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This paper delves into the imperative need for coexistence and compatibility in the 470–694 MHz frequency band, as mandated by the World Radiocommunication Conference 2015 (WRC-15) and the WRC-23. It focuses on challenges in the coexistence of Digital Terrestrial Television Broadcasting (DTTB) and International Mobile Telecommunications-2020 (IMT-2020) services in downlink-only mode, particularly in adjacent-channel scenarios. Using Monte Carlo simulations, the study evaluates the probability of interference from IMT base stations with DTTB reception. The analysis thoroughly investigates the impact of the IMT transmitter's Adjacent Channel Leakage Ratio (ACLR) and the DTTB receiver's Adjacent-Channel Selectivity (ACS) on the probability of interference. The results demonstrate a significant degradation in the DTTB reception probability at the edge of coverage based on standard assumptions. To address these challenges, this paper provides recommendations for mitigating interference. These include defining enhanced ACLR regulations for IMT base stations, implementing antenna discriminations, providing specialized filters, and establishing national coordination procedures. The research provides valuable insights for informed decision making in spectrum management within the 470–694 MHz band, aiming to facilitate the coexistence of DTTB and IMT-2020 services, in line with international regulations and best practices. [ABSTRACT FROM AUTHOR]

In classical broadcast models, information is disseminated in synchronous rounds under the constant communication time model, wherein a node may only inform one of its neighbors in each time-unit—also known as the processor-bound model. These models assume either a coordinating leader or that each node has a set of coordinated actions optimized for each originator, which may require nodes to have sufficient storage, processing power, and the ability to determine the originator. This assumption is not always ideal, and a broadcast model based on the node's local knowledge can sometimes be more effective. Messy models address these issues by eliminating the need for a leader, knowledge of the starting time, and the identity of the originator, relying solely on local knowledge available to each node. This paper investigates the messy broadcast time and optimal scheme in a grid graph, a structure widely used in computer networking systems, particularly in parallel computing, due to its robustness, scalability, fault tolerance, and simplicity. The focus is on scenarios where the originator is located at one of the corner vertices, aiming to understand the efficiency and performance of messy models in such grid structures. [ABSTRACT FROM AUTHOR]

Joint source–channel coding (JSCC) based on deep learning has shown significant advancements in image transmission tasks. However, previous channel-adaptive JSCC methods often rely on the signal-to-noise ratio (SNR) of the current channel for encoding, which overlooks the neural network's self-adaptive capability across varying SNRs. This paper investigates the self-adaptive capability of deep learning-based JSCC models to dynamically changing channels and introduces a novel method named Channel-Blind JSCC (CBJSCC). CBJSCC leverages the intrinsic learning capability of neural networks to self-adapt to dynamic channels and diverse SNRs without relying on external SNR information. This approach is advantageous, as it is not affected by channel estimation errors and can be applied to one-to-many wireless communication scenarios. To enhance the performance of JSCC tasks, the CBJSCC model employs a specially designed encoder–decoder. Experimental results show that CBJSCC outperforms existing channel-adaptive JSCC methods that depend on SNR estimation and feedback, both in additive white Gaussian noise environments and under slow Rayleigh fading channel conditions. Through a comprehensive analysis of the model's performance, we further validate the robustness and adaptability of this strategy across different application scenarios, with the experimental results providing strong evidence to support this claim. [ABSTRACT FROM AUTHOR]

While the commercial value of sport media remains intrinsically pegged to viewing behaviours, technological transformation and digitalisation of media are challenging the utility of established audience measurement techniques. With existing norms appearing increasingly outdated, the aim of this conceptual paper is to articulate the key strategic and commercial implications resulting from technological innovation within sport media, with a focus on audience measurement. We use a cyclical innovation framework to conceptualise the changing structures, products and relationships within the sport media ecosystem to map the corresponding strategic implications for relevant stakeholders. As the efficacy of analogue audience measurement erodes, digital sport media platforms offer new opportunities to harness forms of user data that facilitate deeper and more accurate measurement. While this helps inform rights valuations and creates new commercialisation opportunities, it also creates new complexity. Such complexities surround the harmonisation of linear and digital audience data, establishment of nascent valuation norms and shifts in inter-organisational power dynamics. Five implications linked to interrelated themes of measurement, value and performance assessment are detailed. These include establishing a new paradigm of measurement, including individual-level consumer behaviours, consideration of new forms of value and new currency to inform rights markets, and implications for data and information sharing. Provocations to inform a future research agenda and future research opportunities are provided. [ABSTRACT FROM AUTHOR]

This paper investigated the local content programming and cultural imperialism in the Nigerian broadcast media with a view to determining the extent to which cultural imperialism influences radio and television stations in Nigeria local content programming and the impact of their programmes on the indigenous culture. The theories of cultural imperialism and cultivation Analysis were employed to underpin or support the study. A survey method was used to gather data for the study. Based on the empirical data obtained, it was revealed that broadcast stations in Nigeria are involved in local content programming but the extent is not significant as foreign programmes still take the lead in our broadcast stations. Even most of the programmes that are regarded as indigenous in our broadcast stations, are in most case, dominated by the content of foreign materials. This to a large extent has contributed to cultural imperialism as our indigenous programmes are in most cases relegated to the background even by the indigenous viewers. Findings also revealed that the reasons for this horrible and hideous situation is lack of finance for local programming, lack of digitalized equipment which constitutes poor quality programming and lack of austere monitoring by the NBC. The study concludes that broadcast media in Nigeria is still infantile as well as juvenile in terms of local content programming, thereby opening wider fissure for foreign programmes to fill in. The study recommends that the broadcast media in Nigeria should come back to the drawing board and pay keen attention to local programming as this is the only way our core values as a people and nation can be promoted. NBC must not relent in ensuring compliance and sanctioning violators of the local content provisions. [ABSTRACT FROM AUTHOR]

Session types are formal specifications of communication protocols, allowing protocol implementations to be verified by typechecking. Up to now, session type disciplines have assumed that the communication medium is reliable, with no loss of messages. However, unreliable broadcast communication is common in a wide class of distributed systems such as ad-hoc and wireless sensor networks. Often such systems have structured communication patterns that should be amenable to analysis by means of session types, but the necessary theory has not previously been developed. We introduce the Unreliable Broadcast Session Calculus, a process calculus with unreliable broadcast communication, and equip it with a session type system that we show is sound. We capture two common operations, broadcast and gather, inhabiting dual session types. Message loss may lead to non-synchronised session endpoints. To further account for unreliability we provide with an autonomous recovery mechanism that does not require acknowledgements from session participants. Our type system ensures soundness, safety, and progress between the synchronised endpoints within a session. We demonstrate the expressiveness of our framework by implementing Paxos, the textbook protocol for reaching consensus in an unreliable, asynchronous network. [ABSTRACT FROM AUTHOR]

We address in this work the topic of broadcasting one's visual reality, captured by the video cameras embedded in mobile and wearable devices, to a remote audience. We discuss several designs of such life broadcasting systems, which we position at the intersection of lifelogging, Alternate Reality, and Cross-Reality technology. To this end, we introduce the "Alternate Reality Broadcast-Time" matrix for the broadcasting and consumption of alternate realities, in which designs of systems that implement sharing and consumption of personal visual realities can be positioned, characterized, and compared. These design options range from simple video streaming over the web using conventional video protocols to mediated and augmented reality, to audio narration and vibrotactile rendering of concepts automatically detected from video captured by wearable cameras. To demonstrate the usefulness of our broadcast-time matrix, we describe three prototypes implementing lifelogging, concept recognition from video, augmented and mediated vision, and vibrotactile feedback. Our contributions open the way toward new applications that blend lifelogging, consumption of multimedia alternate realities, and XR technology to empower users with richer opportunities for self-expression and new means to connect with the followers of events in their lives. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has been applied widely in the field of communication. In this paper, a deep learning-based frequency modulation (FM) demodulation method is proposed for FM signal demodulation in complex electromagnetic environment. The received in-phase and quadrature (IQ) signals are fed into a designed model to recover the source signal. We perform experiments with random, voice and broadcasting signals under noise, multipath fading and single tone jamming scenarios. The results demonstrate that the proposed method outperforms the traditional method in terms of demodulation performance across all scenarios. Especially in the case of multipath fading and single tone jamming, our proposed method exhibits significant performance improvements over traditional demodulation. We further propose to use downsampling to reduce the computational complexity of DeepDeFM. The results indicate that by selecting a proper downsampling factor, it is possible to significantly reduce time complexity while maintaining high sound quality with minimal attenuation.

While centred on the medium of audio, podcasts are often a multimedia concern, and one that has become hugely popular in recent years, though relatively little is known about the perspectives of podcast creators and their visions of innovation. This article details the results of an exploratory study conducted to enhance our understanding of potential innovation in the field of podcasting. Sixteen podcast creators were interviewed about their work and what they wanted from next-generation podcasts, in order to understand the requirements and expectations of tools that could be built to create new forms of audio-based programming. Through a combination of qualitative and quantitative analysis, we reveal novel findings, such as the duality between "listener-centric" and "creator-centric" innovations, to improve listener experience but also to unleash new creative possibilities in a streamlined production workflow. We shed light on what podcast creators envision as "next-generation podcasting," the archetypal podcast production workflow, and creators' expectations of podcasting tools. Combining these findings, we identify how the workflow could be modified to include new steps that will help to realise podcast creators' visions. This study crystalises on important information about podcasters--their behavior and perspectives on the future of the medium--which will allow further research, design, and development in the field to be founded upon empirical observations. [ABSTRACT FROM AUTHOR]

In classical broadcast models, information is disseminated in synchronous rounds under the constant communication time model, wherein a node may only inform one of its neighbors in each time-unit—also known as the processor-bound model. These models assume either a coordinating leader or that each node has a set of coordinated actions optimized for each originator, which may require nodes to have sufficient storage, processing power, and the ability to determine the originator. This assumption is not always ideal, and a broadcast model based on the node’s local knowledge can sometimes be more effective. Messy models address these issues by eliminating the need for a leader, knowledge of the starting time, and the identity of the originator, relying solely on local knowledge available to each node. This paper investigates the messy broadcast time and optimal scheme in a grid graph, a structure widely used in computer networking systems, particularly in parallel computing, due to its robustness, scalability, fault tolerance, and simplicity. The focus is on scenarios where the originator is located at one of the corner vertices, aiming to understand the efficiency and performance of messy models in such grid structures.

Joint source–channel coding (JSCC) based on deep learning has shown significant advancements in image transmission tasks. However, previous channel-adaptive JSCC methods often rely on the signal-to-noise ratio (SNR) of the current channel for encoding, which overlooks the neural network’s self-adaptive capability across varying SNRs. This paper investigates the self-adaptive capability of deep learning-based JSCC models to dynamically changing channels and introduces a novel method named Channel-Blind JSCC (CBJSCC). CBJSCC leverages the intrinsic learning capability of neural networks to self-adapt to dynamic channels and diverse SNRs without relying on external SNR information. This approach is advantageous, as it is not affected by channel estimation errors and can be applied to one-to-many wireless communication scenarios. To enhance the performance of JSCC tasks, the CBJSCC model employs a specially designed encoder–decoder. Experimental results show that CBJSCC outperforms existing channel-adaptive JSCC methods that depend on SNR estimation and feedback, both in additive white Gaussian noise environments and under slow Rayleigh fading channel conditions. Through a comprehensive analysis of the model’s performance, we further validate the robustness and adaptability of this strategy across different application scenarios, with the experimental results providing strong evidence to support this claim.