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Description: 智能天线自适应非盲波束形成SMI算法，有很好的参考价值。-Smart Antenna Adaptive SMI non-blind beamforming algorithms, have a good reference value.
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Size: 2048 |
Author: 刘秦 |
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Description: 本文推导了MIMO系统，在频域分组调度准则下，SINR分布函数的表达式，以及平均信道容量。-The paper contributions are the derivation
of a mathematical expression of the SINR distribution and the
average channel capacity for multiuser MIMO systems with a
frequency domain packet scheduler.
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Size: 126976 |
Author: lina |
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Description: Abstract—2×2 unitary precoding based on receiver feedback
is applied alongside spatial multiplexing at the base station in
HSDPA (D-TxAA) when the mobile terminal supports MIMO
transmissios [1]. This precoding will influence achievable sumrate
of the MIMO channel if it influences the Signal-to-
Interference-plus-Noise Ratio (SINR) of streams at the receiver
(RX) output. We propose a set of MIMO HSDPA receivers, all
based upon a LMMSE chip-level matrix filter (equalizer) front
end, and introduce the notion of joint bias for the MIMO chip
equalizer. Statistical properties of the spatial model thus obtained
are exploited to analyze the performance of proposed MIMO
receivers. It is shown that precoding choice depends upon the
MIMO receiver and the extent of its impact depends on the
MIMO RX.
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Size: 195584 |
Author: ashish |
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Description: mimo radar SINR plot vs doppler frequency-mimo radar SINR plot vs doppler frequency
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Size: 1024 |
Author: vijay |
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Description: Analysis of frequency and channel estimator for down link of MIMO OFDM system-The analysis should be done based on MATLAB codes, which includes parameters like SNR, SINR
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Size: 435200 |
Author: 崽崽 |
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Description: We present a user-scheduling scheme for multiuser multiple-input–multiple-output (MIMO) systems with random unitary beamforming (RUB) in this paper. The new scheme, which is termed as adaptive beam activation based on the conditional best beam index feedback (ABA-CBBI), requires low average feedback load by imposing a feedback threshold on the users’ signal-to-interference-plus-noise ratio (SINR) and suffers less multiuser interference by only activating those beams requested by at least one user.
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Size: 12401664 |
Author: diwakar pandey |
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Description: The model is built upon the assumption that the probabil-
ity distribution of available destinations among the bu ered frames at
the Base Station (BS) is approximately the same as the probability
distribution of the trac arriving to the BS, this is, the amount of
trac directed to each MN with respect to the total trac load. This
assumption leads to a simple, but accurate, queueing model for Multi-
user MIMO systems that accounts for the impact of a nite number of
active MNs in non-saturated conditions. The model is easily applicable
to any Multi-user MIMO scenario given that the probability density
function of the post-processing SINR (Signal to Interference and Noise
Ratio) for each MN is known.
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Size: 92160 |
Author: ImranKhan |
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Description: k用户MIMO干扰信道干扰对齐算法。包括最小干扰泄露算法，最大信干噪比算法，秩约束秩最小化算法-k-user MIMO interference channel interference alignment algorithm. Including a minimum interference leakage algorithm, the maximum SINR algorithm, constrained rank rank minimization algorithm
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Size: 8192 |
Author: linjingjing |
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Description: Thanks to the small wavelength at millimeter
wave (mmWave) frequency, it is promising to combine massive
multiple-input and multiple-output (MIMO) with mmWave.
MmWave massive MIMO will differ the conventional
massive MIMO, due to the differences in propagation and
hardware constraints. This paper proposes a stochastic geometry
framework for uating the performance in large-scale
mmWave massive MIMO networks. Based on the system model,
analytical expressions are provided for the asymptotic signal-tointerference-
plus-noise ratio (SINR) distributions in both uplink
and downlink, when the number of base station antennas goes
to infinity. Numerical results indicate a fast convergence in the
SINR distribution to its asymptotic equivalence in dense mmWave
networks. A comparison with conventional massive MIMO shows
that mmWave massive MIMO achieves a higher cell throughput
with sufficiently dense deployments.-Thanks to the small wavelength at millimeter
wave (mmWave) frequency, it is promising to combine massive
multiple-input and multiple-output (MIMO) with mmWave.
MmWave massive MIMO will differ the conventional
massive MIMO, due to the differences in propagation and
hardware constraints. This paper proposes a stochastic geometry
framework for uating the performance in large-scale
mmWave massive MIMO networks. Based on the system model,
analytical expressions are provided for the asymptotic signal-tointerference-
plus-noise ratio (SINR) distributions in both uplink
and downlink, when the number of base station antennas goes
to infinity. Numerical results indicate a fast convergence in the
SINR distribution to its asymptotic equivalence in dense mmWave
networks. A comparison with conventional massive MIMO shows
that mmWave massive MIMO achieves a higher cell throughput
with sufficiently dense deployments.
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Size: 188416 |
Author: hadjidw |
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Description: MESC is a procedure to select the codeword and receive combining vector jointly to maximize an SINR-like metric.
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Size: 30480 |
Author: mohanad84 |
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