This paper describes the state of the art in software frameworks for executing Software Defined Radio (SDR) components. These frameworks are catalyzing drastic changes in signal processing by enabling software engineers and signal processing engineers to work in tandem on core challenges, such as effectively processing large amounts of data in real-time on limited hardware resources. In addition to a historical perspective of this area, we showcase the REDHAWK framework as an example of a modern SDR framework which provides many facilities for distributed SDR deployment.

Analytical expressions for the throughput (in kbit/s) as a function of channel symbol signal-to-noise ratio (E/sub s//N/sub 0/) are derived for the six Bluetooth ACL packets that use automatic repeat request (ARQ). The analysis is exact under the assumptions that the outer CRC code provides perfect error detection and that the channel remains stationary for the duration of each packet. Using an expression for noncoherent correlated (h<0.5) full response FSK signals, numerical results are provided for AWGN and quasi-static Rayleigh fading channels. These curves are an appropriate benchmark against which practical demodulators and custom error control techniques may be compared.

This paper presents alternate methods to generate bit errors that simulate the effect of a convolutional code on an MPEG-2 sequence. Given the complexity of Viterbi decoders, a fast way to generate bit errors that closely resembles the performance of a convolutional code/Viterbi decoder is desirable in order to minimize the execution time of Monte Carlo simulations. Three methods of generating the bit error sequences are presented in this paper. A simulation of a Viterbi decoder is used as a reference against which the proposed methods are compared. A two-random-variable method is presented that closely matches the performance of the simulation of the Viterbi decoder in both BER and PSNR. A simplification of the two-random-variable method is also presented, where only a single random variable is used, and a deterministic form of the behavior of the convolutional code/Viterbi decoder is used. This single-random-variable method yields results that are similar to the two-random-variable method, with comparable values of BER and PSNR. The statistics of the bit error distribution are sacrificed in exchange for a simplified model and less knowledge of the behavior of the Viterbi decoder. The third method proposed is a simplification of the single-random-variable method, where a single bit error is used to model whole bit error bursts. This method yields inaccurate BER estimates but also PSNR values that are biased but close to the behavior of the convolutional code.