| partial differential equation | PDE | 270 | 25 | 1.00318 | 2009.00139,2009.00736,etc |
| theorem | Theorem | 888 | 23 | 2.00238 | 2009.00177,2009.00233,etc |
| channel state information | CSI | 244 | 19 | 2.004503 | 2009.00105,2009.00171,etc |
| signal to noise ratio | SNR | 129 | 19 | 2.001503 | 2009.00105,2009.00517,etc |
| deutsche forschungsgemeinschaft | DFG | 19 | 18 | 1.000218 | 2009.00316,2009.00352,etc |
| base station | BS | 478 | 16 | 2.00373 | 2009.00105,2009.00267,etc |
| multiple input multiple output | MIMO | 161 | 14 | 2.002127 | 2009.00389,2009.00724,etc |
| line of sight | LoS | 212 | 14 | 2.003192 | 2009.00473,2009.01988,etc |
| additive white gaussian noise | AWGN | 29 | 13 | 1.000725 | 2009.00267,2009.00806,etc |
| stochastic differential equation | SDE | 198 | 13 | 1.001744 | 2009.01276,2009.01299,etc |
| internet of things | IoT | 128 | 11 | 1.004284 | 2009.00105,2009.00724,etc |
| european research council | ERC | 15 | 11 | 1.00021 | 2009.00441,2009.01201,etc |
| alternating direction method of multipliers | ADMM | 330 | 11 | 2.004408 | 2009.00801,2009.01790,etc |
| ordinary differential equation | ODE | 109 | 10 | 2.001575 | 2009.01299,2009.02327,etc |
| karush kuhn tucker | KKT | 81 | 10 | 2.002396 | 2009.03020,2009.03880,etc |
| intelligent reflecting surface | IRS | 861 | 9 | 2.007701 | 2009.00267,2009.02324,etc |
| finite element method | FEM | 81 | 9 | 1.002248 | 2009.00532,2009.00571,etc |
| signal to interference plus noise ratio | SINR | 264 | 9 | 1.006348 | 2009.00724,2009.01753,etc |
| mean squared error | MSE | 37 | 9 | 1.000405 | 2009.00801,2009.02327,etc |
| radio frequency | RF | 95 | 8 | 2.004244 | 2009.00328,2009.00789,etc |
| reconfigurable intelligent surface | RIS | 406 | 8 | 2.008737 | 2009.00517,2009.00789,etc |
| discontinuous galerkin | DG | 278 | 8 | 2.00462 | 2009.00704,2009.00991,etc |
| model predictive control | MPC | 192 | 8 | 2.005097 | 2009.01298,2009.01332,etc |
| lemma | Lemma | 406 | 8 | 2.001496 | 2009.02644,2009.03127,etc |
| singular value decomposition | SVD | 74 | 7 | 2.001689 | 2009.00267,2009.00389,etc |
| probability density function | PDF | 23 | 7 | 2.000793 | 2009.00328,2009.00517,etc |
| maximum likelihood | ML | 65 | 7 | 2.003481 | 2009.00789,2009.02507,etc |
| kullback leibler | KL | 81 | 7 | 2.002206 | 2009.01364,2009.01704,etc |
| millimeter wave | mmWave | 156 | 7 | 2.002289 | 2009.01988,2009.02747,etc |
| non orthogonal multiple access | NOMA | 243 | 6 | 1.008928 | 2009.00105,2009.00267,etc |
| proper orthogonal decomposition | POD | 32 | 6 | 2.00081 | 2009.01332,2009.01596,etc |
| minimum mean square error | MMSE | 116 | 6 | 1.003808 | 2009.02031,2009.02747,etc |
| optimal control problem | OCP | 46 | 6 | 1.002575 | 2009.04187,2009.05686,etc |
| markov chain monte carlo | MCMC | 29 | 5 | 1.000383 | 2009.00195,2009.04239,etc |
| eavesdropper | Eve | 294 | 5 | 2.00413 | 2009.00517,2009.05920,etc |
| bit error rate | BER | 39 | 5 | 1.000838 | 2009.00789,2009.00806,etc |
| orthogonal frequency division multiplexing | OFDM | 22 | 5 | 2.000559 | 2009.00806,2009.02747,etc |
| hamilton jacobi bellman | HJB | 41 | 5 | 2.00111 | 2009.01332,2009.05667,etc |
| algorithm | Algorithm | 87 | 5 | 2.000938 | 2009.01349,2009.02911,etc |
| fifth generation | 5G | 41 | 5 | 2.002904 | 2009.02324,2009.06286,etc |
| convolutional neural network | CNN | 29 | 5 | 1.001809 | 2009.02713,2009.04103,etc |
| user equipment | UE | 181 | 5 | 2.005323 | 2009.02747,2009.02875,etc |
| mean square error | MSE | 109 | 5 | 2.004394 | 2009.02747,2009.03892,etc |
| central limit theorem | CLT | 25 | 5 | 1.002729 | 2009.03000,2009.05420,etc |
| maximum a posteriori | MAP | 116 | 5 | 2.002388 | 2009.03504,2009.04188,etc |
| reproducing kernel hilbert space | RKHS | 28 | 5 | 1.001046 | 2009.04188,2009.04239,etc |
| orthogonal multiple access | OMA | 129 | 4 | 2.006584 | 2009.00105,2009.00267,etc |
| artificial noise | AN | 55 | 4 | 2.00153 | 2009.00267,2009.00473,etc |
| electrical impedance tomography | EIT | 19 | 4 | 2.000943 | 2009.00370,2009.02525,etc |
| semidefinite relaxation | SDR | 36 | 4 | 1.000892 | 2009.00473,2009.02747,etc |
| gradient descent | GD | 53 | 4 | 1.001616 | 2009.00673,2009.02604,etc |
| total variation | TV | 66 | 4 | 2.005922 | 2009.00801,2009.03470,etc |
| stochastic gradient descent | SGD | 40 | 4 | 1.002409 | 2009.01790,2009.02713,etc |
| linear program | LP | 48 | 4 | 2.00072 | 2009.01942,2009.02377,etc |
| unmanned aerial vehicle | UAV | 10 | 4 | 1.000136 | 2009.01988,2009.02716,etc |
| markov decision process | MDP | 13 | 4 | 1.000199 | 2009.02053,2009.02146,etc |
| reduced order model | ROM | 101 | 4 | 1.00449 | 2009.02176,2009.02769,etc |
| multiple access channel | MAC | 51 | 4 | 1.001585 | 2009.02324,2009.03788,etc |
| conjugate gradient | CG | 80 | 4 | 1.003038 | 2009.02604,2009.05814,etc |
| expectation maximization | EM | 5 | 4 | 1.000229 | 2009.02736,2009.05072,etc |
| uniform linear array | ULA | 11 | 4 | 1.00081 | 2009.02747,2009.03528,etc |
| reinforcement learning | RL | 35 | 4 | 2.002706 | 2009.02911,2009.03616,etc |
| angle of arrival | AoA | 62 | 4 | 1.002405 | 2009.03536,2009.05893,etc |
| discrete fourier transform | DFT | 14 | 4 | 1.00075 | 2009.03536,2009.04428,etc |
| stochastic partial differential equation | SPDE | 15 | 4 | 2.000187 | 2009.05421,2009.06137,etc |
| quality of service | QoS | 21 | 3 | 1.000783 | 2009.00105,2009.00267,etc |
| successive interference cancellation | SIC | 17 | 3 | 1.000647 | 2009.00105,2009.00267,etc |
| upper confidence bound | UCB | 15 | 3 | 2.000779 | 2009.00105,2009.01339,etc |
| conjecture | Conjecture | 16 | 3 | 2.000261 | 2009.00233,2009.01831,etc |
| physical layer security | PLS | 41 | 3 | 2.000597 | 2009.00267,2009.01988,etc |
| successive convex approximation | SCA | 10 | 3 | 2.000639 | 2009.00473,2009.05893,etc |
| batalin vilkovisky | BV | 166 | 3 | 2.002013 | 2009.00509,2009.04064,etc |
| science and engineering research board | SERB | 3 | 3 | 1.000049 | 2009.00532,2009.00571,etc |
| linear matrix inequality | LMI | 22 | 3 | 1.000644 | 2009.00673,2009.05893,etc |
| circularly symmetric complex gaussian | CSCG | 5 | 3 | 1.000173 | 2009.00724,2009.02551,etc |
| linear time invariant | LTI | 24 | 3 | 1.001346 | 2009.00739,2009.02468,etc |
| positive intrinsic negative | PIN | 9 | 3 | 1.000131 | 2009.00789,2009.02694,etc |
| definition of | Definition | 30 | 3 | 2.000431 | 2009.01124,2009.07953,etc |
| linear programming | LP | 81 | 3 | 2.0019 | 2009.01298,2009.02377,etc |
| partial integro differential equation | PIDE | 27 | 3 | 2.000407 | 2009.01392,2009.06521,etc |
| isogeometric analysis | IGA | 239 | 3 | 2.00963 | 2009.01499,2009.08132,etc |
| virtual reality | VR | 71 | 3 | 1.003332 | 2009.01632,2009.01753,etc |
| difference of convex | DC | 10 | 3 | 1.000485 | 2009.01753,2009.03504,etc |
| maximum ratio transmission | MRT | 12 | 3 | 1.00042 | 2009.01753,2009.02875,etc |
| section | Section | 27 | 3 | 2.000354 | 2009.01988,2009.06563,etc |
| non line of sight | NLoS | 52 | 3 | 2.001265 | 2009.01988,2009.03536,etc |
| poisson point process | PPP | 5 | 3 | 1.000154 | 2009.02031,2009.02192,etc |
| nash equilibrium | NE | 16 | 3 | 1.000654 | 2009.02053,2009.02146,etc |
| broadcast channel | BC | 95 | 3 | 2.002912 | 2009.02324,2009.04010,etc |
| left hand side | LHS | 4 | 3 | 1.000296 | 2009.02324,2009.04564,etc |
| raviart thomas | RT | 4 | 3 | 1.00042 | 2009.02607,2009.03928,etc |
| pairwise error probability | PEP | 26 | 3 | 2.000749 | 2009.02682,2009.03536,etc |
| electromagnetic | EM | 35 | 3 | 2.002166 | 2009.02694,2009.08038,etc |
| deep neural network | DNN | 74 | 3 | 1.002857 | 2009.02713,2009.08024,etc |
| angle of departure | AoD | 54 | 3 | 1.002811 | 2009.03536,2009.05893,etc |
| log likelihood ratio | LLR | 57 | 3 | 2.001737 | 2009.04148,2009.05072,etc |
| cyclic redundancy check | CRC | 29 | 3 | 2.001882 | 2009.04338,2009.06796,etc |
| maximum likelihood estimator | MLE | 6 | 3 | 1.000064 | 2009.04856,2009.08562,etc |
| nonlinear schr"odinger | NLS | 15 | 3 | 2.000237 | 2009.04929,2009.06877,etc |
| nonlinear programming | NLP | 14 | 3 | 2.001865 | 2009.05845,2009.05873,etc |
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