| signal to noise ratio | SNR | 243 | 28 | 1.001908 | 1904.00453,1904.01256,etc |
| partial differential equation | PDE | 549 | 27 | 1.002788 | 1904.00258,1904.00334,etc |
| channel state information | CSI | 216 | 24 | 1.002191 | 1904.00453,1904.01256,etc |
| theorem | Theorem | 556 | 19 | 2.003223 | 1904.00147,1904.00517,etc |
| additive white gaussian noise | AWGN | 118 | 16 | 2.004222 | 1904.01799,1904.02023,etc |
| base station | BS | 298 | 15 | 2.002983 | 1904.00327,1904.01257,etc |
| stochastic differential equation | SDE | 138 | 13 | 2.001344 | 1904.00075,1904.00429,etc |
| multiple input multiple output | MIMO | 184 | 12 | 1.001763 | 1904.00453,1904.02783,etc |
| ordinary differential equation | ODE | 119 | 10 | 1.002766 | 1904.01697,1904.03037,etc |
| line of sight | LoS | 66 | 9 | 2.001699 | 1904.00318,1904.00453,etc |
| deutsche forschungsgemeinschaft | DFG | 12 | 9 | 1.00008 | 1904.00377,1904.01343,etc |
| internet of things | IoT | 71 | 9 | 1.003049 | 1904.00453,1904.01323,etc |
| bit error rate | BER | 116 | 9 | 1.002006 | 1904.00832,1904.01323,etc |
| low density parity check | LDPC | 207 | 9 | 2.005758 | 1904.00832,1904.03844,etc |
| maximum likelihood | ML | 118 | 9 | 2.002088 | 1904.01323,1904.01799,etc |
| maximum a posteriori | MAP | 60 | 7 | 1.001965 | 1904.00363,1904.01697,etc |
| orthogonal frequency division multiplexing | OFDM | 66 | 7 | 2.001786 | 1904.00453,1904.02783,etc |
| probability density function | PDF | 67 | 7 | 2.002227 | 1904.00752,1904.02272,etc |
| quality of service | QoS | 37 | 7 | 1.001725 | 1904.01257,1904.01730,etc |
| alternating direction method of multipliers | ADMM | 73 | 7 | 2.001471 | 1904.01799,1904.02086,etc |
| isogeometric analysis | IGA | 80 | 7 | 1.002057 | 1904.02527,1904.04714,etc |
| belief propagation | BP | 239 | 7 | 2.004216 | 1904.05396,1904.05484,etc |
| model predictive control | MPC | 165 | 6 | 2.002856 | 1904.00053,1904.00987,etc |
| log likelihood ratio | LLR | 28 | 6 | 2.00082 | 1904.00832,1904.02327,etc |
| zero forcing | ZF | 85 | 6 | 2.001695 | 1904.01256,1904.05591,etc |
| unmanned aerial vehicle | UAV | 694 | 6 | 2.009023 | 1904.01257,1904.02602,etc |
| central limit theorem | CLT | 27 | 6 | 1.00026 | 1904.01323,1904.03184,etc |
| monte carlo | MC | 103 | 6 | 2.003075 | 1904.02017,1904.02966,etc |
| european research council | ERC | 12 | 6 | 1.000505 | 1904.02558,1904.04000,etc |
| access point | AP | 51 | 6 | 1.002799 | 1904.03297,1904.03512,etc |
| karush kuhn tucker | KKT | 8 | 6 | 1.000267 | 1904.04530,1904.06708,etc |
| finite element method | FEM | 11 | 5 | 1.000335 | 1904.00186,1904.01951,etc |
| mean square error | MSE | 23 | 5 | 2.000947 | 1904.00429,1904.03458,etc |
| discontinuous galerkin | DG | 67 | 5 | 1.001771 | 1904.00972,1904.02578,etc |
| stochastic gradient descent | SGD | 86 | 5 | 2.001878 | 1904.01145,1904.01517,etc |
| non orthogonal multiple access | NOMA | 161 | 5 | 2.005155 | 1904.01730,1904.02086,etc |
| radio frequency | RF | 45 | 5 | 2.002402 | 1904.02023,1904.03458,etc |
| maximum distance separable | MDS | 34 | 5 | 1.001715 | 1904.05272,1904.05487,etc |
| boundary value problem | BVP | 30 | 4 | 1.002798 | 1904.00258,1904.02051,etc |
| reinforcement learning | RL | 39 | 4 | 2.001332 | 1904.00327,1904.02572,etc |
| minimum mean square error | MMSE | 18 | 4 | 2.002824 | 1904.00327,1904.00453,etc |
| markov decision process | MDP | 22 | 4 | 1.001909 | 1904.00327,1904.01185,etc |
| total variation | TV | 111 | 4 | 2.003058 | 1904.00423,1904.01799,etc |
| non line of sight | NLOS | 30 | 4 | 1.000822 | 1904.00453,1904.02068,etc |
| signal to interference plus noise ratio | SINR | 27 | 4 | 1.000411 | 1904.00453,1904.02783,etc |
| cumulative distribution function | CDF | 15 | 4 | 1.001303 | 1904.01185,1904.02086,etc |
| degrees of freedom | DoF | 36 | 4 | 2.000905 | 1904.01256,1904.03158,etc |
| millimeter wave | mmWave | 45 | 4 | 2.000803 | 1904.01257,1904.03657,etc |
| user equipment | UE | 255 | 4 | 2.005027 | 1904.01323,1904.06946,etc |
| linear programming | LP | 13 | 4 | 2.000287 | 1904.01711,1904.01833,etc |
| machine type communication | MTC | 27 | 4 | 1.001057 | 1904.01730,1904.02086,etc |
| time division multiple access | TDMA | 62 | 4 | 2.00163 | 1904.01730,1904.03512,etc |
| linear program | LP | 17 | 4 | 1.000382 | 1904.01757,1904.02131,etc |
| proposition | Proposition | 151 | 4 | 2.001078 | 1904.01887,1904.02525,etc |
| stochastic partial differential equation | SPDE | 107 | 4 | 1.001483 | 1904.02017,1904.02274,etc |
| finite element | FE | 29 | 4 | 2.001165 | 1904.02017,1904.05851,etc |
| quadrature phase shift keying | QPSK | 13 | 4 | 1.0009 | 1904.02023,1904.03805,etc |
| successive convex approximation | SCA | 89 | 4 | 2.001267 | 1904.02086,1904.06029,etc |
| computer aided design | CAD | 5 | 4 | 1.000402 | 1904.02527,1904.05577,etc |
| root mean square error | RMSE | 25 | 4 | 2.001271 | 1904.02578,1904.03437,etc |
| successive interference cancellation | SIC | 39 | 4 | 2.001755 | 1904.02783,1904.07978,etc |
| direction of arrival | DoA | 42 | 4 | 2.003332 | 1904.03458,1904.03569,etc |
| neural network | NN | 217 | 4 | 2.009072 | 1904.03657,1904.06619,etc |
| fifth generation | 5G | 9 | 4 | 1.000361 | 1904.03768,1904.03979,etc |
| principal component analysis | PCA | 43 | 4 | 2.000791 | 1904.03784,1904.04796,etc |
| poisson point process | PPP | 11 | 4 | 1.001402 | 1904.04139,1904.04530,etc |
| partial differential equations | PDE | 53 | 4 | 1.002979 | 1904.04685,1904.06114,etc |
| algorithm | Algorithm | 41 | 4 | 2.001601 | 1904.06474,1904.06583,etc |
| sample average approximation | SAA | 124 | 3 | 2.002544 | 1904.00137,1904.01550,etc |
| generalized minimal residual | GMRES | 14 | 3 | 2.000297 | 1904.00196,1904.05960,etc |
| kullback leibler | KL | 245 | 3 | 1.006849 | 1904.00318,1904.02505,etc |
| deep neural network | DNN | 52 | 3 | 1.001415 | 1904.00377,1904.03805,etc |
| multilevel monte carlo | MLMC | 75 | 3 | 2.000943 | 1904.00429,1904.05851,etc |
| spectral efficiency | SE | 108 | 3 | 2.003191 | 1904.00453,1904.03657,etc |
| least square | LS | 12 | 3 | 1.000677 | 1904.00453,1904.03458,etc |
| kardar parisi zhang | KPZ | 92 | 3 | 2.004274 | 1904.01048,1904.03037,etc |
| automatic differentiation | AD | 12 | 3 | 1.000138 | 1904.01145,1904.05460,etc |
| age of information | AoI | 126 | 3 | 2.002988 | 1904.01185,1904.03470,etc |
| edge node | EN | 369 | 3 | 2.006164 | 1904.01256,1904.05591,etc |
| decision maker | DM | 243 | 3 | 2.002946 | 1904.01315,1904.04128,etc |
| amplify and forward | AF | 74 | 3 | 2.001885 | 1904.01323,1904.04530,etc |
| lemma | Lemma | 175 | 3 | 2.001298 | 1904.01361,1904.02525,etc |
| corollary | Corollary | 46 | 3 | 2.000211 | 1904.01361,1904.04539,etc |
| quadratic programming | QP | 16 | 3 | 1.001174 | 1904.01415,1904.03158,etc |
| singular value decomposition | SVD | 3 | 3 | 1.000197 | 1904.01711,1904.07638,etc |
| mixed integer linear program | MILP | 8 | 3 | 1.000047 | 1904.01757,1904.05965,etc |
| fast fourier transform | FFT | 19 | 3 | 1.000522 | 1904.01799,1904.02783,etc |
| orthogonal matching pursuit | OMP | 21 | 3 | 2.000957 | 1904.01818,1904.03657,etc |
| compressed sensing | CS | 21 | 3 | 2.001018 | 1904.01818,1904.03437,etc |
| random variable | RV | 14 | 3 | 2.000663 | 1904.01819,1904.05587,etc |
| frame error rate | FER | 29 | 3 | 1.003368 | 1904.01819,1904.02327,etc |
| dynamic programming | DP | 11 | 3 | 2.0007 | 1904.02041,1904.03755,etc |
| time division duplex | TDD | 15 | 3 | 1.002142 | 1904.02068,1904.02086,etc |
| private information retrieval | PIR | 158 | 3 | 2.003868 | 1904.02131,1904.05906,etc |
| successive cancellation | SC | 177 | 3 | 2.012333 | 1904.02327,1904.02450,etc |
| sc list | SCL | 111 | 3 | 2.006982 | 1904.02327,1904.02450,etc |
| cyclic redundancy check | CRC | 13 | 3 | 1.001221 | 1904.02327,1904.03805,etc |
| circularly symmetric complex gaussian | CSCG | 5 | 3 | 1.000186 | 1904.02802,1904.03297,etc |
| riemann hypothesis | RH | 7 | 3 | 1.000513 | 1904.03123,1904.04616,etc |
| peak to average power ratio | PAPR | 15 | 3 | 1.001006 | 1904.03297,1904.07409,etc |
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