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Register a new userGeneralized Cut-Set Bounds for Broadcast Networks
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Added by
Tie Liu
Publication date
2013
fields
Informatics Engineering
and research's language is
English
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A broadcast network is a classical network with all source messages collocated at a single source node. For broadcast networks, the standard cut-set bounds, which are known to be loose in general, are closely related to union as a specific set operation to combine the basic cuts of the network. This paper provides a new set of network coding bounds for general broadcast networks. These bounds combine the basic cuts of the network via a variety of set operations (not just the union) and are established via only the submodularity of Shannon entropy. The tightness of these bounds are demonstrated via applications to combination networks.
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A class of diamond networks is studied where the broadcast component is orthogonal and modeled by two independent bit-pipes. New upper and lower bounds on the capacity are derived. The proof technique for the upper bound generalizes bounding techniques of Ozarow for the Gaussian multiple description problem (1981) and Kang and Liu for the Gaussian diamond network (2011). The lower bound is based on Martons coding technique and superposition coding. The bounds are evaluated for Gaussian and binary adder multiple access channels (MACs). For Gaussian MACs, both the lower and upper bounds strengthen the Kang-Liu bounds and establish capacity for interesting ranges of bit-pipe capacities. For binary adder MACs, the capacity is established for all ranges of bit-pipe capacities.
In this work, we propose an adaptive set-membership constant modulus (SM-CM) algorithm with a generalized sidelobe canceler (GSC) structure for blind beamforming. We develop a stochastic gradient (SG) type algorithm based on the concept of SM filtering for adaptive implementation. The filter weights are updated only if the constraint cannot be satisfied. In addition, we also propose an extension of two schemes of time-varying bounds for beamforming with a GSC structure and incorporate parameter and interference dependence to characterize the environment which improves the tracking performance of the proposed algorithm in dynamic scenarios. A convergence analysis of the proposed adaptive SM filtering techniques is carried out. Simulation results show that the proposed adaptive SM-CM-GSC algorithm with dynamic bounds achieves superior performance to previously reported methods at a reduced update rate.
In this paper, we investigate the transmission delay of cache-aided broadcast networks with user cooperation. Novel coded caching schemes are proposed for both centralized and decentralized caching settings, by efficiently exploiting time and cache resources and creating parallel data delivery at the server and users. We derive a lower bound on the transmission delay and show that the proposed centralized coded caching scheme is emph{order-optimal} in the sense that it achieves a constant multiplicative gap within the lower bound. Our decentralized coded caching scheme is also order-optimal when each users cache size is larger than the threshold $N(1-sqrt[{K-1}]{ {1}/{(K+1)}})$ (approaching 0 as $Kto infty$), where $K$ is the total number of users and $N$ is the size of file library. Moreover, for both the centralized and decentralized caching settings, our schemes obtain an additional emph{cooperation gain} offered by user cooperation and an additional emph{parallel gain} offered by the parallel transmission among the server and users. It is shown that in order to reduce the transmission delay, the number of users parallelly sending signals should be appropriately chosen according to users cache size, and alway letting more users parallelly send information could cause high transmission delay.
We study noisy broadcast networks with local cache memories at the receivers, where the transmitter can pre-store information even before learning the receivers requests. We mostly focus on packet-erasure broadcast networks with two disjoint sets of receivers: a set of weak receivers with all-equal erasure probabilities and equal cache sizes and a set of strong receivers with all-equal erasure probabilities and no cache memories. We present lower and upper bounds on the capacity-memory tradeoff of this network. The lower bound is achieved by a new joint cache-channel coding idea and significantly improves on schemes that are based on separate cache-channel coding. We discuss how this coding idea could be extended to more general discrete memoryless broadcast channels and to unequal cache sizes. Our upper bound holds for all stochastically degraded broadcast channels. For the described packet-erasure broadcast network, our lower and upper bounds are tight when there is a single weak receiver (and any number of strong receivers) and the cache memory size does not exceed a given threshold. When there are a single weak receiver, a single strong receiver, and two files, then we can strengthen our upper and lower bounds so as they coincide over a wide regime of cache sizes. Finally, we completely characterise the rate-memory tradeoff for general discrete-memoryless broadcast channels with arbitrary cache memory sizes and arbitrary (asymmetric) rates when all receivers always demand exactly the same file.
This paper reviews 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 systems ergodic behavior. However, when the variations are infrequent, their temporal average can deviate significantly from the channels ergodic mode, rendering a lack of instantaneous performance guarantees. To circumvent a lack of short-term guarantees, the {em 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.
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