Adaptive Dijkstra’s Search Algorithm for MIMO detection (original) (raw)

2022, International journal of electrical and computer engineering systems

Employing Maximum Likelihood (ML) algorithm for signal detection in a large-scale Multiple-Input- Multiple-Output (MIMO) system with high modulation order is a computationally expensive approach. In this paper an adaptive best first search detection algorithm is proposed. The proposed Adaptive Dijkstra’s Search (ADS) algorithm exploits the resources available in the search procedure to reduce the required number of nodes to be visited in the tree. A tunable parameter is used to control the number of the best possible candidate nodes required. Unlike the conventional DS, the ADS algorithm results in signal detection with low computation complexity and quasi-optimal performance for systems under low and medium SNR regimes. Simulation results demonstrate a 25% computational complexity reduction, compared to the conventional DS.

IJERT-Comparative Study and Complexity Analysis of Signal Detection in MIMO

International Journal of Engineering Research and Technology (IJERT), 2015

https://www.ijert.org/comparative-study-and-complexity-analysis-of-signal-detection-in-mimo https://www.ijert.org/research/comparative-study-and-complexity-analysis-of-signal-detection-in-mimo-IJERTV4IS010020.pdf There is ever growing demand of wireless services of higher data rates to increase the system capacity and spectral Efficiency but as we know wireless transmission is impaired by signal fading and interference. Also a conventional single input single output (SISO) system where the transmitter and receiver are equipped with single antenna could have limitations to support higher data services. The increasing requirement on data rate and quality of service for wireless system calls for new techniques to increase spectrum efficiency and the link reliability. The use of multiple antennas at both the ends of wireless link promises significant improvement in terms of spectrum efficiency and link reliability. This technology is known as multiple input and multiple output (MIMO) wireless system. MIMO offers diversity gain(spatial diversity) and increases the data rate by transmitting several information stream in parallel at the same transmit power. However, spatial demultiplexing or signal detection at the receiver side is a challenging task for Spatially Multiplex MIMO(SM MIMO) systems. To achieve this, wide range of algorithms offering various trade off between performance and computational complexities have been discussed by various researchers over the last decade. This paper gives the development of the various signal detection algorithms for SM MIMO , their performance and associated complexity.

Low Complexity Near-ML Detection for MIMO- OFDM System

— A low complexity M-algorithm based multiple-input multiple-output (MIMO) tree search algorithm with near maximum likelihood (ML) performance is proposed in this paper. Numerical examples show that our tree search algorithm is able to provide a significant performance gain over the MMSE detection. Based on this algorithm, a fully pipelined architecture is presented for the MIMO orthogonal frequency division multiplexing (OFDM) systems. The throughput for a 4x4 MIMO-OFDM IEEE 802.11n system with 64-QAM is 312 Mbps.

Practical aspects of preprocessing techniques for K-Best tree search MIMO detectors

Computers & Electrical Engineering, 2011

It has been shown in several works that some preprocessing techniques can improve data detection performance when they are applied to the channel matrix of MIMO wireless systems. In particular, these techniques can be used previously to K-Best tree search algorithms, and they are known to achieve successful results. Throughout this work, the performance and complexity of two preprocessing techniques

A Survey of VLSI Implementations of Tree Search Algorithms for MIMO Detection

Multiple-input multiple-output (MIMO) detection algorithms have received considerable research interest in recent years, as a result of the increasing need for high data-rate communications. Detection techniques range from the low-complexity linear detectors to the maximum likelihood detector, which scales exponentially with the number of transmit antennas. In between these two extremes are the tree search (TS) algorithms, such as the popular sphere decoder, which have emerged as attractive choices for implementing MIMO detection, due to their excellent performance-complexity trade-offs. In this paper, we survey some of the state-of-the-art VLSI implementations of TS algorithms and compare their results using various metrics such as the throughput and power consumption. We also present notable contributions that have been made in the last three decades in implementing TS algorithms for MIMO detection, especially with respect to achieving low complexity, high throughput designs. Finally, a number of design considerations and trade-offs for implementing MIMO detectors in hardware are presented. Keywords MIMO detection algorithms · sphere decoding · survey · wireless communications · very large scale integration (VLSI)

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