efficiency.

eess.SP↗

arXiv2022

Efficient Rate-Splitting Multiple Access for the Internet of Vehicles: Federated Edge Learning and Latency Minimization

Rate-Splitting Multiple Access (RSMA) has recently found favour in the multi-antenna-aided wireless downlink, as a benefit of relaxing the accuracy of Channel State Information at the Transmitter (CSIT), while in achieving high spectral efficiency and providing security guarantees. These benefits are particularly important in high-velocity vehicular platoons since their high Doppler affects the estimation accuracy of the CSIT. To tackle this challenge, we propose an RSMA-based Internet of Vehicles (IoV) solution that jointly considers platoon control and FEderated Edge Learning (FEEL) in the downlink. Specifically, the proposed framework is designed for transmitting the unicast control messages within the IoV platoon, as well as for privacy-preserving FEEL-aided downlink Non-Orthogonal Unicasting and Multicasting (NOUM). Given this sophisticated framework, a multi-objective optimization problem is formulated to minimize both the latency of the FEEL downlink and the deviation of the vehicles within the platoon. To efficiently solve this problem, a Block Coordinate Descent (BCD) framework is developed for decoupling the main multi-objective problem into two sub-problems. Then, for solving these non-convex sub-problems, a Successive Convex Approximation (SCA) and Model Predictive Control (MPC) method is developed for solving the FEEL-based downlink problem and platoon control problem, respectively. Our simulation results show that the proposed RSMA-based IoV system outperforms the conventional systems.

cs.NI↗

arXiv2022

Single-photon-memory measurement-device-independent quantum secure direct communication

Quantum secure direct communication (QSDC) uses the quantum channel to transmit information reliably and securely. In order to eliminate the security loopholes resulting from practical detectors, the measurement-device-independent (MDI) QSDC protocol has been proposed. However, block-based transmission of quantum states is utilized in MDI-QSDC, which requires practical quantum memory that is still unavailable at the time of writing. For circumventing this impediment, we propose a single-photon-memory MDI QSDC protocol (SPMQC) for dispensing with high-performance quantum memory. The performance of the proposed protocol is characterized by simulations considering realistic experimental parameters, and the results show that it is feasible to implement SPMQC by relying on present-day technology.

quant-ph↗

arXiv2022

Secrecy Rate Maximization of RIS-assisted SWIPT Systems: A Two-Timescale Beamforming Design Approach

Reconfigurable intelligent surfaces (RISs) achieve high passive beamforming gains for signal enhancement or interference nulling by dynamically adjusting their reflection coefficients. Their employment is particularly appealing for improving both the wireless security and the efficiency of radio frequency (RF)-based wireless power transfer. Motivated by this, we conceive and investigate a RIS-assisted secure simultaneous wireless information and power transfer (SWIPT) system designed for information and power transfer from a base station (BS) to an information user (IU) and to multiple energy users (EUs), respectively. Moreover, the EUs are also potential eavesdroppers that may overhear the communication between the BS and IU. We adopt two-timescale transmission for reducing the signal processing complexity as well as channel training overhead, and aim for maximizing the average worst-case secrecy rate achieved by the IU. This is achieved by jointly optimizing the short-term transmit beamforming vectors at the BS as well as the long-term phase shifts at the RIS, under the energy harvesting constraints considered at the EUs and the power constraint at the BS. The stochastic optimization problem formulated is non-convex with intricately coupled variables, and is non-smooth due to the existence of multiple EUs/eavesdroppers. No standard optimization approach is available for this challenging scenario. To tackle this challenge, we propose a smooth approximation aided stochastic successive convex approximation (SA-SSCA) algorithm. Furthermore, a low-complexity heuristic algorithm is proposed for reducing the computational complexity without unduly eroding the performance. Simulation results show the efficiency of the RIS in securing SWIPT systems. The significant performance gains achieved by our proposed algorithms over the relevant benchmark schemes are also demonstrated.

cs.IT↗

arXiv2022

Codebook-Based Solutions for Reconfigurable Intelligent Surfaces and Their Open Challenges

Reconfigurable intelligent surfaces (RIS) is a revolutionary technology to cost-effectively improve the performance of wireless networks. We first review the existing framework of channel estimation and passive beamforming (CE & PBF) in RIS-assisted communication systems. To reduce the excessive pilot signaling overhead and implementation complexity of the CE & PBF framework, we conceive a codebook-based framework to strike flexible tradeoffs between communication performance and signaling overhead. Moreover, we provide useful insights into the codebook design and learning mechanisms of the RIS reflection pattern. Finally, we analyze the scalability of the proposed framework by flexibly adapting the training overhead to the specified quality-of-service requirements and then elaborate on its appealing advantages over the existing CE & PBF approaches. It is shown that our novel codebook-based framework can be beneficially applied to all RIS-assisted scenarios and avoids the curse of model dependency faced by its existing counterparts, thus constituting a competitive solution for practical RIS-assisted communication systems.

cs.IT↗

arXiv2022

Five Facets of 6G: Research Challenges and Opportunities

Whilst the fifth-generation (5G) systems are being rolled out across the globe, researchers have turned their attention to the exploration of radical next-generation solutions. At this early evolutionary stage we survey five main research facets of this field, namely {\em Facet~1: next-generation architectures, spectrum and services, Facet~2: next-generation networking, Facet~3: Internet of Things (IoT), Facet~4: wireless positioning and sensing, as well as Facet~5: applications of deep learning in 6G networks.} In this paper, we have provided a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, applications as well as designs. We have portrayed a plethora of heterogeneous architectures relying on cooperative hybrid networks supported by diverse access and transmission mechanisms. The vulnerabilities of these techniques are also addressed and carefully considered for highlighting the most of promising future research directions. Additionally, we have listed a rich suite of learning-driven optimization techniques. We conclude by observing the evolutionary paradigm-shift that has taken place from pure single-component bandwidth-efficiency, power-efficiency or delay-optimization towards multi-component designs, as exemplified by the twin-component ultra-reliable low-latency mode of the 5G system. We advocate a further evolutionary step towards multi-component Pareto optimization, which requires the exploration of the entire Pareto front of all optiomal solutions, where none of the components of the objective function may be improved without degrading at least one of the other components.

cs.NI↗

arXiv2022

Environment-Aware AUV Trajectory Design and Resource Management for Multi-Tier Underwater Computing

The Internet of underwater things (IoUT) is envisioned to be an essential part of maritime activities. Given the IoUT devices' wide-area distribution and constrained transmit power, autonomous underwater vehicles (AUVs) have been widely adopted for collecting and forwarding the data sensed by IoUT devices to the surface-stations. In order to accommodate the diverse requirements of IoUT applications, it is imperative to conceive a multi-tier underwater computing (MTUC) framework by carefully harnessing both the computing and the communications as well as the storage resources of both the surface-station and of the AUVs as well as of the IoUT devices. Furthermore, to meet the stringent energy constraints of the IoUT devices and to reduce the operating cost of the MTUC framework, a joint environment-aware AUV trajectory design and resource management problem is formulated, which is a high-dimensional NP-hard problem. To tackle this challenge, we first transform the problem into a Markov decision process (MDP) and solve it with the aid of the asynchronous advantage actor-critic (A3C) algorithm. Our simulation results demonstrate the superiority of our scheme.

cs.DC↗

arXiv2022

Two-Timescale Design for Reconfigurable Intelligent Surface-Aided Massive MIMO Systems with Imperfect CSI

This paper investigates the two-timescale transmission design for reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems, where the beamforming at the base station (BS) is adapted to the rapidly-changing instantaneous channel state information (CSI), while the passive beamforming at the RIS is adapted to the slowly-changing statistical CSI. Specifically, we first propose a linear minimum mean square error (LMMSE) estimator to obtain the aggregated channel from the users to the BS in each channel coherence interval. Based on the estimated channel, we apply the low-complexity maximal ratio combining (MRC) beamforming at the BS, and then derive the ergodic achievable rate in a closed form expression. To draw design insights, we perform a detailed theoretical analysis departing from the derived ergodic achievable rate. If the BS-RIS channel is Rician distributed, we prove that the transmit power can be scaled proportionally to $1/M$, as the number of BS antennas, $M$, grows to infinity while maintaining a non-zero rate. If the BS-RIS channel is Rayleigh distributed, the transmit power can be scaled either proportionally to $1/\sqrt{M}$ as $M$ grows large, or proportionally to $1/N$ as the number of reflecting elements, $N$, grows large, while still maintaining a non-zero rate. By capitalizing on the derived expression of the data rate under the statistical knowledge of the CSI, we maximize the minimum user rate by designing the passive beamforming at the RIS. Numerical results confirm that, even in the presence of imperfect CSI, the integration of an RIS in massive MIMO systems results in promising performance gains. In addition, the obtained results reveal that it is favorable to place the RIS close to the users rather than close to the BS.

cs.IT↗

arXiv2022

Secrecy Rate of the Cooperative RSMA-Aided UAV Downlink Relying on Optimal Relay Selection

The Cooperative Rate-Splitting (CRS) scheme, proposed evolves from conventional Rate Splitting (RS) and relies on forwarding a portion of the RS message by the relaying users. In terms of secrecy enhancement, it has been shown that CRS outperforms its non-cooperative counterpart for a two-user Multiple Input Single Output (MISO) Broadcast Channel (BC). Given the massive connectivity requirement of 6G, we have generalized the existing secure two-user CRS framework to the multi-user framework, where the highest-security users must be selected as the relay nodes. This paper addresses the problem of maximizing the Worst-Case Secrecy Rate (WCSR) in a UAV-aided downlink network where a multi-antenna UAV Base-Station (UAV-BS) serves a group of users in the presence of an external eavesdropper (Eve). We consider a practical scenario in which only imperfect channel state information of Eve is available at the UAV-BS. Accordingly, we conceive a robust and secure resource allocation algorithm, which maximizes the WCSR by jointly optimizing both the Secure Relaying User Selection (SRUS) and the network parameter allocation problem, including the RS transmit precoders, message splitting variables, time slot sharing and power allocation. To circumvent the resultant non-convexity owing to the discrete variables imposed by SRUS, we propose a two-stage algorithm where the SRUS and network parameter allocation are accomplished in two consecutive stages. With regard to the SRUS, we study both centralized and distributed protocols. On the other hand, for jointly optimizing the network parameter allocation we resort to the Sequential Parametric Convex Approximation (SPCA) algorithm. Our numerical results show that the proposed solution significantly outperforms the existing benchmarks for a wide range of network loads in terms of the WCSR.

cs.IT↗

arXiv2022

Joint Reconfigurable Intelligent Surface Location and Passive Beamforming Optimization for Maximizing the Secrecy-Rate

The physical layer security (PLS) is investigated for reconfigurable intelligent surface (RIS) assisted wireless networks, where a source transmits its confidential information to a legitimate destination with the aid of a single small RIS in the presence of a malicious eavesdropper. A new joint RIS location and passive beamforming (J-LPB) optimization scheme is proposed for the sake of maximizing the secrecy rate under the RIS location constraint and the constraint that the modulus of the reflecting coefficient at each RIS's unit is not larger than 1. Specifically, we analyze the optimal location of the RIS, and conclude that the product involving the source-RIS distance and the RIS-destination distance should be minimized. Since the product minimization problem is nonconvex, we then propose a two-tier optimization algorithm for solving it. Based on the near-optimal RIS 3D location obtained, we further formulate the passive beamforming optimization problem, and then propose to apply the Charnes-Cooper transformation along with the sequential rank-one constraint relaxation (SROCR) algorithm to solve it. Our numerical results show that the secrecy rate of the proposed J-LPB optimization scheme is higher than that of the benchmarks. Explicitly, we use the following benchmarks: the near-source-based RIS location and passive beamforming (NSB-LPB) optimization scheme, the near-destination-based RIS location and passive beamforming (NDB-LPB) optimization scheme, and the random RIS location and passive beamforming (R-LPB) optimization scheme. Finally, the benefits of our J-LPB scheme are further increased with the number of RIS units.

cs.IT↗

arXiv2022

Large-Scale Rate-Splitting Multiple Access in Uplink UAV Networks: Effective Secrecy Throughput Maximization Under Limited Feedback Channel

UAVs are capable of improving the performance of next generation wireless systems. Specifically, UAVs can be exploited as aerial base-stations (UAV-BS) for supporting legitimate ground users in remote uncovered areas or in environments temporarily requiring high capacity. However, their communication performance is prone to both channel estimation errors and potential eavesdropping. Hence, we investigate the effective secrecy throughput of the UAV-aided uplink, in which rate-splitting multiple access (RSMA) is employed by each legitimate user for secure transmission under the scenario of massive access. To maximize the effective network secrecy throughput in the uplink, the transmission rate vs. power allocation relationship is formulated as a max-min optimization problem, relying on realistic imperfect CSI of both the legitimate users and of the potential eavesdroppers (Eves). We then propose a novel transformation of the associated probabilistic constraints for decoupling the variables, so that our design problem can be solved by alternatively activating the related block coordinate decent programming. In the model considered, each user transmits a superposition of two messages to a UAV-BS, each having different transmit power and the UAV-BS uses a SIC technique to decode the received messages. Given the non-convexity of the problem, it is decoupled into a pair of sub-problems. In particular, we derive a closed form expression for the optimal rate-splitting fraction of each user. Then, given the optimal rate-splitting fraction of each user, the ε-constrainted transmit power of each user is calculated by harnessing SPCA programming.

cs.IT↗

arXiv2022

Joint Activity Detection and Channel Estimation for Massive IoT Access Based on Millimeter-Wave/Terahertz Multi-Panel Massive MIMO

The multi-panel array, as a state-of-the-art antenna-in-package technology, is very suitable for millimeter-wave (mmWave)/terahertz (THz) systems, due to its low-cost deployment and scalable configuration. But in the context of nonuniform array structures it leads to intractable signal processing. Based on such an array structure at the base station, this paper investigates a joint active user detection (AUD) and channel estimation (CE) scheme based on compressive sensing (CS) for application to the massive Internet of Things (IoT). Specifically, by exploiting the structured sparsity of mmWave/THz massive IoT access channels, we firstly formulate the multi-panel massive multiple-input multiple-output (mMIMO)-based joint AUD and CE problem as a multiple measurement vector (MMV)-CS problem. Then, we harness the expectation maximization (EM) algorithm to learn the prior parameters (i.e., the noise variance and the sparsity ratio) and an orthogonal approximate message passing (OAMP)-EM-MMV algorithm is developed to solve this problem. Our simulation results verify the improved AUD and CE performance of the proposed scheme compared to conventional CS-based algorithms.

cs.IT↗

arXiv2022

Power Allocation for Space-Terrestrial Cooperation Systems with Statistical CSI

This paper studies an integrated network design that boosts system capacity through cooperation between wireless access points (APs) and a satellite. By coherently combing the signals received by the central processing unit from the users through the space and terrestrial links, we mathematically derive an achievable throughput expression for the uplink (UL) data transmission over spatially correlated Rician channels. A closed-form expression is obtained when maximum ratio combining is employed to detect the desired signals. We formulate the max-min fairness and total transmit power optimization problems relying on the channel statistics to perform power allocation. The solution of each optimization problem is derived in form of a low-complexity iterative design, in which each data power variable is updated based on a closed-form expression. The mathematical analysis is validated with numerical results showing the added benefits of considering a satellite link in terms of improving the ergodic data throughput.

cs.IT↗

arXiv2022

Circuit Symmetry Verification Mitigates Quantum-Domain Impairments

State-of-the-art noisy intermediate-scale quantum computers require low-complexity techniques for the mitigation of computational errors inflicted by quantum decoherence. Symmetry verification constitutes a class of quantum error mitigation (QEM) techniques, which distinguishes erroneous computational results from the correct ones by exploiting the intrinsic symmetry of the computational tasks themselves. Inspired by the benefits of quantum switch in the quantum communication theory, we propose beneficial techniques for circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state. In particular, we propose the spatio-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain stabilizer formalism to circuit-oriented stabilizers. The applicability and implementational strategies of the proposed techniques are demonstrated by using practical quantum algorithms, including the quantum Fourier transform (QFT) and the quantum approximate optimization algorithm (QAOA).

quant-ph↗