A System View on Iterative MIMO Detection: Dynamic Sphere Detection versus Fixed Effort List Detection

Table 4

Design overview for individual components in open-loop and closed-loop systems. Showing them in columns next to each other gives a good overview of individual design problems, throughputs, and constraints even if they are not put in a system yet.

Component	QR decomposition	MIMO sphere	MIMO fixed effort	Convolutional decoder
Flexibility	2×2 or 4×4 matrices	up to 4×4 antennas, up to 64-QAM	4×4 antennas, 16-QAM or 64-QAM	code rates 0.5–1
Throughput depends on	Number of antennas, sorting, MMSE or zero-forcing	Modulation, number of antennas, radius	Modulation, number of antennas, list size	Constant
Communications performance depends on	MMSE/zero-forcing, sorted/unsorted	radius	list size	—
Throughput range (4×4, 64-QAM)	≥43 Mbit/s (ergodic)	38–58 Mbit/s	109 Mbit/s (fixed effort det.), 55 Mbit/s (LLR)	300 Mbit/s
Open loop		Best communications performance	Good communications performance	Low complexity Viterbi algorithm with hard output
		Dynamic throughput over SNR	List storage not required
Area	0.14mm^2 (P&R)	0.26mm^2 (P&R)	0.36+0.14mm^2 (P&R)	0.11mm^2 [31]
	+0.06mm^2 memories	+0.032mm^2 memories	+0.032mm^2 memories	+0.032 mm^2 memories
Closed loop	No further processing necessary	Best communications performance	Gets stuck after 2nd iteration	BCJR algorithm with soft-output of the parity information
		Dynamic throughput over iterations	For one feedback loop good throughput
Area	0.14mm^2 (P&R)	0.26mm^2 (P&R)	0.36+0.14mm^2 (P&R)	0.31mm^2 [32]
	+0.11mm^2 memories	+0.146mm^2 memories	+0.146mm^2 memories	+0.016mm^2 memories
			+0.32mm^2 list storage