| partial differential equation | PDE | 4257 | 331 | 1.515894 | 1901.01009,1901.01375,etc |
| signal to noise ratio | SNR | 2674 | 252 | 1.442321 | 1901.00304,1901.00313,etc |
| channel state information | CSI | 2644 | 235 | 1.49158 | 1901.00313,1901.00354,etc |
| base station | BS | 5554 | 206 | 1.727852 | 1901.00313,1901.00354,etc |
| theorem | Theorem | 5308 | 191 | 2.002437 | 1901.00892,1901.01457,etc |
| additive white gaussian noise | AWGN | 689 | 181 | 1.188777 | 1901.00368,1901.00828,etc |
| multiple input multiple output | MIMO | 2943 | 174 | 1.865169 | 1901.00313,1901.00354,etc |
| ordinary differential equation | ODE | 1348 | 146 | 1.501498 | 1901.00107,1901.00124,etc |
| internet of things | IoT | 1124 | 136 | 1.384824 | 1901.00368,1901.01053,etc |
| radio frequency | RF | 1473 | 113 | 1.56031 | 1901.00313,1901.00368,etc |
| probability density function | PDF | 886 | 110 | 1.265471 | 1901.00368,1901.00555,etc |
| stochastic differential equation | SDE | 1156 | 109 | 1.597879 | 1901.00124,1901.00795,etc |
| line of sight | LOS | 1296 | 106 | 1.596598 | 1901.00963,1901.00971,etc |
| signal to interference plus noise ratio | SINR | 893 | 96 | 1.377017 | 1901.00354,1901.04280,etc |
| cumulative distribution function | CDF | 500 | 93 | 1.388278 | 1901.00190,1901.00971,etc |
| fifth generation | 5G | 338 | 90 | 1.189452 | 1901.00313,1901.00354,etc |
| bit error rate | BER | 969 | 90 | 1.413091 | 1901.00190,1901.00368,etc |
| quality of service | QoS | 478 | 89 | 1.27073 | 1901.00190,1901.00354,etc |
| alternating direction method of multipliers | ADMM | 1286 | 89 | 1.497674 | 1901.00262,1901.01872,etc |
| singular value decomposition | SVD | 633 | 87 | 1.346639 | 1901.00304,1901.00313,etc |
| karush kuhn tucker | KKT | 419 | 85 | 1.400814 | 1901.03614,1901.03927,etc |
| orthogonal frequency division multiplexing | OFDM | 1047 | 82 | 1.539934 | 1901.00356,1901.00368,etc |
| non orthogonal multiple access | NOMA | 4055 | 81 | 1.783445 | 1901.01432,1901.03020,etc |
| deutsche forschungsgemeinschaft | DFG | 103 | 78 | 1.000132 | 1901.00799,1901.00815,etc |
| maximum likelihood | ML | 631 | 77 | 1.313113 | 1901.00313,1901.00368,etc |
| european research council | ERC | 101 | 76 | 1.000198 | 1901.00555,1901.01862,etc |
| discontinuous galerkin | DG | 1801 | 76 | 1.753323 | 1901.00079,1901.01803,etc |
| unmanned aerial vehicle | UAV | 6106 | 69 | 1.734543 | 1901.01432,1901.02804,etc |
| mean squared error | MSE | 899 | 67 | 1.315357 | 1901.00354,1901.03269,etc |
| millimeter wave | mmWave | 1634 | 67 | 1.764399 | 1901.00963,1901.01432,etc |
| finite element method | FEM | 662 | 65 | 1.509231 | 1901.00343,1901.01685,etc |
| zero forcing | ZF | 870 | 64 | 1.455866 | 1901.00313,1901.00354,etc |
| successive interference cancellation | SIC | 850 | 64 | 1.455108 | 1901.03927,1901.04149,etc |
| stochastic gradient descent | SGD | 700 | 63 | 1.1766 | 1901.01375,1901.04630,etc |
| central limit theorem | CLT | 388 | 62 | 1.629879 | 1901.00368,1901.03958,etc |
| user equipment | UE | 1907 | 56 | 1.808141 | 1901.00968,1901.00971,etc |
| access point | AP | 1842 | 54 | 1.670625 | 1901.01348,1901.01894,etc |
| linear programming | LP | 665 | 54 | 1.70542 | 1901.02157,1901.03240,etc |
| lemma | Lemma | 1859 | 54 | 2.001833 | 1901.00179,1901.01782,etc |
| markov decision process | MDP | 606 | 52 | 1.443983 | 1901.00963,1901.01992,etc |
| discrete fourier transform | DFT | 197 | 49 | 1.123583 | 1902.00838,1902.02292,etc |
| low density parity check | LDPC | 953 | 48 | 1.546025 | 1901.01280,1901.01348,etc |
| mean square error | MSE | 571 | 48 | 1.668776 | 1902.00150,1902.00917,etc |
| model predictive control | MPC | 1036 | 47 | 1.918024 | 1901.03930,1901.04046,etc |
| principal component analysis | PCA | 541 | 47 | 1.320832 | 1902.00104,1902.03840,etc |
| three dimensional | 3D | 357 | 46 | 1.479136 | 1901.01432,1901.02804,etc |
| conjugate gradient | CG | 604 | 45 | 1.557421 | 1901.00090,1901.00654,etc |
| belief propagation | BP | 707 | 44 | 1.5251 | 1901.01348,1901.02287,etc |
| two dimensional | 2D | 251 | 44 | 1.227896 | 1901.00635,1901.01432,etc |
| markov chain monte carlo | MCMC | 289 | 43 | 1.489243 | 1901.00262,1901.03144,etc |
| fast fourier transform | FFT | 359 | 42 | 1.45434 | 1901.00635,1901.00913,etc |
| semidefinite programming | SDP | 677 | 42 | 1.859103 | 1901.00354,1901.04013,etc |
| deep neural network | DNN | 849 | 42 | 1.741034 | 1901.00354,1901.02210,etc |
| multiple input single output | MISO | 312 | 42 | 1.644027 | 1901.00354,1901.01156,etc |
| algorithm | Algorithm | 536 | 41 | 1.903671 | 1901.00611,1901.01736,etc |
| age of information | AoI | 1759 | 40 | 1.905113 | 1901.02178,1901.02873,etc |
| proper orthogonal decomposition | POD | 965 | 40 | 1.578653 | 1901.02285,1901.04903,etc |
| orthogonal multiple access | OMA | 4361 | 40 | 1.857551 | 1901.03020,1901.04149,etc |
| minimum mean square error | MMSE | 416 | 38 | 1.712111 | 1901.02523,1901.03264,etc |
| uniform linear array | ULA | 104 | 38 | 1.000663 | 1901.00313,1901.06089,etc |
| kullback leibler | K-L | 591 | 37 | 1.515748 | 1901.03036,1901.03269,etc |
| total variation | TV | 907 | 36 | 1.613483 | 1901.00262,1901.03780,etc |
| semidefinite program | SDP | 725 | 35 | 1.802829 | 1902.03373,1902.05238,etc |
| hamilton jacobi bellman | HJB | 320 | 34 | 1.677714 | 1901.04677,1901.05583,etc |
| expectation maximization | EM | 630 | 34 | 1.532255 | 1902.00194,1902.00866,etc |
| log likelihood ratio | LLR | 347 | 33 | 1.546844 | 1901.02287,1901.02914,etc |
| single input single output | SISO | 105 | 33 | 1.515861 | 1901.01389,1901.03620,etc |
| simultaneous wireless information and power transfer | SWIPT | 292 | 33 | 1.698824 | 1901.01740,1901.03301,etc |
| finite element | FEM | 480 | 33 | 1.516425 | 1901.03263,1901.05188,etc |
| time division duplex | TDD | 146 | 33 | 1.334034 | 1902.00824,1902.05184,etc |
| stochastic partial differential equation | SPDE | 355 | 32 | 1.469789 | 1901.00653,1901.01026,etc |
| degrees of freedom | DoF | 604 | 32 | 1.533323 | 1901.04106,1901.06010,etc |
| minimum mean squared error | MMSE | 449 | 32 | 1.502469 | 1902.00150,1902.07053,etc |
| non line of sight | NLOS | 239 | 31 | 1.45308 | 1901.00971,1901.06218,etc |
| random variable | RV | 181 | 30 | 1.567559 | 1901.02285,1901.03039,etc |
| time division multiple access | TDMA | 237 | 30 | 1.568012 | 1901.02203,1901.04959,etc |
| linear program | LP | 229 | 29 | 1.414614 | 1901.01483,1901.02933,etc |
| maximum ratio combining | MRC | 255 | 28 | 1.179924 | 1901.00968,1901.03097,etc |
| monte carlo | MC | 640 | 28 | 1.645893 | 1901.01432,1901.05583,etc |
| reproducing kernel hilbert space | RKHS | 258 | 28 | 1.679297 | 1901.01036,1901.03269,etc |
| reinforcement learning | RL | 508 | 28 | 1.751756 | 1901.05719,1901.07159,etc |
| maximum distance separable | MDS | 363 | 27 | 1.632612 | 1901.00164,1901.02033,etc |
| compressed sensing | CS | 684 | 27 | 1.81802 | 1901.00828,1901.02763,etc |
| angle of arrival | AOA | 200 | 27 | 1.74159 | 1902.00838,1902.02903,etc |
| binary erasure channel | BEC | 168 | 26 | 1.42377 | 1901.01280,1901.01573,etc |
| decode and forward | DF | 449 | 26 | 1.655042 | 1901.03301,1901.03585,etc |
| multiple access channel | MAC | 314 | 25 | 1.4812 | 1901.00929,1901.00939,etc |
| poisson point process | PPP | 288 | 25 | 1.522151 | 1901.01432,1901.04280,etc |
| orthogonal frequency division multiple access | OFDMA | 200 | 25 | 1.601033 | 1901.03614,1901.03637,etc |
| successive cancellation | SC | 1211 | 25 | 2.00598 | 1901.04433,1901.05459,etc |
| korteweg de vries | KdV | 313 | 25 | 1.68268 | 1901.00182,1901.01461,etc |
| partial differential equations | PDE | 220 | 25 | 1.481315 | 1902.04221,1902.04466,etc |
| successive convex approximation | SCA | 298 | 25 | 1.401674 | 1903.01932,1903.02686,etc |
| dynamic programming | DP | 561 | 24 | 1.752466 | 1901.01659,1901.03840,etc |
| binary symmetric channel | BSC | 237 | 24 | 1.501306 | 1901.02523,1901.05825,etc |
| deep learning | DL | 279 | 24 | 1.127039 | 1901.00354,1901.04630,etc |
| least squares | LS | 385 | 24 | 1.752519 | 1901.00653,1901.00913,etc |
| frequency division duplex | FDD | 181 | 24 | 1.250838 | 1902.00824,1902.05184,etc |
| convolutional neural network | CNN | 262 | 23 | 1.436481 | 1901.00354,1901.03620,etc |
| maximum a posteriori | MAP | 146 | 23 | 1.08836 | 1901.04433,1901.05323,etc |
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