เปรียบเทียบวิธี

	Polar Codes with Successive Cancellation Decoding	รหัสตรวจสอบพาริตีแบบความหนาแน่นต่ำ (LDPC)	ทฤษฎีความจุช่องสัญญาณของแชนนอน
สาขาวิชา	โทรคมนาคม	โทรคมนาคม	โทรคมนาคม
ตระกูล	Process / pipeline	Process / pipeline	Process / pipeline
ปีกำเนิด	2009	1962	1948
ผู้ริเริ่ม	Erdal Arikan	Robert Gallager	Claude Shannon
ประเภท	recursive error-correcting code	linear error-correcting code	fundamental theoretical bound
แหล่งต้นตำรับ	Arikan, E. (2009). Channel polarization: A method for constructing capacity-achieving codes for symmetric binary-input memoryless channels. IEEE Transactions on Information Theory, 55(7), 3051-3073.	Gallager, R. G. (1962). Low-density parity-check codes. IRE Transactions on Information Theory, 8(1), 21-28.	Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423.
ชื่อเรียกอื่น	channel polarization, recursive codes	sparse codes, belief propagation codes	channel capacity, information theory bound
ที่เกี่ยวข้อง	5	5	5
สรุป	Polar codes, introduced by Erdal Arikan in 2009, are the first constructive family of codes proven to achieve the Shannon capacity of symmetric binary-input memoryless channels. They use recursive construction and successive cancellation decoding, a simple greedy algorithm with theoretical guarantees. Polar codes were adopted in 5G NR for control channel coding and are studied for future 6G systems. Unlike turbo and LDPC codes (which are empirical), polar codes provide rigorous theoretical foundations.	LDPC codes, invented by Robert Gallager in 1962 and rediscovered in the 1990s by MacKay, are linear error-correcting codes defined by sparse parity-check matrices. They achieve performance within 0.4 dB of the Shannon limit with iterative belief-propagation decoding and have become the standard for modern wireless (WiFi-6, 5G NR, Digital Video Broadcasting). Unlike turbo codes, LDPC codes have a more elegant graph-theoretic structure and more mature theoretical analysis.	Shannon's channel capacity theorem, published in 1948, establishes the maximum rate at which information can be reliably transmitted over a noisy channel. Expressed as C = B log2(1 + S/N) for additive white Gaussian noise (AWGN), it is a fundamental bound in information theory and communications engineering. Shannon proved that reliable communication is possible at any rate below capacity, and impossible above it. This theorem underpins the design of all modern communication systems and motivates coding theory, modulation, and signal processing techniques.