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![Figure 3.1: DFG representation of the computation y[k] = x[k] + ay[k − 1].](profile/Micaela-Troglia-Gamba/publication/281472406/figure/fig3/AS:669383554777125@1536604884984/DFG-representation-of-the-computation-yk-xk-ayk-1_Q320.jpg)
Multiple Input Multiple Output (MIMO) systems are recognized as a key enabling technology in high performance wireless communications. However the complexity of high throughput MIMO detectors poses a serious implementation issue. Among known MIMO detectors, Sphere Decoder Algorithm (SDA) has emerged to reduce the processing complexity, with respect...
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![Table 3 The DT-ACMORO SG algorithms 1: Initialization: Generate [ 0]...](publication/322588283/figure/tbl2/AS:668538352193540@1536403372148/The-DT-ACMORO-SG-algorithms-1-Initialization-Generate-0-randomly-with-the-power_Q320.jpg)
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Citations
... This is because allocating a constant SBI value to the list without a counter-hypothesis prevents the incorporation of soft outputs provided by the channel decoder. Therefore, its behavior reduces to a hard output detector [15], [16]. Other techniques have been proposed to solve this problem, including path augmentation and bit flipping methods [17]- [19], yet most of these approaches require high computational complexity. ...
In this paper, we evaluate a number of reduced search detection methods for iterative detection and decoding (IDD) system. We evaluate the basic issues which can restrict the performance of the reduced search detectors in IDD systems. One of the main issues in reduced search detectors is related to missing hypothesis sometimes known as log likelihood ratio (LLR) clipping problem. If a suitable method is not used for missing hypothesis, it can degrade the performance of the reduced search based IDD systems. The solution is provided which can overcome the problem of missing hypothesis. The simulation results show that the proposed method provides substantial performance gain in each MIMO detector iterations.
