Channel allocation schemes

About: Channel allocation schemes is a research topic. Over the lifetime, 10656 publications have been published within this topic receiving 182117 citations.

Call set-up in a radio communication system with dynamic channel allocation

Abstract: The call set-up technique of this invention is characterized by the use of channel information from both base station and subscriber terminal in determining the radio traffic channel upon which to set-up a new call. Communication between the base station and subscriber terminal is carried out on a signalling channel until the traffic channel is chosen. Calls are set-up so that they proceed on the radio channel which, of a set of channels under consideration by the subscriber terminal, contains the least amount of interference as measured at the subscriber terminal. The set of channels under consideration by the subscriber terminal is a subset of the entire set of channels allocated to the service. This subset is comprised of those channels having little interference, as measured by the base station. The call set-up technique thereby assures that calls proceed on channels containing little interference from the viewpoints of both base station and subscriber terminal.

Fast switching of forward link in wireless system

Abstract: A technique for distributing channel allocation information in a demand access communication system. Multiple access codes are used that have a defined code repeat period or code epoch. For each such epoch duration, a schedule of assignment of traffic channels to active terminals for each epoch is determined. For each terminal designated as active during the epoch, a list of active channels for such terminal unit is assigned. Prior to the start of each epoch, a channel set up message is sent on one of the forward link channels, such as a paging channel, indicating the lists of active channels for epochs of the associated traffic channel(s) that are to follow.

Cognitive Spectrum Access for Underwater Acoustic Communications

Abstract: While very successful in traditional radio communications, the usage of TDMA and CSMA schemes for underwater acoustic communications is severely limited in efficiency and scalability, primarily due to the very large propagation delays. FDMA seems a viable alternative in that the propagation delay does not impact significantly its efficiency. However, in underwater communications, the capacity achievable on a particular channel depends strongly both on its frequency and on the communication distance, unlike in traditional radio transmissions where FDMA channels usually have comparable performance. Therefore, fixed channel allocation schemes traditionally used for radio FDMA do not perform well in underwater communications. In this paper, we investigate the application of the principles of cognitive radio and dynamic spectrum access to underwater communications. In particular, we propose a channel allocation scheme which exploits user location knowledge in order to maximize the minimum channel capacity among those achieved by the users. This provides maximum fairness and makes a more efficient use of the available spectrum resources. Performance evaluation carried out by means of simulation shows that our approach can achieve a great improvement in fairness among users, with respect to fixed allocation schemes, while at the same time scaling much better and thus allowing effective communications over larger distances.

A Deep Reinforcement Learning Based Approach for Energy-Efficient Channel Allocation in Satellite Internet of Things

Abstract: Recently, Satellite Internet of Things (SIoT), a space network that consists of numerous Low Earth Orbit (LEO) satellites, is regarded as a promising technique since it is the only solution to provide 100% global coverage for the whole earth, without any additional terrestrial infrastructure supports. However, compared with Geostationary Earth Orbit (GEO) satellites, the LEO satellites always move very fast to cover an area within only 5-12 minutes per pass, bringing high dynamics to the network access. Furthermore, to reduce the cost, the power and spectrum channel resources of each LEO satellite are very limited, i.e., less than 10% of GEO. Therefore, to take fully advantage of the limited resource, it is very challenging to have an efficient resource allocation scheme for SIoT. Current resource allocation schemes for satellites are mostly designed for GEO, and these schemes do not consider many LEO specific concerns, including the constrained energy, the mobility characteristic, the dynamics of connections and transmissions etc. Towards this end, we proposed DeepCA, a novel reinforcement learning based approach for energy-efficient channel allocation in SIoT. In DeepCA, we firstly introduce a new sliding block scheme to facilitate the modeling of dynamic feature of the LEO satellite, and formulate the dynamic channel allocation problem in SIoT as a Markov decision process (MDP). We then propose a deep reinforcement learning algorithm for optimal channel allocation. To accelerate the learning process of DeepCA, we utilize the image form to represent the requests of users to reduce the input size, and carefully divide an action into multiple mini-actions to reduce the size of the action set. Extensive simulations show that our proposed DeepCA approach can save at least 67.86% energy consumption compared with traditional algorithms.

Assembly, and associated method, for facilitating frequency allocations in a radio communication system to attain statistical spreading of electromagnetic energy

Abstract: Apparatus, and an associated method, by which to facilitate frequency channel allocation, and reallocation, in a radio communication system. Channel allocation and reallocation is effectuated to attain a desired statistical emission spectrum. Implementation is effectuated, for instance, in a WLAN system operable pursuant to the IEEE 802.11 standard but implemented in a 5 GHz frequency band.