Information delivery through broadcasting in satellite communication networks

Satellite broadcast is an important candidate for large-scale multimedia information distribution due to the inherent wide-range multicasting capability of satellites and the asymmetry of satellite communications (high bandwidth downlink, limited bandwidth uplink) that matches nicely the information flow asymmetry in multimedia applications. We consider a data broadcasting model that is encountered in most asymmetric satellite communication environments. The problem of scheduling the data broadcast such that average response time experienced by the users is low is considered. In a push-based system, where the users cannot place requests directly to the server and the broadcast schedule should be determined based solely on the access probabilities, we formulate a deterministic dynamic optimization problem, the solution of which provides the optimal broadcast schedule. Properties of the optimal solution are obtained and then we propose a suboptimal dynamic policy which achieves average response time close to the lower bound. In a pull-based system where the users may place requests about information items directly to the server, the scheduling can be based on the number of pending requests for each item. Suboptimal policies with good performance are obtained in this case as well. If a user has local memory, it can alleviate its access latency by selectively prefetching the items from the broadcast and storing them in the memory. A good memory management strategy can substantially reduce the user's access latency. An optimal memory management policy is identified, that minimizes the expected aggregate latency. Memory update strategies with limited look-ahead are presented as implementable approximations of the optimal policy as well. We also consider the problem of joint broadcast scheduling and user's cache management and propos a joint optimization scheme which can achieve the performance up to 40% better than the existing non-joint approach.     

Broadcast scheduling for information distribution

Broadcast data delivery is encountered in many applications where there is a need to disseminate information to a large user community in a wireless asymmetric communication environment. In this paper, we consider the problem of scheduling the data broadcast such that average response time experienced by the users is low. In a push-based system, where the users cannot place requests directly to the server and the broadcast schedule should be determined based solely on the access probabilities, we formulate a deterministic dynamic optimization problem, the solution of which provides the optimal broadcast schedule. Properties of the optimal solution are obtained and then we propose a suboptimal dynamic policy which achieves average response time close to the lower bound. The policy has low complexity, it is adaptive to changing access statistics, and is easily generalizable to multiple broadcast channels. In a pull-based system where the users may place requests about information items directly to the server, the scheduling can be based on the number of pending requests for each item. Suboptimal policies with good performance are obtained in this case as well. Finally, it is demonstrated by a numerical study that as the request generation rate increases, the achievable performance of the pull- and push-based systems becomes almost identical.     

Optimal memory management strategies for a mobile user in abroadcast data delivery system

Data broadcasting has been considered as a promising way of disseminating information to a massive number of users in a wireless communication environment. In a broadcast data delivery system, there is a server which is broadcasting data to a user community. Due to the lack of communication from the users to the server, the server cannot know what a user needs. In order to access a certain item, a user has to wait until the item appears in the broadcast. The waiting time will be considerably long if the server's broadcast schedule does not match the user's access needs. If a user has a local memory, it can alleviate its access latency by selectively prefetching the items from the broadcast and storing them in the memory. A good memory management strategy can substantially reduce the user's access latency, which is a major concern in a broadcast data delivery system. An optimal memory management policy is identified that minimizes the expected aggregate latency. We present optimal memory update strategies with limited look ahead as implementable approximations of the optimal policy. Some interesting special cases are given for which the limited look-ahead policies are optimal. We also show that the same formulation can be used to find the optimal memory management policy which minimizes the number of deadline misses when users generate information requests which have to be satisfied within some given deadlines     

Automatic segmentation and identification of mixed-language speech using delta-BIC and LSA-based GMMs

This paper proposes an approach to segmenting and identifying mixed-language speech. A delta Bayesian information criterion (delta-BIC) is firstly applied to segment the input speech utterance into a sequence of language-dependent segments using acoustic features. A VQ-based bi-gram model is used to characterize the acoustic-phonetic dynamics of two consecutive codewords in a language. Accordingly the language-specific acoustic-phonetic property of sequence of phones was integrated in the identification process. A Gaussian mixture model (GMM) is used to model codeword occurrence vectors orthonormally transformed using latent semantic analysis (LSA) for each language-dependent segment. A filtering method is used to smooth the hypothesized language sequence and thus eliminate noise-like components of the detected language sequence generated by the maximum likelihood estimation. Finally, a dynamic programming method is used to determine globally the language boundaries. Experimental results show that for Mandarin, English, and Taiwanese, a recall rate of 0.87 for language boundary segmentation was obtained. Based on this recall rate, the proposed approach achieved language identification accuracies of 92.1% and 74.9% for single-language and mixed-language speech, respectively.