Throughput Maximisation in RIS-Assisted NOMA-THz Communication Network

In order to overcome spectrum scarcity and provide higher data rates, the sixth-generation (6G) wireless communication network is expected to perform data transmission using terahertz (THz) frequencies. However, the effective implementation of these communication systems is hampered by severe levels of signal degradation to which the THz bandwidth is subject to. Recent improvements and advancements in the fabrication process of electromagnetic (EM) metamaterials have made reconfigurable intelligent surfaces (RIS) a very promising solution to address these THz-related attenuation issues. Additionally, the adoption of non-orthogonal multiple access (NOMA) transmissions represents an effective way to improve spectrum efficiency for 6G networks. In this paper, we investigate the problem of downlink aggregated sum-rate maximisation for a multiple-input multiple-output (MIMO) system assisted by a RIS panel in performing NOMA transmission within the THz bandwidth. More specifically, we propose an optimization algorithm that jointly optimizes the transmitting power at the access point (AP) and the phase-shift coefficients for the RIS elements iteratively. Through simulation results, we demonstrate that the proposed method outperforms conventional benchmark schemes in terms of achieved aggregated throughput

Dynamic Packet Content Construction and Processing for End-to-End Streaming in 6G

In the context of 6G, the use of drones/UAVs and satellite is a high priority. One of the main issues is that there is limited and varying bandwidth in these environments, so the question arises: how do we provide high Quality of Experience (QoE) to the users. BPP is a recent protocol which is effective when used with Scalable Video Coding (SVC) streams and limited bandwidth environments. We present an end-to-end architecture, with a drone sending video, utilizing functions for dynamically constructing the content of packets, and then dynamically processing those packets during their transmission across a network, all managed by a multi-domain orchestrator. These functions are implemented as virtualized network elements, as in our previous work. In this current work, we investigate how different packing strategies for filling packets impact different QoE parameters, when evaluated using a number of different bandwidths. These insights can be utilized for choosing the best QoE, and will be especially useful in 5G / 6G environments

WIND: A Wireless Intelligent Network Digital Twin for federated learning and multi-layer optimization

oai:repository.mdx.ac.uk:269387The forthcoming wireless network is expected to support a wide range of applications, from supporting autonomous vehicles to massive Internet of Things (IoT) deployments. However, the coexistence of diverse applications under a unified framework presents several challenges, including seamless resource allocation, latency management, and systemwide optimization. Considering these requirements, this paper introduces WIND (Wireless Intelligent Network Digital Twin), a self-adaptive, self-regulating, and self-monitoring framework that integrates Federated Learning (FL) and multi-layer digital twins to optimize wireless networks. Unlike traditional Digital Twin (DT) models, the proposed framework extends beyond network modeling, incorporating both communication infrastructure and application-layer DTs to create a unified, intelligent, and context-aware wireless ecosystem. Besides, WIND utilizes local Machine Learning (ML) models at the edge node to handle low-latency resource allocation. At the same time, a global FL framework ensures long-term network optimization without centralized data collection. This hierarchical approach enables dynamic adaptation to traffic conditions, providing improved efficiency, security, and scalability. Moreover, the proposed framework is validated through a case study on federated reinforcement learning for radio resource management. Furthermore, the paper emphasizes the essential aspects, including the associated challenges, standardization efforts, and future directions opening the research in this domain

A real-time, scalable, fast and highly resource efficient decoder for a quantum computer

Quantum computers promise to solve computing problems that are currently intractable using traditional approaches. This can only be achieved if the noise inevitably present in quantum computers can be efficiently managed at scale. A key component in this process is a classical decoder, which diagnoses the errors occurring in the system. If the decoder does not operate fast enough, an exponential slowdown in the logical clock rate of the quantum computer occurs. Additionally, the decoder must be resource efficient to enable scaling to larger systems and potentially operate in cryogenic environments. Here we introduce the Collision Clustering decoder, which overcomes both challenges. We implement our decoder on both an FPGA and ASIC, the latter ultimately being necessary for any cost-effective scalable solution. We simulate a logical memory experiment on large instances of the leading quantum error correction scheme, the surface code, assuming a circuit-level noise model. The FPGA decoding frequency is above a megahertz, a stringent requirement on decoders needed for e.g. superconducting quantum computers. To decode an 881 qubit surface code it uses only $4.5\%$ of the available logical computation elements. The ASIC decoding frequency is also above a megahertz on a 1057 qubit surface code, and occupies 0.06 mm$^2$ area and consumes 8 mW of power. Our decoder is optimised to be both highly performant and resource efficient, while its implementation on hardware constitutes a viable path to practically realising fault-tolerant quantum computers.Comment: 11 pages, 4 figure

Effect of the COVID-19 pandemic on surgery for indeterminate thyroid nodules (THYCOVID): a retrospective, international, multicentre, cross-sectional study

Background Since its outbreak in early 2020, the COVID-19 pandemic has diverted resources from non-urgent and elective procedures, leading to diagnosis and treatment delays, with an increased number of neoplasms at advanced stages worldwide. The aims of this study were to quantify the reduction in surgical activity for indeterminate thyroid nodules during the COVID-19 pandemic; and to evaluate whether delays in surgery led to an increased occurrence of aggressive tumours.Methods In this retrospective, international, cross-sectional study, centres were invited to participate in June 22, 2022; each centre joining the study was asked to provide data from medical records on all surgical thyroidectomies consecutively performed from Jan 1, 2019, to Dec 31, 2021. Patients with indeterminate thyroid nodules were divided into three groups according to when they underwent surgery: from Jan 1, 2019, to Feb 29, 2020 (global prepandemic phase), from March 1, 2020, to May 31, 2021 (pandemic escalation phase), and from June 1 to Dec 31, 2021 (pandemic decrease phase). The main outcomes were, for each phase, the number of surgeries for indeterminate thyroid nodules, and in patients with a postoperative diagnosis of thyroid cancers, the occurrence of tumours larger than 10 mm, extrathyroidal extension, lymph node metastases, vascular invasion, distant metastases, and tumours at high risk of structural disease recurrence. Univariate analysis was used to compare the probability of aggressive thyroid features between the first and third study phases. The study was registered on ClinicalTrials.gov, NCT05178186.Findings Data from 157 centres (n=49 countries) on 87 467 patients who underwent surgery for benign and malignant thyroid disease were collected, of whom 22 974 patients (18 052 [78 center dot 6%] female patients and 4922 [21 center dot 4%] male patients) received surgery for indeterminate thyroid nodules. We observed a significant reduction in surgery for indeterminate thyroid nodules during the pandemic escalation phase (median monthly surgeries per centre, 1 center dot 4 [IQR 0 center dot 6-3 center dot 4]) compared with the prepandemic phase (2 center dot 0 [0 center dot 9-3 center dot 7]; p<0 center dot 0001) and pandemic decrease phase (2 center dot 3 [1 center dot 0-5 center dot 0]; p<0 center dot 0001). Compared with the prepandemic phase, in the pandemic decrease phase we observed an increased occurrence of thyroid tumours larger than 10 mm (2554 [69 center dot 0%] of 3704 vs 1515 [71 center dot 5%] of 2119; OR 1 center dot 1 [95% CI 1 center dot 0-1 center dot 3]; p=0 center dot 042), lymph node metastases (343 [9 center dot 3%] vs 264 [12 center dot 5%]; OR 1 center dot 4 [1 center dot 2-1 center dot 7]; p=0 center dot 0001), and tumours at high risk of structural disease recurrence (203 [5 center dot 7%] of 3584 vs 155 [7 center dot 7%] of 2006; OR 1 center dot 4 [1 center dot 1-1 center dot 7]; p=0 center dot 0039).Interpretation Our study suggests that the reduction in surgical activity for indeterminate thyroid nodules during the COVID-19 pandemic period could have led to an increased occurrence of aggressive thyroid tumours. However, other compelling hypotheses, including increased selection of patients with aggressive malignancies during this period, should be considered. We suggest that surgery for indeterminate thyroid nodules should no longer be postponed even in future instances of pandemic escalation.Funding None.Copyright (c) 2023 Published by Elsevier Ltd. All rights reserved

Characterisation of DOG-1 expression in salivary gland tumours and comparison with myoepithelial markers

DOG1 is an established diagnostic marker for gastrointestinal stromal tumours (GIST), but has been reported in salivary gland tumours (SGT) as an acinar and intercalated duct marker. However, its specificity and distribution is not well established. The aim of this study was to evaluate the diagnostic utility of DOG-1 expression in SGT in addition to comparing it with myoepithelial markers. Normal salivary tissue and SGT (n = 184) were examined for expression of DOG1 and a range of myoepithelial markers. SGT included: acinic cell carcinoma (ACC, n = 15), secretory carcinoma (SC, n = 9), pleomorphic adenoma (PA, n = 49), carcinoma ex-PA (Ca ex-PA, n = 11), adenoid cystic carcinoma (AdCC, n = 20), polymorphous adenocarcinoma (PAC, n = 6), myoepithelioma (n = 6), myoepithelial carcinoma (MC, n = 2), basal cell adenoma (BCA, n = 14), canalicular adenoma (CA, n = 19), mucoepidermoid carcinoma (MEC, n = 11), oncocytoma (n = 2), adenocarcinoma NOS (AdNOS, n = 4), basal cell adenocarcinoma (BCAC, n = 2), salivary duct carcinoma (SDC, n = 3) and papillary cystadenocarcinoma (PCAC, n = 1). Normal acini and ACC (14/15) showed strong luminal DOG1 staining; SC were largely negative with only focal expression in 3/9 cases. Luminal staining was seen in PA (14/49), PAC (4/6), Ca ex-PA (4/11) and AdCC (6/20). 8/11 MEC showed luminal and/or mucous cell staining. No staining was seen in myoepithelioma, MC, CA, adNOS and BCAC. BCA showed strong staining of myoepithelial cells in some cases (5/14). Variable myoepithelial DOG1 staining was seen in PA, Ca ex PA, BCA, SDC and PCAC which was not as consistent as myoepithelial markers such as calponin, p63 and αSMA. Absence of DOG1 can differentiate ACC from SC, but staining is variable in PA, PLGA and Ca ex-PA. Myoepithelial staining in some tumours but not in normal gland suggests a wider distribution in SGT than originally envisaged