The Broadcast Approach in Communication Networks

Abstract

In this paper we review the theoretical and practical principles of the broadcast approach to communication over state-dependent channels and networks in which the transmitters have access to only the probabilistic description of the time-varying states while remaining oblivious to their instantaneous realizations. When the temporal variations are frequent enough, an effective long-term strategy is adapting the transmission strategies to the system’s ergodic behavior. However, when the variations are infrequent, their temporal average can deviate significantly from the channel’s ergodic mode, rendering a lack of instantaneous performance guarantees. To circumvent a lack of short-term guarantees, the broadcast approach provides principles for designing transmission schemes that benefit from both short- and long-term performance guarantees. This paper provides an overview of how to apply the broadcast approach to various channels and network models under various operational constraints.

Learning to Broadcast for Ultra-Reliable Communication with Differential Quality of Service via the Conditional Value at Risk

“…We assume that the BS does not know the fading realizations nor the common fading distribution h (ℎ), while each client knows its own channel h. Due to the lack of CSI, the BS applies layered division multiplexing (LDM) [17] with layers, or sub-messages, in order to enable differential quality of service at the clients. The transmitted signal x( ) in (1) is accordingly given as…”

“…A first challenge in developing iterative solutions to problem (28), is that the partial derivative of the indicator in the achievable rate expression (6) with respect to vector ; G, ( −1) ) (defined in (39)) 1) ; G, ( −1) ) (defined in (40))…”

“…Related Work: LDM, also known as the broadcast approach, has been extensively studied as means to improve spectral efficiency in various scenarios. A comprehensive survey of the state-of-the-art is available in [1], and we mention here some representative examples. The broadcast approach for slowly fading single-user channels was investigated in [17], where it was shown that transmitting multiple layers can increase the expected achievable rate.…”

Distributed Computations with Layered Resolution

“…To the best of our knowledge, the previous distributed coded systems, while reducing the delay and uncertainty, consider only a single delay factor which indicates the time it takes from the job arrival until when the fusion node receives enough task results to release the final result. For data communications, layering has been already considered under multi-level delay or quality constraint using broadcast approach [23]- [28] and in the classical notion of distributed CEO problem [29], where delivery of different distortion (resolutions) is studied [30]. However, layering has been rarely investigated in the realm of distributed computations.…”

Learning to Broadcast with Layered Division Multiplexing

“…Layered division multiplexing (LDM) has been introduced in several standards as an effective means to support differential quality-of-service (QoS) in broadcast and multicast services. With LDM, multiple independent sub-messages, or layers, are superimposed, enabling the decoding of a different number of messages depending on the channel conditions, thus supporting communication at a variable rate [1]- [4]. The most common use of LDM is for multimedia broadcast, as adopted by the Advanced Television Systems Committee (ATSC 3.0) [4], [5], in which LDM supports a robust configuration for mobile receivers and a high-capacity connection for fixed receivers.…”