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Found 36 papers, 3 papers with code
Secrecy Rate Maximization with Artificial Noise for Pinching-Antenna Systems
Security is emerging as a critical performance metric for next-generation wireless networks, but conventional multiple-input-multiple-output (MIMO) systems often suffer from severe path loss and are vulnerable to nearby eavesdroppers due to their fixed-antenna configurations.
Redefining Information Freshness: AoGI for Generative AI in 6G Networks
Generative Artificial Intelligence (GenAI) is playing an increasingly important role in enriching and facilitating human life by generating various useful information, of which real-time GenAI is a significant part and has great potential in applications such as real-time robot control, automated driving, augmented reality, etc.
Cross-Band Modulation Design for Hybrid RF-Optical Systems
We present a novel cross-band modulation framework that combines 3D modulation in the RF domain with intensity modulation and direct detection in the optical domain, the first such integration to enhance communication reliability.
Stochastic Geometry for Modeling and Analysis of Sensing and Communications: A Survey
Stochastic geometry is the study and analysis of random spatial patterns, and as such, stochastic geometry tools have been considered for the performance evaluation of wireless networks with different types of nodes.
Latency-Aware Resource Allocation for Integrated Communications, Computation, and Sensing in Cell-Free mMIMO Systems
In this paper, we investigate a cell-free massive multiple-input and multiple-output (MIMO)-enabled integration communication, computation, and sensing (ICCS) system, aiming to minimize the maximum computation latency to guarantee the stringent sensing requirements.
AFDM-Enabled Integrated Sensing and Communication: Theoretical Framework and Pilot Design
However, the unprecedented characteristics of 6G, especially the doubly dispersive channel, make classical ISAC waveforms rather challenging to guarantee a desirable performance level.
Rethinking Hardware Impairments in Multi-User Systems: Can FAS Make a Difference?
In this paper, we analyze the role of fluid antenna systems (FAS) in multi-user systems with hardware impairments (HIs).
Split Learning in Computer Vision for Semantic Segmentation Delay Minimization
In this paper, we propose a novel approach to minimize the inference delay in semantic segmentation using split learning (SL), tailored to the needs of real-time computer vision (CV) applications for resource-constrained devices.
Fluid Antenna Systems Enabling 6G:Principles, Applications, and Research Directions
Fluid antenna system (FAS) as a new version of reconfigurable antenna technologies promoting shape and position flexibility, has emerged as an exciting and possibly transformative technology for wireless communications systems.
FAS-Driven Spectrum Sensing for Cognitive Radio Networks
Cognitive radio (CR) networks face significant challenges in spectrum sensing, especially under spectrum scarcity.
Hybrid Semantic-Shannon Communications
Semantic communications are considered a promising beyond-Shannon/bit paradigm to reduce network traffic and increase reliability, thus making wireless networks more energy efficient, robust, and sustainable.
Transmit Beamforming Design for ISAC with Stacked Intelligent Metasurfaces
This paper proposes a transmit beamforming strategy for the integrated sensing and communication (ISAC) systems enabled by the novel stacked intelligent metasurface (SIM) architecture, where the base station (BS) simultaneously performs downlink communication and radar target detection via different beams.
Age of Trust (AoT): A Continuous Verification Framework for Wireless Networks
We then investigate how to find a compromise between average AoT and wireless transmission efficiency with limited resources.
Statistical Age of Information: A Risk-Aware Metric and Its Applications in Status Updates
Motivated by this concern, and following the spirit of entropic value-at-risk (EVaR) in the field of risk analysis, in this paper we present a concept, termed Statistical AoI, for providing a unified framework to guarantee various requirements of risk-sensitive status-update services with the demand on the violation probability of the peak age.
Waveform Design for Over-the-Air Computing
To this end, we examine the theoretical mean squared error (MSE) for OTA transmission under time sampling error and ISI, while also exploring methods for minimizing the MSE in the OTA transmission.
Deep Joint Semantic Coding and Beamforming for Near-Space Airship-Borne Massive MIMO Network
Specifically, we propose a deep joint semantic coding and beamforming (JSCBF) scheme for airship-based massive MIMO image transmission network in space, in which semantics from both source and channel are fused to jointly design the semantic coding and physical layer beamforming.
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems
Federated learning (FL) is a decentralized learning technique that enables participating devices to collaboratively build a shared Machine Leaning (ML) or Deep Learning (DL) model without revealing their raw data to a third party.
On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach
Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing.
Multiple Access in the Era of Distributed Computing and Edge Intelligence
This paper focuses on the latest research and innovations in fundamental next-generation multiple access (NGMA) techniques and the coexistence with other key technologies for the sixth generation (6G) of wireless networks.
Learning to Optimize Resource Assignment for Task Offloading in Mobile Edge Computing
In this paper, we consider a multiuser mobile edge computing (MEC) system, where a mixed-integer offloading strategy is used to assist the resource assignment for task offloading.
Wireless Quantized Federated Learning: A Joint Computation and Communication Design
The impact of the quantization error on the convergence time is evaluated and the trade-off among model accuracy and timely execution is revealed.
Optimization of Grant-Free NOMA with Multiple Configured-Grants for mURLLC
To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs.
Channel modeling for in-body optical wireless communications
Moreover, we formulate a model for the calculation of the absorption coefficient of any generic biological tissue.
Waveform Design for Joint Sensing and Communications in Millimeter-Wave and Low Terahertz Bands
Finally, a data-embedded MS-QP (DE-MS-QP) waveform is constructed through time-domain extension of the MS-QP sequence, generating null frequency points on each subband for data transmission.
Wireless Federated Learning (WFL) for 6G Networks -- Part II: The Compute-then-Transmit NOMA Paradigm
As it has been discussed in the first part of this work, the utilization of advanced multiple access protocols and the joint optimization of the communication and computing resources can facilitate the reduction of delay for wireless federated learning (WFL), which is of paramount importance for the efficient integration of WFL in the sixth generation of wireless networks (6G).
Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trends
Conventional machine learning techniques are conducted in a centralized manner.
Large Scale Global Optimization Algorithms for IoT Networks: A Comparative Study
To the best of the authors knowledge, this is the first time that LGSO algorithms are applied to the optimal power allocation problem in IoT networks.
On the Distribution of the Sum of Double-Nakagami-m Random Vectors and Application in Randomly Reconfigurable Surfaces
To facilitate the performance analysis of a RRS-assisted system, first, we present novel closed-form expressions for the probability density function, the cumulative distribution function, the moments, and the characteristic function of the distribution of the sum of double-Nakagami-m random vectors, whose amplitudes follow the double-Nakagami-m distribution, i. e., the distribution of the product of two Nakagami-m random variables, and phases follow the circular uniform distribution.
Information Theory Signal Processing Information Theory Applications
Learning based signal detection for MIMO systems with unknown noise statistics
Existing detection methods have mainly focused on specific noise models, which are not robust enough with unknown noise statistics.
Towards Optimally Efficient Search with Deep Learning for Large-Scale MIMO Systems
This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems.
Non-Orthogonal Multiple Access (NOMA) With Multiple Intelligent Reflecting Surfaces
More importantly, simulation results reveal that a 3-bit resolution for discrete phase shifts is sufficient to achieve near-optimal outage performance.
Optimal Resource Allocation for Delay Minimization in NOMA-MEC Networks
We adopt the partial offloading policy, in which each user can partition its computation task into offloading and locally computing parts.
AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems
Accurate channel state information (CSI) feedback plays a vital role in improving the performance gain of massive multiple-input multiple-output (m-MIMO) systems, where the dilemma is excessive CSI overhead versus limited feedback bandwith.
Machine Learning in Nano-Scale Biomedical Engineering
Next, we discuss the state of the art ML methodologies that are used to countermeasure the aforementioned challenges.
All-Optical Cochlear Implants
In the present work, we introduce a novel cochlear implant (CI) architecture, namely all-optical CI (AOCI), which directly converts acoustic to optical signals capable of stimulating the cochlear neurons.
Analyzing Grant-Free Access for URLLC Service
Based on this framework, we define the latent access failure probability to characterize URLLC reliability and latency performances.
