| partial differential equation | PDE | 676 | 33 | 2.003119 | 1907.00507,1907.00667,etc |
| theorem | Theorem | 583 | 20 | 2.001992 | 1907.00092,1907.01352,etc |
| internet of things | IoT | 54 | 18 | 1.000993 | 1907.00928,1907.01064,etc |
| channel state information | CSI | 197 | 17 | 1.003874 | 1907.00365,1907.01064,etc |
| signal to noise ratio | SNR | 177 | 15 | 2.003252 | 1907.00365,1907.01078,etc |
| discontinuous galerkin | DG | 437 | 15 | 2.004556 | 1907.01027,1907.01246,etc |
| multiple input multiple output | MIMO | 159 | 14 | 2.002177 | 1907.00365,1907.00386,etc |
| additive white gaussian noise | AWGN | 23 | 12 | 1.000364 | 1907.00527,1907.00784,etc |
| alternating direction method of multipliers | ADMM | 49 | 11 | 1.0009 | 1907.00723,1907.01142,etc |
| base station | BS | 143 | 11 | 1.002759 | 1907.02942,1907.03085,etc |
| line of sight | LOS | 141 | 10 | 1.003188 | 1907.00365,1907.02941,etc |
| stochastic differential equation | SDE | 100 | 10 | 1.001199 | 1907.00557,1907.01410,etc |
| probability density function | PDF | 87 | 10 | 2.001412 | 1907.00668,1907.01064,etc |
| deutsche forschungsgemeinschaft | DFG | 10 | 9 | 1.000076 | 1907.00197,1907.00447,etc |
| ordinary differential equation | ODE | 112 | 9 | 2.001291 | 1907.00961,1907.01711,etc |
| orthogonal frequency division multiplexing | OFDM | 40 | 9 | 2.001698 | 1907.01308,1907.01512,etc |
| european research council | ERC | 16 | 9 | 1.000144 | 1907.02745,1907.03287,etc |
| radio frequency | RF | 94 | 8 | 1.003772 | 1907.00365,1907.02941,etc |
| singular value decomposition | SVD | 25 | 8 | 1.000343 | 1907.00386,1907.03133,etc |
| markov decision process | MDP | 79 | 8 | 2.00144 | 1907.02633,1907.03289,etc |
| bit error rate | BER | 53 | 7 | 1.001059 | 1907.00365,1907.00527,etc |
| lemma | Lemma | 168 | 7 | 2.001898 | 1907.00551,1907.01156,etc |
| stochastic partial differential equation | SPDE | 43 | 7 | 1.001565 | 1907.00806,1907.01530,etc |
| signal to interference plus noise ratio | SINR | 109 | 7 | 2.001822 | 1907.03133,1907.03262,etc |
| stochastic gradient descent | SGD | 101 | 6 | 1.00258 | 1907.00138,1907.02745,etc |
| sample average approximation | SAA | 93 | 6 | 2.000868 | 1907.03219,1907.04472,etc |
| invariant energy quadratization | IEQ | 16 | 5 | 1.000661 | 1907.00167,1907.02122,etc |
| scalar auxiliary variable | SAV | 181 | 5 | 2.007754 | 1907.00167,1907.02234,etc |
| linear programming | LP | 25 | 5 | 1.000572 | 1907.00255,1907.04044,etc |
| semidefinite program | SDP | 66 | 5 | 2.002874 | 1907.00255,1907.02989,etc |
| maximum likelihood | ML | 44 | 5 | 1.001559 | 1907.00365,1907.01077,etc |
| multiple input single output | MISO | 24 | 5 | 1.00122 | 1907.00386,1907.01064,etc |
| restricted isometry property | RIP | 60 | 5 | 2.003488 | 1907.00880,1907.01078,etc |
| new radio | NR | 75 | 5 | 2.00295 | 1907.01349,1907.01512,etc |
| markov chain monte carlo | MCMC | 22 | 5 | 1.000534 | 1907.01843,1907.03086,etc |
| fifth generation | 5G | 35 | 5 | 1.002101 | 1907.02361,1907.03530,etc |
| access point | AP | 163 | 5 | 2.003836 | 1907.02745,1907.03262,etc |
| non orthogonal multiple access | NOMA | 117 | 5 | 1.004527 | 1907.02941,1907.03133,etc |
| cumulative distribution function | CDF | 51 | 5 | 1.002704 | 1907.03341,1907.03530,etc |
| unmanned aerial vehicle | UAV | 427 | 5 | 1.011094 | 1907.04299,1907.04474,etc |
| approximate message passing | AMP | 96 | 4 | 1.008052 | 1907.00138,1907.02247,etc |
| quasi monte carlo | qMC | 76 | 4 | 2.00237 | 1907.00349,1907.00806,etc |
| monte carlo | MC | 116 | 4 | 2.002682 | 1907.00349,1907.00718,etc |
| minimum mean squared error | MMSE | 70 | 4 | 1.002924 | 1907.00386,1907.02496,etc |
| successive interference cancellation | SIC | 16 | 4 | 1.000473 | 1907.00386,1907.02941,etc |
| large deviation principle | LDP | 39 | 4 | 1.000466 | 1907.00531,1907.02033,etc |
| conjugate gradient | CG | 23 | 4 | 1.001471 | 1907.00667,1907.01200,etc |
| convolutional neural network | CNN | 14 | 4 | 1.00034 | 1907.00718,1907.02745,etc |
| uncertainty quantification | UQ | 18 | 4 | 1.000552 | 1907.00718,1907.00806,etc |
| recurrent neural network | RNN | 94 | 4 | 1.00569 | 1907.00718,1907.03289,etc |
| block error rate | BLER | 30 | 4 | 1.002543 | 1907.00784,1907.01077,etc |
| nonlinear schr"odinger | NLS | 99 | 4 | 1.001477 | 1907.00926,1907.01998,etc |
| age of information | AoI | 156 | 4 | 2.006426 | 1907.00928,1907.03826,etc |
| principal component analysis | PCA | 33 | 4 | 2.000821 | 1907.00949,1907.01840,etc |
| experimental order of convergence | EOC | 46 | 4 | 1.001157 | 1907.00961,1907.01711,etc |
| mixed integer programming | MIP | 23 | 4 | 1.000823 | 1907.01031,1907.04951,etc |
| fast fourier transform | FFT | 10 | 4 | 1.000168 | 1907.01142,1907.01479,etc |
| enhanced mobile broadband | eMBB | 9 | 4 | 1.000101 | 1907.01349,1907.04474,etc |
| user equipment | UE | 52 | 4 | 2.003798 | 1907.01349,1907.02942,etc |
| korteweg de vries | KdV | 65 | 4 | 2.000908 | 1907.01412,1907.01438,etc |
| mixed integer linear programming | MILP | 88 | 4 | 1.001567 | 1907.02371,1907.02405,etc |
| mean square error | MSE | 36 | 4 | 1.001682 | 1907.02921,1907.02942,etc |
| zero forcing | ZF | 44 | 4 | 2.001713 | 1907.02941,1907.03530,etc |
| initial value problem | IVP | 37 | 4 | 2.001692 | 1907.03067,1907.03151,etc |
| wireless sensor network | WSN | 32 | 4 | 2.000484 | 1907.03071,1907.03474,etc |
| deep neural network | DNN | 52 | 4 | 2.00084 | 1907.03140,1907.03289,etc |
| algebraic multigrid | AMG | 158 | 4 | 2.003369 | 1907.03406,1907.04229,etc |
| linear program | LP | 45 | 4 | 1.000986 | 1907.03435,1907.04793,etc |
| linear time invariant | LTI | 15 | 4 | 1.00116 | 1907.04095,1907.06256,etc |
| two dimensional | 2D | 36 | 4 | 1.00127 | 1907.04195,1907.04474,etc |
| karush–kuhn–tucker | KKT | 8 | 3 | 1.000249 | 1907.00312,1907.04450,etc |
| proper orthogonal decomposition | POD | 48 | 3 | 2.003778 | 1907.00349,1907.00806,etc |
| non los | NLoS | 18 | 3 | 2.000761 | 1907.00365,1907.03133,etc |
| conjecture | Conjecture | 13 | 3 | 1.00061 | 1907.00453,1907.06276,etc |
| belief propagation | BP | 29 | 3 | 2.001712 | 1907.00527,1907.02247,etc |
| low density parity check | LDPC | 40 | 3 | 2.002163 | 1907.00527,1907.03938,etc |
| cyclic redundancy check | CRC | 72 | 3 | 2.008317 | 1907.00527,1907.00784,etc |
| maximum distance separable | MDS | 20 | 3 | 2.000568 | 1907.00598,1907.05058,etc |
| hard thresholding pursuit | HTP | 9 | 3 | 1.000664 | 1907.00723,1907.06054,etc |
| quadrature phase shift keying | QPSK | 4 | 3 | 1.003705 | 1907.00784,1907.04228,etc |
| artificial noise | AN | 117 | 3 | 2.007466 | 1907.01064,1907.03085,etc |
| semidefinite relaxation | SDR | 18 | 3 | 2.00104 | 1907.01064,1907.03133,etc |
| discrete fourier transform | DFT | 15 | 3 | 1.000645 | 1907.01078,1907.03467,etc |
| iterative hard thresholding | IHT | 46 | 3 | 2.002471 | 1907.01078,1907.06054,etc |
| summation by parts | SBP | 49 | 3 | 2.00168 | 1907.01105,1907.03287,etc |
| symmetric positive definite | SPD | 17 | 3 | 2.000291 | 1907.01200,1907.01790,etc |
| key performance indicator | KPI | 9 | 3 | 1.000397 | 1907.01349,1907.03530,etc |
| riemann hilbert | RH | 93 | 3 | 2.001944 | 1907.01998,1907.02460,etc |
| central limit theorem | CLT | 10 | 3 | 1.000203 | 1907.02247,1907.05182,etc |
| natural science foundation of china | NSFC | 4 | 3 | 1.000143 | 1907.02430,1907.02652,etc |
| stochastic block model | SBM | 33 | 3 | 2.000702 | 1907.02496,1907.05566,etc |
| reinforcement learning | RL | 90 | 3 | 2.001597 | 1907.02633,1907.03289,etc |
| maximum likelihood estimator | MLE | 15 | 3 | 1.00054 | 1907.02652,1907.05377,etc |
| karush kuhn tucker | KKT | 5 | 3 | 1.000081 | 1907.03025,1907.04285,etc |
| intelligent reflecting surface | IRS | 150 | 3 | 2.003032 | 1907.03085,1907.03133,etc |
| partial differential equations | PDE | 11 | 3 | 1.000352 | 1907.03134,1907.06079,etc |
| proximal point algorithm | PPA | 45 | 3 | 2.004612 | 1907.03245,1907.03435,etc |
| discontinuous galerkin spectral element method | DGSEM | 34 | 3 | 1.000911 | 1907.03287,1907.04939,etc |
| mixed integer nonlinear programming | MINLP | 18 | 3 | 2.000808 | 1907.03289,1907.06972,etc |
| korteweg de vries equation | KdV | 7 | 3 | 1.000123 | 1907.04297,1907.05786,etc |
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