Po L. Tien

Multiple access control with intelligent bandwidth allocation for wireless ATM networks

Two major challenges pertaining to wireless asynchronous transfer mode (ATM) networks are the design of multiple access control (MAC), and dynamic bandwidth allocation. While the former draws more attention, the latter has been considered nontrivial and remains mostly unresolved. We propose a new intelligent multiple access control system (IMACS) which includes a versatile MAC scheme augmented with dynamic bandwidth allocation, for wireless ATM networks. IMACS supports four types of traffic-CBR, VBR, ABR, and signaling control (SCR). It aims to efficiently satisfy their diverse quality-of-service (QoS) requirements while retaining maximal network throughput. IMACS is composed of three components: multiple access controller (MACER), traffic estimator/predictor (TEP), and intelligent bandwidth allocator (IBA). MACER employs a hybrid-mode TDMA scheme, in which its contention access is based on a new dynamic-tree-splitting (DTS) collision resolution algorithm parameterized by an optimal splitting depth (SD). TEP performs periodic estimation and on-line prediction of ABR self-similar traffic characteristics based on wavelet analysis and a neural-fuzzy technique. IBA is responsible for static bandwidth allocation for CBR/VBR traffic following a closed-form formula. In cooperation with TEP, IBA governs dynamic bandwidth allocation for ABR/SCR traffic through determining the optimal SD. The optimal SDs under various traffic conditions are postulated via experimental results, and then off-line constructed using a back propagation neural network (BPNN), being used on-line by IBA. Consequently, with dynamic bandwidth allocation, IMACS offers various QoS guarantees and maximizes network throughput irrelevant to traffic variation.

Intelligent video smoother for multimedia communications

Multimedia communications often require intramedia synchronization for video data to prevent potential playout discontinuity resulting from network delay variation (jitter) while still achieving satisfactory playout throughput. In this paper, we propose a neural network (NN) based intravideo synchronization mechanism, called the intelligent video smoother (IVS), operating at the application layer of the receiving end system. The IVS is composed of an NN traffic predictor, an NN window determinator, and a window-based playout smoothing algorithm. The NN traffic predictor employs an on-line-trained back-propagation neural network (BPNN) to periodically predict the characteristics of traffic modeled by a generic interrupted Bernoulli process (IBP) over a future fixed time period. With the predicted traffic characteristics, the NN window determinator determines the corresponding optimal window by means of an off-line-trained BPNN in an effort to achieve a maximum of the playout quality (Q) value. The window-based playout smoothing algorithm then dynamically adopts various playout rates according to the window and the number of packets in the buffer. Finally, we show that via simulation results and live video scenes, compared to two other playout approaches, IVS achieves high-throughput and low-discontinuity playout under a mixture of IBP arrivals.

Intelligent voice smoother for VBR voice over ATM networks

For distinctively transporting voice data with silence suppression over asynchronous transfer mode (ATM) networks via the variable bit rate (VBR) service, the problem of jitter introduced from the network often renders the speech unintelligible. It is thus indispensable to offer intramedia synchronization to remove jitter while retaining minimal playout delay. We propose a neural-network-based intra-voice synchronization mechanism, called the intelligent voice smoother (IVoS). The IVoS is composed of three components: smoother buffer, neural network (NN) traffic predictor, and constant bit rate (CBR) enforcer. Newly arriving frames, being assumed to follow a generic Markov modulated Bernoulli process (MMBP), are queued in the smoother buffer. The NN traffic predictor employs an on-line-trained backpropagation neural network (BPNN) to predict three traffic characteristics of every newly encountered talkspurt period. Based on the predicted characteristics, the CBR enforcer derives an adaptive buffering delay by means of a near-optimal, simple, closed-form formula. It then imposes such delay on the playout of the first frame in the talkspurt period. The CBR enforcer in turn regulates CBR-based departures for the remaining frames of the talkspurt, aimed at assuring minimal mean and variance of distortion of talkspurts (DOT) and mean playout delay (PD). Simulation results reveal that, compared to three other playout approaches, the IVoS achieves superior playout yielding negligible DOT and PD irrespective of traffic variation.

Neural-network-based call admission control in ATM networks with heterogeneous arrivals

Call admission control (CAC) has been accepted as a potential solution for supporting diverse, heterogeneous traffic sources demanding different quality of services in asynchronous transfer mode (ATM) networks. Besides, CAC is required to consume a minimum of time and space to make call acceptance decisions. We present an efficient neural-network-based CAC (NNCAC) mechanism for ATM networks with heterogeneous arrivals. All heterogeneous traffic calls are initially categorized into various classes. Based on the number of calls in each class, the NNCAC efficiently and accurately estimates the cell delay and cell loss ratio of each class in real time by means of a pre-trained neural network. According to our decent study which exhibits the superiority of the employment of analysis-based training data over simulation-based data, we particularly construct the training data from heterogeneous-arrival dual-class queueing model M/sup [N1]/+I/sup [N2]//D/1/K, where M and I represent the Bernoulli process and interrupted Bernoulli process, and N/sub 1/ and N/sub 2/ represent the corresponding numbers of calls, respectively. Analytic results of the queueing model are confirmed by simulation results. Finally, we demonstrate the profound agreement of our neural-network-based estimated results with analytic results, justifying the viability of our NNCAC mechanism.

HOPSMAN: An Experimental Optical Packet-Switched Metro WDM Ring Network with High-Performance Medium Access Control

The paper presents the design and experimentation of a high-performance optical packet-switched metro WDM ring network (HOPSMAN). Equipped with novel medium access control, HOPSMAN achieves superior bandwidth efficiency, access delay, fairness, and bursty traffic adaptation.

A contention access protocol with dynamic bandwidth allocation for wireless ATM networks

We propose a new contention access protocol (CAP) augmented with a dynamic bandwidth allocator (DBA) for wireless ATM networks supporting ABR and signaling control (SCR) traffic. CAP incorporates a dynamic tree-splitting collision resolution algorithm parameterized by an optimal splitting depth (SD). DBA performs estimation and on-line prediction of ABR self-similar traffic characteristics. It in turn determines the optimal SD per frame, satisfying ABR throughput and SCR blocking probability requirements while retaining maximal aggregate throughput. Simulation results postulate the optimal SDs under various ABR and SCR traffic conditions. These results are then off-line trained and constructed by a backpropagation neural network (BPNN), which is used on-line for optimal bandwidth allocation.

A novel intra-media synchronization mechanism for multimedia communications

Multimedia communications often require intra-media synchronization for video data to prevent potential playout discontinuity resulting from network delay variation (jitter) while still achieving satisfactory playout throughput. We propose a neural-network-based intra-media synchronization mechanism, called neural network smoother (NNS). The NNS is composed of a neural network (NN) traffic predictor, an NN window determinator, and a window-based playout smoothing algorithm. The NN traffic predictor employs an on-line-trained backpropagation neural network (BPNN) to periodically predict future traffic characteristics. With the predicted traffic characteristics, the NN window determinator determines the corresponding optimal window by means of an off-line-trained BPNN in an effort to achieve a maximum of the playout quality (Q) value. The window-based playout smoothing algorithm then dynamically adopts various playout rates according to the window and the number of packets in the buffer. Compared to two other playout approaches, simulation results show that NNS achieves high-throughput and low-discontinuity playout under a variety of traffic arrivals.

Intra-media synchronization for multimedia communications

Multimedia communications require intra-media synchronization for video data to prevent potential playout discontinuity resulting from network delay variation (jitter) while still achieving satisfactory playout throughput. In this paper, we propose a neural-network-based intra-media synchronization mechanism, called neural network smoother (NNS). NNS is composed of a neural network (NN) traffic predictor, an NN window determinator, and a window-based playout smoothing algorithm. The NN traffic predictor employs an online-trained backpropagation neural network (BPNN) to periodically predict future traffic characteristics. The NN window determinator determines the corresponding optimal window by means of an off-line-trained BPNN in an effort to achieve a maximum of the playout quality value. According to the window, the window-based playout smoothing algorithm then dynamically adopts various playout rates. Compared to two other playout approaches, simulation results show that NNS achieves high-throughput and low-discontinuity playout under a variety of traffic arrivals.