Cognitive radio systems offer the opportunity to improve the spectrum utilization by detecting unused frequency bands while avoiding interference to primary users. This paper proposes a new algorithm for spectrum sensing, which is an energy detector using a hybrid (adaptive and fixed) threshold, in order to compensate the weak points of the existing energy detector in the distorted communication channel environment. Simulation results are presented which show that the performance of the new proposed scheme is better than the existing scheme using a fixed threshold or an adaptive threshold. Additionally, the performance is investigated in terms of several parameters such as the mobile speed and the probability of false alarms. The simulation results also show that the proposed algorithm makes the detector highly robust against fading, shadowing, and interference.

Current literature on input buffer management reveals that, in representative ATM networks under highly bursty traffic conditions, the fuzzy thresholding approach yields lower cell loss rate at the cost of lower throughput. Also, under less bursty traffic, the traditional fixed thresholding approach achieves higher throughput at the expense of higher cell loss rate. The integration of these two properties into practice is termed adaptive dynamic buffer management (ADBM) approach for input buffers and its assessment is the objective of this paper. The argument is that, given that the traffic conditions are constantly changing, to achieve efficiency during actual operation, the network control must dynamically switch, at every ATM switch, under the call processor's control, between the two input buffer management techniques, dictated by the nature of the traffic at the inputs of the corresponding switch. The need to involve the call processor marks the first effort in the literature to dynamically configure input buffer management architectures at the switch fabric level under higher level call processor control. It stems from the fact that the switch fabric operates very fast and cannot engage in complex decision making without incurring stiff penalty. To achieve this goal, the network control needs knowledge of the burstiness of the traffic at the inputs of every ATM switch. The difficulties with this need are two-fold. First, it is not always easy to obtain the traffic model and model parameters for a specific user's call. Second, even where the traffic model and the model parameters are known for a specific user's call, this knowledge is valid only at the source switch where the user interfaces with the network. At all other switches in the network, the cells of the traffic in question interact asynchronously with the cells from other traffic sources and are subject to statistical multiplexing. Thus, to obtain the exact nature of the composite traffic at the inputs of any ATM switch, is a challenge. Conceivably, one may determine the burstiness by counting the number of cells incurred at the inputs of an ATM switch over a defined time interval. The challenge posed by this proposition lies in the very definition of burstiness in that the time interval must approach, in the limit, zero or the resolution of time in the network. To address this challenge, first, a 15-node representative ATM network is modeled in an asynchronous, distributed simulator and, second, simulated on a network of workstations under realistic traffic stimuli. Third, burstiness indices are measured for the synthetic, stochastic traffic at the inputs of every ATM switch as a function of the progress of simulation for different choices of time interval values, ranging from 20,000 timesteps down to 1,000 timesteps. A timestep equals 2.73 µs. Results reveal that consistent burstiness indices are obtained for interval choices between 1,000 and 5,000 timesteps and that a burstiness index of 25, measured at 3,000 timestep interval, constitutes a reasonable and practical threshold value that distinguishes highly bursty traffic that warrants the use of the fuzzy thresholding approach from less bursty traffic that can benefit from the fixed thresholding scheme. A comparative performance analysis of ADBM yields the following. For pure fixed and pure fuzzy thresholding schemes, the throughputs are at 73.88% and 71.53% while the cell drop rates are at 4.31% and 2.44%,respectively. For the ADBM approach, where the input buffer management alternates at each individual ATM switch between the fixed and fuzzy schemes, governed by measured burstiness index threshold of 25 for a 3,000 timestep interval, the throughput is 74.77%, which is higher than even the pure fixed scheme while the cell drop rate is 2.21% that is lower than that of the pure fuzzy scheme. In essence, ADBM successfully integrates the best characteristics of the fuzzy and fixed thresholding schemes.