Wireless World Research and Trends Magazine

Federated Learning for Energy Efficiency in 6G

2024-10-03T12:40:08+02:00

This paper presents a multi-tier Federated Learning (FL) architecture designed to optimize energy efficiency in 6G, with particular emphasis on compliance with the Network Data Analytics Function (NWDAF) standards defined by 3GPP. Unlike existing FL architectures that often overlook energy efficiency and lack full integration with network functions like NWDAF, our proposed architecture integrates AI-driven strategies across multi layers. This multi-tier approach dynamically adjusts computation and communication rounds, reducing energy consumption while maintaining high model accuracy and network performance. By addressing challenges such as data heterogeneity and personalisation through adaptive training, intelligent routing, and advanced model aggregation, the architecture significantly enhances energy efficiency. Initial simulations, aligned with NWDAF processing requirements, underscore the architecture’s suitability for deployment in 6G , offering a scalable, energy-efficient, and privacy-preserving solution that aligns with industry standards and addresses key challenges in distributed learning.

Federated Learning Enhancement Through Transfer and Continual Learning Integration: Analyzing Effects of Different Levels of Dirichlet Distribution

2024-09-12T22:41:39+02:00

Machine learning plays a pivotal role in modern technology, driving advancements across various domains such as healthcare, finance, and autonomous systems. Federated Learning (FL) offers a significant advantage over traditional machine learning by enabling decentralized model training without requiring data to be centralized, thereby enhancing privacy and security. With the advent of 6G networks, which promise ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC), FL can be significantly enhanced. 6G’s improved bandwidth and latency characteristics will enable more efficient data exchange and model updates, further enhancing the adoption of FL. However, the performance of FL can be significantly affected by data distribution, particularly in non-IID (non-Independent and Identically Distributed) scenarios, where FL tends to perform poorly. This paper proposes a novel approach to enhance FL by integrating Transfer Learning (TL) and Continual Learning (CL), named Integrated Federated Transfer and Continual Learning (IFTCL). TL can extract features from client training samples to benefit subsequent clients, while CL mitigates catastrophic forgetting caused by heterogeneous data across clients. This integration improves FL performance under varying degrees of heterogeneous data distributions simulated by Dirichlet distribution, enhancing accuracy, convergence speed, and reducing communication overhead. The proposed method’s feasibility is validated using a publicly available radar recognition dataset.

The Impact of Mobility, Beam Sweeping and Smart Jammers on Security Vulnerabilities of 5G Cells

2024-05-03T23:59:19+02:00

The vulnerability of 5G networks to jamming attacks has emerged as a significant concern. This paper contributes in two primary aspects. Firstly, it investigates the effect of a multi-jammer on 5G cell metrics, specifically throughput and goodput. The investigation is conducted within the context of a mobility model for user equipment (UE), with a focus on scenarios involving connected vehicles (CVs) engaged in a mission. Secondly, the vulnerability of synchronization signal block (SSB) components is examined concerning jamming power and beam sweeping. Notably, the study reveals that increasing jamming power beyond 40 dBm blackin our specific scenario configuration no longer decreases network throughput due to the re-transmission of packets through the hybrid automatic repeat request (HARQ) process. Furthermore, it is observed that under the same jamming power, the physical downlink shared channel (PDSCH) is more vulnerable than the primary synchronization signal (PSS) and secondary synchronization signal (SSS). However, a smart jammer can disrupt the cell search process by injecting less power and targeting PSS-SSS or physical broadcast channel (PBCH) data compared to a barrage jammer. On the other hand, beam sweeping proves effective in mitigating the impact of a smart jammer, reducing the error vector magnitude root mean square from 51.59% to 23.36% under the same jamming power.

Bayesian Learning based Rate Adaptation in IEEE 802.11ax WLANs with a Target PER

2024-07-02T21:29:47+02:00

The optimal modulation and coding scheme (MCS) selection in wireless transmission depends on the dynamically evolving channel state. Hence, Rate adaptation in a wireless channel relies on periodically reported channel quality indicator (CQI) values to select the optimal MCS. The latest 802.11ax, with a HE-sounding protocol, supports an explicit feedback mechanism where the client sends back a transformed estimate of the channel state information (CSI) in the HE CQI Report field. When generated more frequently, these reports can be expensive as they introduce unnecessary computational and protocol overhead. Also, the CSI feedback information is quantized, delayed, and noisy. To reduce the frequent CSI feedback (receiver to the transmitter) overhead, in our work, we obtain CSI statistically at the transmitter through Bayesian Learning (BL). Further, we propose a Bayesian Learning based Rate Adaptation (BLbRA) scheme at the transmitter. BLbRA throughput performance is consistent even with reduced feedback overhead. BLbRA can be implemented without any change in the standard frame format, and therefore, it is suitable for practical deployment.

Spectral Sampling and Signal Decomposition (SSSD) for Improved Spectral Efficiency

2024-10-22T00:26:26+02:00

Spectrally Efficient Frequency Division Multiplexing (SEFDM) aims to enhance spectral efficiency by compressing subcarriers in the frequency domain, thereby reducing the required bandwidth. This approach primarily focuses on minimizing Inter-Carrier Interference (ICI), which typically necessitates a complex receiver design. We propose a simpler receiver design based on Spectral Sampling and Signal Decomposition (SSSD) technique. This technique facilitates the receiver to process Orthogonal Frequency Division Multiplexing (OFDM) signals outside the conventional orthogonality points in the frequency domain. Unlike traditional SEFDM approaches, the SSSD receiver utilizes interfering carriers as useful signals. Through simulations, we showcase the SSSD receiver’s performance in extracting SEFDM signals and accommodating various pulse shapes beyond the conventional sinc pulse. However, our results also highlight a significant challenge posed by severely ill-conditioned matrices, which can be mitigated by exploring alternative pulse types.

Enabling Safer Crosswalks with State-of-the-Art Vehicle-to-Everything (V2X) Technology

2024-08-10T00:08:47+02:00

Vehicle-to-Everything (V2X) communication is a technology that enables vehicles to communicate with other vehicles (V2V), infrastructure (V2I), cyclists, and pedestrians (V2P). V2X employs antenna technology that allows omnidirectional wireless data transmission between all nodes in the transportation ecosystem. This has strong implications for improving pedestrian safety, reducing traffic congestion, and enabling smart city applications. One area where V2X can have a tremendous impact is at pedestrian crosswalks. Currently, these are dangerous zones where vehicle-pedestrian collisions occur frequently due to blind spots, distracted driving/walking, and unclear right-of-way. V2X aims to eliminate these collisions by allowing vehicles and pedestrian smartphones to continuously share their real-time locations, trajectories, and analytics. This seamless connectivity is enabled by V2X antennas embedded in cars and mobile devices. The primary goal of this research is to increase the safety of pedestrians on and near crosswalks positioned on roadways. The Altair FEKO 2022.1 software is used in this study to create a symmetrical V2X communication scenario with intersecting highways. Cars, pedestrians, roads, and traffic lights are arranged in the Altair WallMan Computer-Aided Design (CAD) software. The Shooting Bouncing Ray (SBR+) solver in Altair ProMan is computed on a CAD-modelled database to simulate the power received at key prediction points on crosswalks in the path of each vehicle. Intelligent Ray Tracing (IRT) is utilized to animate the scene with moving cars and pedestrians over a three-second time interval while simultaneously counting the number of propagation pathways and rays used at each instant. At each prediction time instant and for every prediction point, power received is measured in decibel-milliwatts (dBm). The computed simulation results are analysed at 5.8 GHz, 6 GHz, and 28 GHz.

Multi-disciplinary Approach to 6G

2024-07-12T20:38:35+02:00

6G aims to address a number of societal challenges, while promising performance improvements over prior generations of international mobile communication technology. This calls for going beyond a traditional technology-driven approach in mobile communication system development and considering a wider set of values that are particularly driven by sustainability principles. In this 6G era, multi-disciplinarity, covering disciplines beyond telecommunications engineering, becomes an increasingly important approach to develop future-proof mobile communication technologies and services. This paper introduces a multi-disciplinary approach to 6G research and development (R&D) considering technology, business, regulation, and sustainability perspectives. Key topics and tools are introduced together with two case examples that illustrate how multi-disciplinarity, considering the proposed four perspectives, can bring new insights into practical topics relevant for 6G R&D.