On the Performance Supremum of CFO Based Physical Layer Identification

<div>Identification accuracy of schemes using KNN with CFO (HC) and deep learning with raw IQ samples (DP) under different CFO ranges (e.g., 2.5CFO means <svg height="11.927pt" id="M183" style="vertical-align:-3.291101pt" version="1.1" viewbox="-0.0498162 -8.6359 43.4976 11.927" width="43.4976pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M715 650H476L470 622C550 619 562 610 562 570C562 536 553 481 535 384L523 321C485 121 408 37 283 37C178 37 99 109 134 297L178 532C193 612 199 616 278 622L284 650H29L23 622C105 616 109 609 94 532L48 291C23 160 51 83 98 40C143 -1 202 -16 260 -16C321 -16 387 3 439 49C509 111 542 204 563 312L577 384C595 479 608 533 619 569C629 603 637 620 709 622L715 650Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,8.606,3.132)"><path d="M396 374C396 399 365 451 262 451C173 451 84 405 84 327C84 295 107 257 157 243V241C91 226 24 184 24 119C24 41 92 -12 189 -12C272 -12 344 31 386 91L367 118C325 76 274 50 222 50C163 50 120 84 120 138C120 191 169 218 223 218C247 218 263 217 278 216L312 260L308 267H259C200 267 167 297 167 338S202 414 250 414C297 414 326 387 336 341C338 331 341 328 349 328C361 328 396 349 396 374Z"></path></g><g transform="matrix(.013,0,0,-0.013,16.572,0)"><path d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z"></path></g><g transform="matrix(.013,0,0,-0.013,27.835,0)"><path d="M412 140C382 77 369 73 315 73H129L270 222C362 320 402 379 402 466C402 571 322 635 234 635C177 635 130 609 99 576L42 495L64 475C90 514 133 568 201 568C274 568 318 519 318 435C318 349 255 267 193 193C144 135 87 78 32 23V0H405C417 45 427 89 440 131L412 140Z"></path></g><g transform="matrix(.013,0,0,-0.013,34.075,0)"><path d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z"></path></g><g transform="matrix(.013,0,0,-0.013,37.039,0)"><path d="M153 550H386L412 615L406 623H120L82 318C104 327 142 338 184 338C294 338 347 275 347 187C347 112 305 39 221 39C160 39 119 71 97 89C88 97 80 96 71 90C59 80 50 67 49 57C48 45 52 36 66 23C80 9 123 -12 169 -12C221 -11 288 15 342 59C403 109 431 165 431 225C431 308 366 395 238 395C212 395 165 379 127 364L153 550Z"></path></g></svg> ppm) and AWGN channel vs. the accuracy supremum (SUP).</div>

Wireless Communications and Mobile Computing

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Figure 2

Figure 2: On the Performance Supremum of CFO Based Physical Layer Identification 