This paper proposes a maximum likelihood sequence estimation (MLSE) for the differential space-time block code (DSTBC) in cooperation with blind linear prediction (BLP) of fast frequency-flat fading channels. This method that linearly predicts the fading complex envelope derives its linear prediction coefficients by the method of Lagrange multipliers, and does not require data of decision-feedback or information on the channel parameters such as the maximum Doppler frequency in contrast to conventional ones. Computer simulations under fast fading conditions demonstrate that the proposed method with an appropriate degree of polynomial approximation is superior in BER performance to the conventional method that estimates the coefficients by the RLS algorithm using a training sequence.

This paper proposes low-complexity blind detection for orthogonal frequency division multiplexing (OFDM) systems with the differential space-time block code (DSTBC) under time-varying frequency-selective Rayleigh fading. The detector employs the maximum likelihood sequence estimation (MLSE) in cooperation with the blind linear prediction (BLP), of which prediction coefficients are determined by the method of Lagrange multipliers. Interpolation of channel frequency responses is also applied to the detector in order to reduce the complexity. A complexity analysis and computer simulations demonstrate that the proposed detector can reduce the complexity to about a half, and that the complexity reduction causes only a loss of 1 dB in average Eb/N0 at BER of 10-3 when the prediction order and the degree of polynomial approximation are 2 and 1, respectively.

In this paper we present an algorithm to solve an as-yet untreated knapsack problem, the Multidimensional Multiple-choice Knapsack Problem (MMKP). Since our specific application occurs in the real-time domain, a solution for the MMKP with a small upper bound on the runtime is desirable. Thus, the Lagrange multiplier method is chosen, and a heuristic with a worst-case runtime behavior better than O(n2m) is developed, n being the number of elements and m the number of dimensions. Extensive testing against an exact algorithm based on partial enumeration is used to establish the accuracy and efficiency of the heuristic.