On Optimizing the Backoff Interval for Random Access Schemes

To improve the channel throughput and the fairness of random access channels, we propose a new backoff algorithm, namely, the sensing backoff algorithm (SBA). A novel feature of the SBA scheme is the sensing mechanism, in which every node modifies its backoff interval according to the results of the sensed channel activities. In particular, every active node sensing the successful transmission decreases its backoff interval by an additive factor of the transmission time of a packet. In order to find the optimum parameters for the SBA scheme, we have studied the optimum backoff intervals as a function of different number of active nodes (N) in a single transmission area with pure ALOHA-type channels.We have found that the optimum backoff interval should be 4N times the transmission time of a packet when the random access channel operates under a pure ALOHA scheme. Based on this result, we have numerically calculated the optimum values of the parameters for SBA, which are independent of N. The SBA scheme operates close to the optimum backoff interval. Furthermore, its operation does not depend on the knowledge of N. The optimum backoff interval and the SBA scheme are also studied by simulative means. It is shown that the SBA scheme out-performs other backoff schemes, such as binary exponential backoff (BEB) and multiplicative increase linear decrease (MILD). As a point of reference, the SBA scheme offers a channel capacity of 0.19 when N is 10, while the MILD scheme can only offer 0.125. The performance gain is about 50%

Application Protocols enabling Internet of Remote Things via Random Access Satellite Channels

Nowadays, Machine-to-Machine (M2M) and Internet of Things (IoT) traffic rate is increasing at a fast pace. The use of satellites is expected to play a large role in delivering such a traffic. In this work, we investigate the use of two of the most common M2M/IoT protocols stacks on a satellite Random Access (RA) channel, based on DVB-RCS2 standard. The metric under consideration is the completion time, in order to identify the protocol stack that can provide the best performance level

A Simulation Study of a Limited Sensing Random Access Algorithm for a Local Area Network with Voice Users

The purpose of this work is to evaluate the perfonnance of a limited sensing random access algorithm in a local area network with voice users. Random access algorithms have proven to be very efficient in local area network environments with data users. However, in contrast to data packets, voice packets cannot be allowed to experience long delays, because of the requirement that a voice data stream must be played out at the receiver. If a voice packet does exceed its established maximum delay, it is discarded. This simulation study finds the number of voice users that a network can support, provided the packet loss rate that can be tolerated by a customer does not exceed a certain threshold. Finally, a comparison is made with the simulation results of this algorithm with other commonly used protocols

The ARGUS Vertex Trigger

A fast second level trigger has been developed for the ARGUS experiment which recognizes tracks originating from the interaction region. The processor compares the hits in the ARGUS Micro Vertex Drift Chamber to 245760 masks stored in random access memories. The masks which are fully defined in three dimensions are able to reject tracks originating in the wall of the narrow beampipe of 10.5\,mm radius.Comment: gzipped Postscript, 27 page

Compressive Random Access Using A Common Overloaded Control Channel

We introduce a "one shot" random access procedure where users can send a message without a priori synchronizing with the network. In this procedure a common overloaded control channel is used to jointly detect sparse user activity and sparse channel profiles. The detected information is subsequently used to demodulate the data in dedicated frequency slots. We analyze the system theoretically and provide a link between achievable rates and standard compressing sensing estimates in terms of explicit expressions and scaling laws. Finally, we support our findings with simulations in an LTE-A-like setting allowing "one shot" sparse random access of 100 users in 1ms.Comment: 6 pages, 3 figures, published at Globecom 201