A new GSM data protocol called high speed circuit switched data (HSCSD) have been developed by European Telecommunications Standards Institute (ETSI) for high speed file transfer and mobile video applications. HSCSD increases data rate by using multiple TDMA time slots (up to 8) instead of one time slot in the current GSM implementation. The problem of multiple time slot assignment is that blocking rate of the system will increase. This problem can be solved by flexible resource assignment where the service specifies the maximum and the minimum capacity. Based on the current available capacity of a base station, a user will be assigned any rate between the maximum and the minimum capacities. This article describes HSCSD protocol and presents four radio resource allocation strategies for HSCSD: always allocates maximum, always allocates minimum, allocates maximum unless available resources are not enough, and allocates resources according to the current blocking statistics of the base station. A simulation model is proposed to investigate the performance of these algorithms. The blocking probability, the call completion probability, and the quality of service are used to evaluate the effects of algorithms in different system behaviors.

In GSM High Speed Circuit Switched Data (HSCSD), the data rate can be increased by using multiple time slots instead of single time slot. Multiple time-slot assignment results in high blocking rate. To accommodate more users, flexible resource allocation strategies have been proposed. Since GSM follows TDMA/FDMA, the channels (time slots) in a base station are segmented by frequency carriers. The base station must allocate the channels which belong to the same frequency carrier to individual requests. This Flexible Resource Allocation scheme for GSM (FRA-GSM) is contrastive to the scheme proposed in our previous studies where a base station may arbitrarily allocate idle channels in the base station to incoming requests. We define satisfaction indication SI as the measurement to compare the performance of these schemes. Experiment results indicate that FRA-GSM scheme has good performance when the user mobility is high, or when some cost factors are taken into account.

Cellular Digital Packet Data (CDPD) provides wireless data communication services to mobile users by sharing unused RF channels with AMPS on a non-interfering basis. To prevent interference on the voice activities, CDPD makes forced hop to a channel stream when a voice request is about to use the RF channel occupied by the channel stream. The number of forced hops is affected by the voice channel selection policy. We propose analytic models to investigate the CDPD channel holding time for the the least-idle and random voice channel selection policies. Under various system parameters and voice channel selection policies, we provide guidelines to reduce the number of forced hops.

As a consequence of recent developments in communications, the parameters of communication signals, such as the modulation parameter values, are becoming unstable because of time-varying SNR under electromagnetic conditions. In general, it is difficult to classify target signals that have time-varying parameters using traditional signal recognition methods. To overcome this problem, this study proposes a novel recognition method that works well even for such time-dependent communication signals. This method is mainly composed of feature extraction and classification processes. In the feature extraction stage, we adopt Shannon entropy and index entropy to obtain the stable features of modulated signals. In the classification stage, the interval gray relation theory is employed as suitable for signals with time-varying parameter spaces. The advantage of our method is that it can deal with time-varying SNR situations, which cannot be handled by existing methods. The results from numerical simulation show that the proposed feature extraction algorithm, based on entropy characteristics in time-varying SNR situations,offers accurate clustering performance, and the classifier, based on interval gray relation theory, can achieve a recognition rate of up to 82.9%, even when the SNR varies from -10 to -6 dB.