The effect of signals on stability, throughput region, and delay in a two-user slotted ALOHA based random-access system with collisions is considered. This work gives rise to the development of random access G-networks, which can model virus attacks or other malfunctions and introduce load balancing in highly interacting networks. The users are equipped with infinite capacity buffers accepting external bursty arrivals. We consider both negative and triggering signals. Negative signals delete a packet from a user queue, while triggering signals cause the instantaneous transfer of packets among user queues. We obtain the exact stability region, and show that the stable throughput region is a subset of it. Moreover, we perform a compact mathematical analysis to obtain exact expressions for the queueing delay by solving a Riemann boundary value problem. A computationally efficient way to obtain explicit bounds for the queueing delay is also presented. The theoretical findings are numerically evaluated and insights regarding the system performance are derived.
In the field of computer network performance, the RNN has been used to build distributed controllers for quality of service routing in packet networks @cite_32 @cite_28 and in the design of Software Defined Network controllers @cite_0 . Real-time optimised task allocation algorithms in Cloud systems @cite_6 have also been built and tested. Recent applications has addressed the use of the RNN to detect attacks on Internet of Things (IoT) gateways @cite_30 . The simple and decentralized nature of ALOHA protocol @cite_65 made it very popular in multiple access communication systems. The ever increasing need for massive uncoordinated access has increased the interest on random access protocols @cite_63 @cite_79 , which remain an active research area with challenging open problems even for very simple networks @cite_44 @cite_9 .
@cite_30 @cite_28 @cite_9 @cite_65 @cite_32 @cite_6 @cite_0 @cite_79 @cite_44 @cite_63
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Abstract Recurrent Neural Networks (RNNs) have been widely used in natural language processing and computer vision. Amongst them, the Hierarchical Multi-scale RNN (HM-RNN), a recently proposed multi-scale hierarchical RNN, can automatically learn the hierarchical temporal structure from data. In this paper, we extend the work to solve the computer vision task of action recognition. However, in sequence-to-sequence models like RNN, it is normally very hard to discover the relationships between inputs and outputs given static inputs. As a solution, the attention mechanism can be applied to extract the relevant information from the inputs thus facilitating the modeling of the input–output relationships. Based on these considerations, we propose a novel attention network, namely Hierarchical Multi-scale Attention Network (HM-AN), by incorporating the attention mechanism into the HM-RNN and applying it to action recognition. A newly proposed gradient estimation method for stochastic neurons, namely Gumbel-softmax, is exploited to implement the temporal boundary detectors and the stochastic hard attention mechanism. To reduce the negative effect of the temperature sensitivity of the Gumbel-softmax, an adaptive temperature training method is applied to improve the system performance. The experimental results demonstrate the improved effect of HM-AN over LSTM with attention on the vision task. Through visualization of what has been learnt by the network, it can be observed that both the attention regions of the images and the hierarchical temporal structure can be captured by a HM-AN. In this paper, we consider a robust network control problem. We consider linear unstable and uncertain discrete time plants with a network between the sensor and controller and the controller and plant. We investigate the effect of data drop out in the form of packet losses. Four distinct control schemes are explored and sufficient conditions to ensure almost sure stability of the closed loop system are derived for each of them in terms of minimum packet arrival rate and the maximum uncertainty. I. INTRODUCTION In the past decade, networked control systems (NCS) have gained much attention from both the control com- munity and the network and communication community. When compared with classical feedback control system, networked control systems have several advantages. For example, they can reduce the system wiring, make the system easy to operate and maintain and later diagnose in case of malfunctioning, and increase system agility (20). Although NCS have advantages, inserting a network in between the plant and the controller introduces many problems as well. For instance, zero-delayed sensing and actuation, perfect information and synchronization are no longer guaranteed in the new system architecture as only finite bandwidth is available and data packet drops and delays may occur due to network traffic conditions. These must be revisited and analyzed before networked control systems become prevalent. Recently, many researchers have spent effort on these issues and some significant results were obtained and many are in progress. Many of the aforementioned issues are studied separately. Tatikonda (19) and Sahai (13) have presented some interesting results in the area of control under communication constraints. Specifically, Tatikonda gave a necessary and sufficient condition on the channel data rate such that a noiseless LTI system in the closed loop is asymptotically stable. He also gave rate results for stabilizing a noisy LTI system over a digital channel. Sahai proposed the notion of anytime capacity to deal with real time estimation and control for a networked control system. In our paper (17), the authors have considered various rate issues under finite bandwidth, packet drops and finite controls. An optimal bit allocation scheme is given in (16) under the networked setting. The effect of pacekt drops on state estimation was studied by Sinopoli, et. al. in (3). It has further been investigated by many researchers including the present authors in (15) and (6). In this paper, we investigate a novel slotted ALOHA-based distributed access cognitive network in which a secondary user (SU) selects a random subset of channels for sensing, detects an idle (unused by licensed users) subset therein, and transmits in any one of those detected idle channels. First, we derive a range for the number of channels to be sensed per SU access. Then, the analytical average system throughput is attained for cases where the number of idle channels is a random variable. Based on that, a relationship between the average system throughput and the number of sensing channels is attained. Subsequently, a joint optimization problem is formulated in order to maximize average system throughput. The analytical results are validated by substantial simulations. Random Medium-Access-Control (MAC) algorithms have played an increasingly important role in the development of wired and wireless Local Area Networks (LANs) and yet the performance of even the simplest of these algorithms, such as slotted-Aloha, are still not clearly understood. In this paper we provide a general and accurate method to analyze networks where interfering users share a resource using random MAC algorithms. We show that this method is asymptotically exact when the number of users grows large, and explain why it also provides extremely accurate performance estimates even for small systems. We apply this analysis to solve two open problems: (a) We address the stability region of non-adaptive Aloha-like systems. Specifically, we consider a fixed number of buffered users receiving packets from independent exogenous processes and accessing the resource using Aloha-like algorithms. We provide an explicit expression to approximate the stability region of this system, and prove its accuracy. (b) We outline how to apply the analysis to predict the performance of adaptive MAC algorithms, such as the exponential back-off algorithm, in a system where saturated users interact through interference. In general, our analysis may be used to quantify how far from optimality the simple MAC algorithms used in LANs today are, and to determine if more complicated (e.g. queue-based) algorithms proposed in the literature could provide significant improvement in performance.