Applicability of Space-Time Block Codes for distributed cooperative broadcasting in MANETs with high node mobility

Mobile Ad-Hoc Networks (MANETs) are often characterized by high node mobility and rapid topology changes which in turn can cause high packet loss rates. In order to cope with this, MANETs typically rely on routing algorithms that try to efficiently distribute messages in the entire network. Such routing schemes introduce an overhead that limits the scalability of MANETs with respect to the number of nodes and/or mobility. Cooperative communication techniques have the potential to improve the efficiency of distributing messages and thus to increase the MANET scalability. In this paper, we propose an efficient cooperative broadcasting scheme based on distributed transmit diversity. To this end, we adapt Space-Time Block- Codes (STBCs), that were initially designed for a co-located setup in quasi-static environments, to a distributed setup with time-variant channels. We perform a comprehensive analysis based on bit error simulations to compare different STBC candidates and identify Linear-Scalable Dispersion Codes (LSDCs) to be a valuable option. For those, we propose an improvement of the inner-code for different channel models without the need for channel state information at the transmitter (CSIT). Besides, we validate the performance advantage of cooperative broadcasting by outage simulations in typical MANET scenarios

Distributed cooperative transmission in MANETs with multiple timing and carrier frequency offsets

Cooperative transmission is a promising approach to establish robust communication in Mobile Ad-Hoc Networks (MANETs). In low-complexity MANETs several transmitters (TX) can be aggregated to a virtual multiple input system. For such a setup, the impact of various timing and carrier frequency offsets (TO, CFO) has to be mitigated. We propose an effective, purely time-domain based equalizer structure that allows to establish cooperative transmission with a transmit diversity scheme in presence of aforementioned impairments and multipath channels. Exemplarily investigating the broadcast performance by outage simulations in a MANET scenario, we can demonstrate the benefits and indicate that the proposed structure is auspicious to improve the general scalability of MANETs

Silane Gas Production Through Hydrolysis of Magnesium Silicide by Hydrochloric Acid

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SDR-based demonstration system and applicability of SNR aggregation for multistage distributed cooperative communication in MANETs

Multistage or hierarchical distributed cooperative communication on the physical layer is a promising approach to overcome the scalability limitation of Mobile Ad-hoc Networks (MANETs). Standalone nodes that could successfully decode messages join transmission and support each other. They become a virtual transmit cluster and send simultaneously. While information theoretic research has demonstrated that an approximately linear scaling behaviour can be achieved, imperfections and constraints of practical systems have not been taken into account. Within this paper, we present a scalable and modular low-cost demonstration system based on software-defined radios (SDRs) to study distributed cooperative communication in practical MANETs. Furthermore, we apply SNR aggregation in combination with distributed cooperative transmission. To this end, we show a practical implementation approach and investigate the performance by measurements and simulations. Our results clearly highlight the advantages of combining distributed cooperative communication and SNR aggregation, e. g. to overcome larger distances in a distributed long haul multiple-input single-output (MISO) scenario or to enable a more efficient broadcast

Multi-carrier (OFDM) cooperative transmission in MANETs with multiple carrier frequency offsets

Cooperative transmission, realized by aggregating several nodes to a virtual multiple input system, is an auspicious approach to establish a more robust and effective communication in MANETs. In such a setup, impairments, i. e. multiple timing and carrier frequency offsets (TO, CFO) will occur. While multi-carrier schemes, e. g. Orthogonal Frequency Division Multiplexing (OFDM), are well-known to mitigate the impact of multipath propagation and TO, multiple CFO causes inter-carrier-interference (ICI) which typically degrades the performance significantly. Within this paper, we propose an effective code and equalizer structure that allows to overcome this limitation. It mitigates the impact of multiple CFO that can be significantly larger than the subcarrier spacing with a reasonable computational effort. For that, we utilize inherent code properties of Linear-Scalable Dispersion Codes (LSDCs) and propose a communication system composed of an equalizer structure in combination with LSDCs that enables multi-carrier distributed cooperative transmission for practical MANETs with high node mobility. We demonstrate the benefits of cooperative transmission in comparison to classical non-cooperative multi-hop or concurrent transmission by outage simulations, which clearly indicate that our proposal can be of crucial importance for the overall MANET scalability. Lastly, we compare our OFDM system with a recently proposed time-domain equalization single-carrier system and point out use cases, where the OFDM system can be more advantageous

The Cellular Relay Carpet: Distributed Cooperation with Ubiquitous Relaying

We consider the up- as well as downlink of a cellular network in which base stations (BSs) are supported by a large amount of relays spread over the entire area like a carpet. The BSs only see the static relays as the nodes they communicate with, which enables large antenna arrays at the BSs with sophisticated multi-user MIMO transmission. Together with a simple form of BS cooperation, the communication via the small relay cells allows to improve the data rates by distributed interference management and to reduce the complexity at the terminals. We investigate different types of relays as well as different relaying strategies for this relay carpet and compare them with respect to complexity, required channel state information (CSI), and performance in the interference-limited environment of dense cellular networks. The robustness of the different schemes with respect to channel estimation errors is studied and we conclude that especially relays of very low complexity are not sensitive to CSI imperfections. Relays can thus be applied in large numbers and enable massive MIMO at the BSs. The relay carpet proves thereby to be an efficient approach to enhance future generations of cellular networks significantly

Urology consultants versus large language models : potentials and hazards for medical advice in urology

Background Current interest surrounding large language models (LLMs) will lead to an increase in their use for medical advice. Although LLMs offer huge potential, they also pose potential misinformation hazards. Objective This study evaluates three LLMs answering urology-themed clinical case-based questions by comparing the quality of answers to those provided by urology consultants. Methods Forty-five case-based questions were answered by consultants and LLMs (ChatGPT 3.5, ChatGPT 4, Bard). Answers were blindly rated using a six-step Likert scale by four consultants in the categories: ‘medical adequacy’, ‘conciseness’, ‘coherence’ and ‘comprehensibility’. Possible misinformation hazards were identified; a modified Turing test was included, and the character count was matched. Results Higher ratings in every category were recorded for the consultants. LLMs' overall performance in language-focused categories (coherence and comprehensibility) was relatively high. Medical adequacy was significantly poorer compared with the consultants. Possible misinformation hazards were identified in 2.8% to 18.9% of answers generated by LLMs compared with <1% of consultant's answers. Poorer conciseness rates and a higher character count were provided by LLMs. Among individual LLMs, ChatGPT 4 performed best in medical accuracy (p < 0.0001) and coherence (p = 0.001), whereas Bard received the lowest scores. Generated responses were accurately associated with their source with 98% accuracy in LLMs and 99% with consultants. Conclusions The quality of consultant answers was superior to LLMs in all categories. High semantic scores for LLM answers were found; however, the lack of medical accuracy led to potential misinformation hazards from LLM ‘consultations’. Further investigations are necessary for new generations.Peer reviewe

ChatGPT versus consultants : blinded evaluation on answering otorhinolaryngology case–based questions

Background: Large language models (LLMs), such as ChatGPT (Open AI), are increasingly used in medicine and supplement standard search engines as information sources. This leads to more “consultations” of LLMs about personal medical symptoms. Objective: This study aims to evaluate ChatGPT’s performance in answering clinical case–based questions in otorhinolaryngology (ORL) in comparison to ORL consultants’ answers. Methods: We used 41 case-based questions from established ORL study books and past German state examinations for doctors. The questions were answered by both ORL consultants and ChatGPT 3. ORL consultants rated all responses, except their own, on medical adequacy, conciseness, coherence, and comprehensibility using a 6-point Likert scale. They also identified (in a blinded setting) if the answer was created by an ORL consultant or ChatGPT. Additionally, the character count was compared. Due to the rapidly evolving pace of technology, a comparison between responses generated by ChatGPT 3 and ChatGPT 4 was included to give an insight into the evolving potential of LLMs. Results: Ratings in all categories were significantly higher for ORL consultants (P<.001). Although inferior to the scores of the ORL consultants, ChatGPT’s scores were relatively higher in semantic categories (conciseness, coherence, and comprehensibility) compared to medical adequacy. ORL consultants identified ChatGPT as the source correctly in 98.4% (121/123) of cases. ChatGPT’s answers had a significantly higher character count compared to ORL consultants (P<.001). Comparison between responses generated by ChatGPT 3 and ChatGPT 4 showed a slight improvement in medical accuracy as well as a better coherence of the answers provided. Contrarily, neither the conciseness (P=.06) nor the comprehensibility (P=.08) improved significantly despite the significant increase in the mean amount of characters by 52.5% (n= (1470-964)/964; P<.001). Conclusions: While ChatGPT provided longer answers to medical problems, medical adequacy and conciseness were significantly lower compared to ORL consultants’ answers. LLMs have potential as augmentative tools for medical care, but their “consultation” for medical problems carries a high risk of misinformation as their high semantic quality may mask contextual deficits

Current Trends for 4D Space-Time Topology for Semantic Flow Segmentation

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