KEYWORDS: Orthogonal frequency division multiplexing, Signal detection, Sensors, Detection and tracking algorithms, Reliability, Modulation, Signal to noise ratio, Computing systems, Computer simulations, Receivers
In this paper, a high-dimensional detection problem of single input single output (SISO) spread OFDM system is attempted using low complexity approach. A combination of signal processing procedures is introduced to provide a low complexity detection algorithm with improved performance compared to existing linear detectors such as Minimum Mean Square Error (MMSE). Soft output MMSE is implemented first to provide a reliability measure of all bits in the received vector using an approximated Log Likelihood Ratio (LLR) information. The most reliable bits are deemed correctly received and the least reliable bits undergo Branch and Bound (BB) detection process in a sequential manner. Within this block of unreliable bits, BB starts with the most unreliable bits and then the second most unreliable bits and so on. Simulation results show that the proposed technique provides improved performance with a significant reduction in the computational complexity.
KEYWORDS: Orthogonal frequency division multiplexing, Signal to noise ratio, Signal detection, Receivers, Niobium, Systems modeling, Computer programming, Modulation, Alternate lighting of surfaces, Computer engineering
Symbol spread OFDM technique has been introduced to improve diversity performance of the conventional
OFDM system in the frequency selective fading channel, where, in this technique, every data symbol is mod-
ulated using all OFDM subcarriers. Linear Minimum Mean Square Error (MMSE) equalizer is widely used in
spread OFDM signal detection because of its low complexity compared to optimal equalizers such as Maximum
Likelihood (ML).1 In this paper we introduce turbo equalization based receiver for detecting symbol spread
OFDM signal in which MMSE equalizer and channel decoder exchange soft information in an iterative fashion.
Bit Error Rate (BER) performance is investigated with both full and partial spread scenarios and also with and
without channel decoding. Simulation results show improved performance especially at low SNR regime and
when partially spread OFDM scenario is used.
In this paper, partial spread OFDM system has been presented and its performance has been studied when different
detection techniques are employed, such as minimum mean square error (MMSE), grouped Maximum Likelihood (ML)
and approximated integer quadratic programming (IQP) techniques . The performance study also includes applying two
different spreading matrices, Hadamard and Vandermonde. Extensive computer simulation have been implemented and
important results show that partial spread OFDM system improves the BER performance and the frequency diversity of
OFDM compared to both non spread and full spread systems. The results from this paper also show that partial spreading
technique combined with suboptimal detector could be a better solution for applications that require low receiver
complexity and high information detectability.
KEYWORDS: Orthogonal frequency division multiplexing, Computer programming, Sensors, Computer simulations, Detection and tracking algorithms, Receivers, Optimization (mathematics), Data modeling, Signal to noise ratio, Systems modeling
In this paper we introduce Integer Quadratic Programming (MIQP) approach to optimally detect QPSK Code Spread
OFDM (CS-OFDM) by formulating the problem as a combinatorial optimization problem. The Branch and Bound (BB)
algorithm is utilized to solve this integer quadratic programming problem. Furthermore, we propose combined
preprocessing steps that can be applied prior to BB so that the computational complexity of the optimum receiver is
reduced. The first step in this combination is to detect as much as possible symbols using procedures presented in [9],
which is basically based on the gradient of quadratic function. The second step detects the undetected symbols from the
first step using MMSE estimator. The result of the latter step will be used to predict the initial upper bound of the BB
algorithm. Simulation results show that the proposed preprocessing combination when applied prior to BB provides optimal performance with a significantly reduced computational complexity.
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