| partial differential equation | PDE | 234 | 24 | 2.003333 | 2003.00596,2003.00978,etc |
| additive white gaussian noise | AWGN | 50 | 21 | 1.000676 | 2003.00081,2003.00199,etc |
| channel state information | CSI | 315 | 21 | 1.00446 | 2003.00246,2003.00648,etc |
| signal to noise ratio | SNR | 181 | 20 | 2.00354 | 2003.00081,2003.00243,etc |
| base station | BS | 402 | 19 | 2.003214 | 2003.00199,2003.00243,etc |
| multiple input multiple output | MIMO | 198 | 18 | 2.002033 | 2003.00246,2003.00648,etc |
| theorem | Theorem | 385 | 14 | 2.002647 | 2003.00372,2003.01503,etc |
| ordinary differential equation | ODE | 105 | 14 | 2.000703 | 2003.01044,2003.01201,etc |
| internet of things | IoT | 105 | 13 | 1.00165 | 2003.00199,2003.00383,etc |
| line of sight | LoS | 108 | 13 | 2.001531 | 2003.00391,2003.00648,etc |
| deutsche forschungsgemeinschaft | DFG | 14 | 12 | 1.000131 | 2003.00596,2003.01291,etc |
| successive interference cancellation | SIC | 77 | 10 | 1.002353 | 2003.02117,2003.03105,etc |
| model predictive control | MPC | 151 | 9 | 2.001602 | 2003.00123,2003.00345,etc |
| non orthogonal multiple access | NOMA | 292 | 9 | 2.005538 | 2003.00199,2003.00648,etc |
| cumulative distribution function | CDF | 113 | 9 | 1.00287 | 2003.00246,2003.01350,etc |
| european research council | ERC | 11 | 9 | 1.000127 | 2003.00493,2003.00550,etc |
| bit error rate | BER | 43 | 8 | 1.000882 | 2003.00081,2003.01307,etc |
| karush kuhn tucker | KKT | 40 | 8 | 1.001277 | 2003.00199,2003.02547,etc |
| deep neural network | DNN | 94 | 8 | 1.002878 | 2003.00231,2003.00391,etc |
| markov decision process | MDP | 134 | 8 | 2.002188 | 2003.00383,2003.00384,etc |
| millimeter wave | mmWave | 92 | 8 | 2.001614 | 2003.00648,2003.01653,etc |
| orthogonal frequency division multiplexing | OFDM | 123 | 8 | 1.00291 | 2003.00648,2003.01400,etc |
| stochastic differential equation | SDE | 99 | 8 | 2.00195 | 2003.01201,2003.02614,etc |
| user equipment | UE | 296 | 8 | 2.005411 | 2003.01653,2003.02940,etc |
| signal to interference plus noise ratio | SINR | 38 | 8 | 1.001019 | 2003.01653,2003.02117,etc |
| stochastic gradient descent | SGD | 97 | 7 | 1.000992 | 2003.00116,2003.00231,etc |
| unmanned aerial vehicle | UAV | 618 | 7 | 2.010102 | 2003.00391,2003.02538,etc |
| probability density function | PDF | 93 | 7 | 2.002231 | 2003.01201,2003.01307,etc |
| discrete fourier transform | DFT | 58 | 7 | 1.001558 | 2003.01201,2003.01400,etc |
| orthogonal multiple access | OMA | 387 | 7 | 2.009966 | 2003.01307,2003.03569,etc |
| lemma | Lemma | 177 | 7 | 2.001102 | 2003.01890,2003.02440,etc |
| fifth generation | 5G | 7 | 6 | 1.000077 | 2003.00199,2003.01307,etc |
| age of information | AoI | 442 | 6 | 2.007881 | 2003.00243,2003.00383,etc |
| quadratic program | QP | 70 | 6 | 2.003972 | 2003.00478,2003.00771,etc |
| hamilton jacobi bellman | HJB | 30 | 6 | 2.00077 | 2003.01265,2003.03427,etc |
| angle of arrival | AoA | 36 | 6 | 2.001267 | 2003.01400,2003.03041,etc |
| singular value decomposition | SVD | 89 | 6 | 2.001856 | 2003.01469,2003.04766,etc |
| radio frequency | RF | 65 | 6 | 2.00217 | 2003.01653,2003.01672,etc |
| mean squared error | MSE | 47 | 5 | 2.000964 | 2003.00116,2003.03303,etc |
| minimum mean square error | MMSE | 58 | 5 | 1.002542 | 2003.00243,2003.03140,etc |
| reinforcement learning | RL | 118 | 5 | 1.003498 | 2003.00383,2003.02685,etc |
| quality of service | QoS | 21 | 5 | 1.001211 | 2003.00648,2003.03105,etc |
| alternating direction method of multipliers | ADMM | 40 | 5 | 1.002623 | 2003.00771,2003.00816,etc |
| single input single output | SISO | 12 | 5 | 1.001299 | 2003.01010,2003.01400,etc |
| central limit theorem | CLT | 34 | 5 | 2.001569 | 2003.01350,2003.01772,etc |
| angle of departure | AoD | 20 | 5 | 1.000353 | 2003.01400,2003.03041,etc |
| low density parity check | LDPC | 170 | 4 | 2.002565 | 2003.00081,2003.04421,etc |
| maximum likelihood estimator | MLE | 50 | 4 | 2.001402 | 2003.00083,2003.01827,etc |
| mean square error | MSE | 83 | 4 | 2.00459 | 2003.00116,2003.00648,etc |
| machine learning | ML | 124 | 4 | 1.004792 | 2003.00199,2003.00243,etc |
| convolutional neural network | CNN | 6 | 4 | 1.000103 | 2003.00199,2003.00231,etc |
| simultaneous wireless information and power transfer | SWIPT | 5 | 4 | 1.000203 | 2003.00246,2003.02117,etc |
| time division duplex | TDD | 80 | 4 | 2.004268 | 2003.00246,2003.02538,etc |
| access point | AP | 129 | 4 | 2.010875 | 2003.00383,2003.00648,etc |
| orthogonal frequency division multiple access | OFDMA | 26 | 4 | 2.00059 | 2003.00648,2003.03569,etc |
| markov chain monte carlo | MCMC | 31 | 4 | 2.000666 | 2003.00677,2003.04026,etc |
| orthogonal matching pursuit | OMP | 36 | 4 | 1.001256 | 2003.01307,2003.05875,etc |
| linear time invariant | LTI | 37 | 4 | 2.001141 | 2003.01502,2003.02168,etc |
| zero forcing | ZF | 21 | 4 | 1.001109 | 2003.01653,2003.02940,etc |
| discontinuous galerkin | DG | 113 | 4 | 2.003718 | 2003.02431,2003.02760,etc |
| spectral efficiency | SE | 109 | 4 | 2.001186 | 2003.02940,2003.03683,etc |
| successive convex approximation | SCA | 17 | 4 | 2.000213 | 2003.03105,2003.03574,etc |
| kullback leibler | KL | 29 | 4 | 1.000778 | 2003.03308,2003.04179,etc |
| degrees of freedom | DoF | 8 | 4 | 1.000335 | 2003.03486,2003.06478,etc |
| linear matrix inequality | LMI | 25 | 4 | 1.001158 | 2003.03559,2003.06283,etc |
| boundary value problem | BVP | 55 | 4 | 2.004191 | 2003.05485,2003.06261,etc |
| initial value problem | IVP | 22 | 4 | 1.000465 | 2003.05485,2003.06264,etc |
| uplink | UL | 138 | 4 | 2.003713 | 2003.06478,2003.06562,etc |
| downlink | DL | 155 | 4 | 2.005103 | 2003.06478,2003.06562,etc |
| block coordinate descent | BCD | 51 | 4 | 2.001244 | 2003.06564,2003.07467,etc |
| inter symbol interference | ISI | 5 | 3 | 1.000295 | 2003.00081,2003.00648,etc |
| support vector machine | SVM | 115 | 3 | 2.004575 | 2003.00199,2003.00677,etc |
| empirical risk minimization | ERM | 10 | 3 | 1.000436 | 2003.00231,2003.02684,etc |
| long short term memory | LSTM | 25 | 3 | 2.001146 | 2003.00231,2003.01880,etc |
| monte carlo | MC | 22 | 3 | 1.001075 | 2003.00352,2003.05375,etc |
| directed acyclic graph | DAG | 47 | 3 | 1.001339 | 2003.00362,2003.05755,etc |
| intelligent reflecting surface | IRS | 219 | 3 | 2.004715 | 2003.00648,2003.03105,etc |
| branch and bound | BB | 60 | 3 | 2.004561 | 2003.01265,2003.01837,etc |
| belief propagation | BP | 38 | 3 | 1.001323 | 2003.01307,2003.04421,etc |
| compressive sensing | CS | 149 | 3 | 2.008396 | 2003.01307,2003.03303,etc |
| finite element | FE | 38 | 3 | 2.001002 | 2003.01336,2003.04555,etc |
| uniform linear array | ULA | 13 | 3 | 2.000516 | 2003.01400,2003.03140,etc |
| quantity of interest | QoI | 26 | 3 | 2.001154 | 2003.01772,2003.03399,etc |
| ultra reliable low latency communication | URLLC | 17 | 3 | 1.001504 | 2003.02017,2003.04758,etc |
| maximum ratio combining | MRC | 51 | 3 | 2.003305 | 2003.02017,2003.03486,etc |
| batch normalization | BN | 17 | 3 | 1.00048 | 2003.02027,2003.03303,etc |
| nonlinear programming | NLP | 16 | 3 | 2.000509 | 2003.02418,2003.02547,etc |
| finite element method | FEM | 36 | 3 | 2.001409 | 2003.02431,2003.06628,etc |
| left hand side | LHS | 5 | 3 | 1.000177 | 2003.02547,2003.03105,etc |
| fast fourier transform | FFT | 28 | 3 | 1.001212 | 2003.03011,2003.04924,etc |
| frequency division duplexing | FDD | 14 | 3 | 1.000477 | 2003.03041,2003.03303,etc |
| primary user | PU | 39 | 3 | 2.002056 | 2003.03105,2003.06772,etc |
| full duplex | FD | 75 | 3 | 2.004187 | 2003.03140,2003.06562,etc |
| deep learning | DL | 28 | 3 | 2.001138 | 2003.03303,2003.04179,etc |
| linear quadratic regulator | LQR | 16 | 3 | 1.001531 | 2003.03427,2003.07598,etc |
| linear quadratic gaussian | LQG | 5 | 3 | 1.000117 | 2003.03496,2003.03679,etc |
| three dimensional | 3D | 22 | 3 | 2.000982 | 2003.03574,2003.07014,etc |
| two dimensional | 2D | 20 | 3 | 1.000594 | 2003.03574,2003.07643,etc |
| semidefinite program | SDP | 63 | 3 | 2.00152 | 2003.04021,2003.05547,etc |
| frame error rate | FER | 40 | 3 | 1.001725 | 2003.04421,2003.08640,etc |
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