Multibeam joint detection

Arnau, Jesús; Lamare, Rodrigo C. de; Mosquera, Carlos

doi:10.1016/b978-0-12-799948-7.00001-3

Cited by 1 publication

(5 citation statements)

References 62 publications

(67 reference statements)

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“…The Jacobi iterative method can effectively reduce the computational complexity of traditional LMMSE detection 12 . First, the beam‐domain channel matrix can be divided as

bold \overset{bold\sim}{H} = boldD + boldR,

where D is a diagonal matrix composed of the diagonal elements in

bold \overset{bold\sim}{H}

, and R is the matrix composed of the other elements in

bold \overset{bold\sim}{H}

except for the diagonal elements.…”

Section: System Modelmentioning

confidence: 99%

“…In particular, assuming K is the number of beams, the computational complexity of matrix inverse is

O ((), K^{3})

, which is impractical in satellite communication systems with limited on‐board processing (OBP) ability. A lot of research has been done on the low‐complexity algorithm of matrix inverse in MMSE detection 11–14 . The computational complexity of MMSE detection can be effectively reduced by Jacobi iterative method (JIM) 11,12 .…”

Section: Introductionmentioning

confidence: 99%

“…A lot of research has been done on the low‐complexity algorithm of matrix inverse in MMSE detection 11–14 . The computational complexity of MMSE detection can be effectively reduced by Jacobi iterative method (JIM) 11,12 . Richardson iterative algorithm is an another effective method for large linear equations with a symmetric positive definite coefficient matrix 11,13 .…”

Section: Introductionmentioning

confidence: 99%

“…The symmetric successive over relaxation (SSOR) method can also reduce the computational complexity by exploits the channel hardening phenomenon, which means the off‐diagonal terms of the channel correlation matrix become increasingly weaker compared to the diagonal terms as the size of the channel gain matrix increases 11,14 . Although the mentioned methods in previous works 11–14 can reduce the computational complexity, the computational complexity still reaches

O ((), K^{2})

, which remains challenging for satellite mobile communications systems.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, based on the research of the traditional low‐complexity MMSE detection and just taking the Jacobi iteration method as an example, 11,12 we investigate the low‐complexity detection method for uplink MU‐MIMO, taking the satellite communication system with one‐dimensional beams as an example, such as Oneweb Low Earth Orbit (LEO) satellite system 15 shown in Figure 1. Utilizing the characteristics of uplink channel in beam domain, we simplify the beam‐domain channel matrix to reduce the computational complexity of MMSE detection.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Low‐complexity detection method based on channel matrix periodic N‐diagonal equivalence for uplink MU‐MIMO of multi‐beam satellite communication systems

Zhu

Zhang

et al. 2021

Satell Commun Network

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Summary In this paper, a low‐complexity detection method for uplink Multi‐User Multiple‐Input Multiple‐Output (MU‐MIMO) of multi‐beam satellite communication systems is studied. Firstly, we derive the uplink receiving system model and give the beam‐domain channel expression. Utilizing the characteristics of energy concentration of uplink channel in beam domain, we propose that the channel matrix can be equivalent to periodic N‐diagonal matrix, which can reduce the number of channel estimations and reduce the computational complexity of detection. Then, based on the beam‐domain channel matrix equivalent model, two low‐complexity minimum mean square error (MMSE) detection methods are proposed to reduce the computational complexity of traditional MMSE detection from O()K2 to O(K). Simulation results show that the proposed methods have limited performance loss compared with the traditional methods.

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“…The Jacobi iterative method can effectively reduce the computational complexity of traditional LMMSE detection 12 . First, the beam‐domain channel matrix can be divided as