The Achievable Rate Analysis of Generalized Quadrature Spatial Modulation and a Pair of Low-Complexity Detectors

Abstract

Generalized quadrature spatial modulation (GQSM) seamlessly amalgamates the generalized spatial modulation (GSM), quadrature spatial modulation (QSM) and vertical Bell Laboratories layered space-time (V-BLAST) techniques. In contrast to traditional multiple-input multiple-output (MIMO) schemes transmitting information bits only through the constellation defined on the complex plane, GQSM transmits additional information bits implicitly by selecting the transmit antenna (TA) activation pattern. The philosophy of QSM is that of separating the indices of the real and the imaginary parts of the transmit symbols, which increases the attainable throughput.In this paper, we analyze the inherent benefits of GQSM in terms of throughput. More specifically, we first derive the achievable rate expression of the recent GQSM scheme and unveil the condition for GQSM to approach the maximum rate. Furthermore, we compare the rate of the GQSM scheme to that of other transmission schemes and reveal the conditions for the GQSM's maximum throughput to exceed that of the benchmark schemes. Simulation results show that when the number of TAs is 40 and quadrature phase shift keying (QPSK) is adopted, the rate of the GQSM scheme may reach 150% of that of V-BLAST. Additionally, a pair of low-complexity detectors are conceivedone based on ordered successive interference cancellation (OSIC) and another one on the orthogonal matching pursuit (OMP) algorithm.

Fundamental Detection Probability vs. Achievable Rate Tradeoff in Integrated Sensing and Communication Systems

“…As a result, the instantaneous achievable rate in terms of bits-per-second-per-Hertz (b/s/Hz) at the CU can be expressed by [46]…”

“…, the PD can be numerically approximated by (46) at the top of the next page, where N is an adjustable parameter to strike a flexible tradeoff between the computational complexity and the fitting accuracy. Specifically, N denotes the number of pieces dividing the integral interval.…”

“…Specifically, N denotes the number of pieces dividing the integral interval. Upon increasing the value of N one could narrow the gap between the numerically approximated results of (46) and the true value of (42).…”

“…In this case, the sensing power required P S,min to meet the corresponding sensing requirements, i.e., (47c) and (47d), is obtained by solving the problem of P D = P D,min given the target PFA value quantified by P FA = P FA,δ , where P FA and P D are characterized by (41) and (46), respectively. Once the required sensing power has been determined, the optimal power allocation solution and the minimum transmit power are obtained by applying (51) and (52), respectively.…”

“…Case VIII: Sensing-Interfered Communication and Communication-Interfered Sensing (s s unknown at CU, both s c and h s unknown at SR) Similarly, the optimal power allocation solution and the minimum transmit power can be obtained by applying (54) and (55), respectively, in which P S,min is obtained by numerically solving P D = P D,min , while satisfying P FA = P FA,δ , where P FA and P D are characterized by (41) and (46), respectively.…”