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The DVB standard does not mandate the use of authentication and integrity protection for transport streams. This allows malicious third parties to replace legitimate broadcasts by overpowering terrestrial transmissions. The rogue signal can then deliver a malicious broadcast stream to exploit security vulnerabilities on Smart TVs (STVs) in range. We implemented a proof-of-concept attack based on a malicious Hybrid Broadcast Broadband TV app, able to acquire permanent system-level access to an STV over the air, in less than 10 s. These attacks, however, are severely limited in range due to required co-channel protection ratios (CCPRs), which is in direct contradiction to previous publications. We present evidence for these limitations in form of laboratory experiments, extensive simulations, and field measurements. To this end, we developed an automated, low-cost method for CCPR determination, as well as a method for non-disruptive attack range measurements based on a gap filler and the resulting channel impulse response., Comment: 13 pages

The increasing consumption of video streams and the demand for higher-quality content drive the evolution of telecommunication networks and the development of new network accelerators to boost media delivery while optimizing network usage. Multi-access Edge Computing (MEC) enables the possibility to enforce media delivery by deploying caching instances at the network edge, close to the Radio Access Network (RAN). Thus, the content can be prefetched and served from the MEC host, reducing network traffic and increasing the Quality of Service (QoS) and the Quality of Experience (QoE). This paper proposes a novel mechanism to prefetch Dynamic Adaptive Streaming over HTTP (DASH) streams at the MEC, employing a Machine Learning (ML) classification model to select the media segments to prefetch. The model is trained with media session metrics to improve the forecasts with application layer information. The proposal is tested with Mobile Network Operators (MNOs)' 5G MEC and RAN and compared with other strategies by assessing cache and player's performance metrics.

In an increasingly connected world, wireless networks' monitoring and characterization are of vital importance. Service and application providers need to have a detailed understanding of network performance to offer new solutions tailored to the needs of today's society. In the context of mobility, in-vehicle infotainment services are expected to stand out among other popular connected vehicle services, so it is essential that communication networks are able to satisfy the Quality of Service (QoS) and Quality of Experience (QoE) requirements needed for these type of services. This paper investigates a multi-layer network performance monitoring architecture at the edge providing QoS, QoE, and localization information for vehicular video streaming applications in real-time over 5G networks. In order to conduct field trials and show test results, Mobile Network Operators (MNOs)' 5G Standalone (SA) network and Multi-access Edge Computing (MEC) infrastructure are used to provide connectivity and edge computing resources to a vehicle equipped with a 5G modem.

Vehicles shipping sensors for onboard systems are gaining connectivity. This enables information sharing to realize a more comprehensive understanding of the environment. However, peer communication through public cellular networks brings multiple networking hurdles to address, needing in-network systems to relay communications and connect parties that cannot connect directly. Web Real-Time Communication (WebRTC) is a good candidate for media streaming across vehicles as it enables low latency communications, while bringing standard protocols to security handshake, discovering public IPs and transverse Network Address Translation (NAT) systems. However, the end-to-end Quality of Service (QoS) adaptation in an infrastructure where transmission and reception are decoupled by a relay, needs a mechanism to adapt the video stream to the network capacity efficiently. To this end, this paper investigates a mechanism to apply changes on resolution, framerate and bitrate by exploiting the Real Time Transport Control Protocol (RTCP) metrics, such as bandwidth and round-trip time. The solution aims to ensure that the receiving onboard system gets relevant information in time. The impact on end-to-end throughput efficiency and reaction time when applying different approaches to QoS adaptation are analyzed in a real 5G testbed.

The Shannon upper bound places a limit on the error-free information transmission rate (capacity) of a noisy channel. It has stood for over sixty years, and underlies both theoretical and practical work in the telecommunications industry. This upper bound arises from the Shannon-Hartley law, which has two parameters: the available bandwidth and the signal-to-noise power ratio. However, aside from these explicit parameters, the Shannon-Hartley law also rests on certain assumptions. One of these is that the channel is linear: recent work has shown that nonlinear channels are not limited by the Shannon upper bound. A second assumption, arising from the mathematical tools used in its proof, is that signals are periodic. Surprisingly, the capacity limit associated with non-periodic signals has not previously been examined. Here we show, both theoretically and by construction, that the use of non-periodic signals, based on complex spirals, allows the Shannon upper bound to be extended., Comment: The paper has been withdrawn by the authors due to fundamental doubts raised by reviewers