Search - maximum likelihood mimo

Search - maximum likelihood mimo - List

[Communication-Mobile] SEAL_det_v1.0 DL : 0: A stack-based sequential depth-first decoder that returns Maximum-Likelihood solutions to spherical LAST coded MIMO system-type problems
Date : 2025-07-19 Size : 21kb User : xzl
[matlab] MIMOwithMLequalization DL : 0: 2X2 MIMO系统使用ML（最大似然）接受。（验证可用）-2X2 MIMO system using the ML (maximum likelihood) to accept. (Verification can be used)
Date : 2025-07-19 Size : 1kb User : lynn
[Communication-Mobile] Performance_Analysis_of_Maximum_Likelihood_Detecti DL : 0: 《通信信道处理》课程设计，内容为MIMO系统的性能仿真分析，内含MIMO系统的构建、Rayleigh信道、AWGN干扰、QPSK调制解调等，按照IEEE格式全英书写，请大家多多指教-Performance Analysis of Maximum Likelihood Detection in a MIMO Antenna System.pdf
Date : 2025-07-19 Size : 297kb User : Hongyuan Li
[Communication-Mobile] Jafark_QOSTBC_4by1_rate1.0_QPSK_(1A234)_ML DL : 0: MIMO无线传输无线传输无线传输 -MIMO transmission model, JAKAFANI Quasi orthogonal space time block codes with four transmit and one receive antenans over flat fading channel with maximum likelihood detector (ML)
Date : 2025-07-19 Size : 532kb User : albert
[Communication-Mobile] ber_2x2mimo_ML DL : 0: The results for 2×2 MIMO with Maximum Likelihood (ML) equalization helped us to achieve a performance closely matching the 1 transmit 2 receive antenna Maximal Ratio Combining (MRC) case.
Date : 2025-07-19 Size : 1kb User : Umashankar Dewangan
[Program doc] ML_equalizer_for_STBC_systems DL : 0: It is a program for Maximum likelihood equalization for space time block codes in MIMO systems
Date : 2025-07-19 Size : 7kb User : bunny
[matlab] MLEqforSTBCMIMO DL : 0: Maximum Likelihood equalization for STBC-MIMO systems
Date : 2025-07-19 Size : 634kb User : kio
[Algorithm] matlab DL : 0: The main goal of this work is to provide a unified theory of QSTBCs for four transmit antennas and one (or more) receive antennas. The thesis consists of two main parts: In the first part we analyze the QSTBCs transmission without any channel knowledge at the transmitter and in the second part we analyze transmission with QSTBCs assuming partial channel state (CSI) information at the transmitter. For both cases, the QSTBCs are studied on spatially correlated and uncorrelated frequency flat MIMO channels applying a Maximum Likelihood receivers as well as a low complexity linear Zero-Forcing receivers. The spatial correlation is modelled by the so-called Kronecker Model. Measured indoor channels are also used in our simulations to show the performance of the QSTBCs in real-world environment
Date : 2025-07-19 Size : 1kb User : hammad
[Industry research] xinhaojiance DL : 0: 几种 MIMO 最大似然检测算法性能与复杂度比较及改进，介绍了半定松弛、分枝定界和堆栈三种低复杂度最大似然检测算法，并对其性能和复杂度进行了仿真分析，提出了改进的分枝定界和堆栈算法，仿真结果证明分枝定界和堆栈算法性能要优于半定松弛算法，分枝定界算法的复杂度低于堆栈算法且半定松弛算法以多项式复杂度取得了逼近最大似然的性能，同时改进算法加快了算法收敛速度，降低了计算复杂度和对存储空间的要求。 -Several maximum likelihood MIMO detection algorithm performance and complexity comparison and improvement, introduced the semidefinite relaxation, branch and bound, and the stack of three low-complexity maximum likelihood detection algorithm and its performance and complexity of the simulation analysis , proposed an improved branch and bound, and the stack algorithm, simulation results show that the stack algorithm branch and bound and given a better performance than a half relaxation algorithm, branch and bound algorithm complexity is lower than the stack algorithm and the algorithm for semidefinite relaxation polynomial complexity approximation of the maximum likelihood achieved performance, while improving the algorithm to speed up the convergence rate and reduces the computational complexity and storage space requirements.
Date : 2025-07-19 Size : 764kb User : wanglong
[matlab] ML DL : 0: MIMO 最大似然算法代码自己写的仅做参考-MIMO maximum likelihood algorithm code
Date : 2025-07-19 Size : 1kb User : 刘璞
[Communication-Mobile] 1 DL : 0: 多天线正交频分复用(MIMO-OFDM,Multiple-Input and Multiple-Output Orthogonal Frequency Division Multiplex)系统中,精确的符号定时同步可使接收端解调数据时消除载波频率偏移和符号间干扰。而由于MIMO空间信道复杂度较高,使得单天线OFDM系统的同步方法不能直接应用于MIMO-OFDM系统中。提出的两步同步方法首先利用时域导频作为第一步同步进行粗同步,然后利用循环交织前缀作为第二步同步对每一个MIMO-OFDM符号进行同步偏差调整。需要指出的是,两次同步过程均可以产生尖锐的相关峰,使得同步精度大为提高。-Accurate timing synchronization of symbol is important for MIMO-OFDM system,and it can eliminate the integral frequency shift of carrier and intersymbol interference.Two-step timing synchronization method is proposed in this thesis.First,the time-domain pilot is employed to obtain coarse timing synchronization,then the interleaved cyclic prefix and maximum likelihood estimation method are adopted to adjust the synchronization difference,through which the correlational characteristic of that window could be ...
Date : 2025-07-19 Size : 789kb User : 王睿
[matlab] MIMO-OFDM DL : 0: 单用户MIMO以及多用户MIMO的检测程序,包括最大似然检测,最小均方误差检测等的matlab实现，以及MIMO_OFDM主要程序-Single-user MIMO and multi-user MIMO testing procedures, including maximum likelihood detection, minimum mean square error detection matlab realize and the main program of MIMO_OFDM
Date : 2025-07-19 Size : 1.67mb User : 倩倩
[Software Engineering] A-VLSI-8x8-MIMO-Near-ML-Decoder-Engine DL : 0: 8x8 mimo design with near maximum likelihood
Date : 2025-07-19 Size : 297kb User : Manavalan
[Communication-Mobile] MIMO-Detection-Algorithm- DL : 0: 多入多出(MIMO)系统可以获得比单发单收系统更高的容量，对于MIMO系统，最大似然检测是最优接收，但其指数复杂度难以在实际中应用。针对该问题，结合格缩减理论提出了基于Householder变换的复数域格缩减算法，将该算法和 MIMO次优检测算法相结合，给出了量化判决方法，且该复数域格缩减算法复杂度小于实数域格缩减算法。仿真结果表明，基于Householder变换复数域格缩减的MIMO次优检测算法，通过优化信道矩阵可以得到更好的判决域，取得了逼近最优最大似然检测算法的性能。-Multi-input multi-output (MIMO) systems can achieve more capacity than single input single output systems. For MIMO systems, maximum likelihood detection is the optimum detection but its exponential complexity limits its application. In this paper, a complex lattice reduction algorithm based on householder transform is proposed and combined with MIMO suboptimal detection algorithm. Moreover the complexity of this complex lattice reduction algorithm is lower than that of real lattice reduction algorithm. Simulation results show that MIMO suboptimal detection algorithm based on this complex lattice reduction can approach the optimum performance of maximum likelihood detection by optimizing channel matrix to get better decision domain.
Date : 2025-07-19 Size : 893kb User : 蜡笔
[Program doc] robust-MIMO-detection DL : 0: 在多输入多输出系统中的鲁棒球形译码的实现-We present a maximum-likelihood decoding algorithm for an arbitrary lattice code when used over an independent fading channel with perfect channel state information at the receiver. The decoder is based on a bounded distance search among the lattice points falling inside a sphere centered at the receved point. By judicious choice of the decoding radius we show that this decoder can be practically used to decode lattice codes of dimension up to 32 in a fading environement.
Date : 2025-07-19 Size : 143kb User : 应杰
[3G develop] MIMO--with-convolutional-coding DL : 0: VC2010及IT++4.2下实现MIMO的仿真-This example demonstrates how to use the Modulator_ND (MIMO) class for soft-output demodulation. The program simulates a simple convolutionally coded spatial-multiplexing (V-BLAST style) MIMO system with maximum-likelihood, alternatively zero-forcing, demodulation and soft Viterbi decoding, but no iteration between the demodulator and the decoder.
Date : 2025-07-19 Size : 3kb User : raven
[Program doc] MIMO-detection-algorithm DL : 0: MIMO检测算法，联合多种调制方式，仿真对象为相应的误码率和容量等。-Script for computing the BER for BPSK modulation in a Rayleigh fading channel with 2 Tx, 2Rx MIMO channel Maximum Likelihood equalization
Date : 2025-07-19 Size : 18kb User : dongliang
[matlab] ML-equalization-in-MIMO DL : 0: 在平稳瑞利多径信道下，用BPSK调制，用matlab 仿真由最大似然译码的性能。-It discuss a receiver structure called Maximum Likelihood (ML) decoding which gives us a better performance. We will assume that the channel is a flat fading Rayleigh multipath channel and the modulation is BPSK.
Date : 2025-07-19 Size : 2kb User : mingzhan
[Editor] Maximum-Likelihood DL : 0: Maximum likelihood detection for MIMO. all supporting file
Date : 2025-07-19 Size : 159kb User : abinashkaji
[Software Engineering] MIMO DL : 0: Low complexity maximum likelihood MIMO signal detection algorithm
Date : 2025-07-19 Size : 954kb User : 王莫尘

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