Broad Coverage Precoder Design for Synchronization in Satellite Massive MIMO Systems

Guo, Weiran; Lu, An-An; Gao, Xiqi; Xia, Xiang-Gen

doi:10.1109/tcomm.2021.3075519

Cited by 11 publications

(3 citation statements)

References 32 publications

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“…4, the achievable rate is averaged over the considered time. Then, there are no more periodic variations of the achievable rate, but the achievable rate does not further increase for D S ≥ 65 km, which justifies the heuristic (20).…”

Section: B Inter-satellite Distancementioning

confidence: 53%

Beamspace MIMO for Satellite Swarms

Röper¹,

Matthiesen²,

Wübben³

et al. 2021

Preprint

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Systems of small distributed satellites in low Earth orbit (LEO) transmitting cooperatively to a multiple antenna ground station (GS) are investigated. These satellite swarms have the benefit of much higher spatial separation in the transmit antennas than traditional big satellites with antenna arrays, promising a massive increase in spectral efficiency. However, this would require instantaneous perfect channel state information (CSI) and strong cooperation between satellites. In practice, orbital velocities around 7.5 km/s lead to very short channel coherence times on the order of fractions of the inter-satellite propagation delay, invalidating these assumptions. In this paper, we propose a distributed linear precoding scheme and a GS equalizer relying on local position information. In particular, each satellite only requires information about its own position and that of the GS, while the GS has complete positional information. Due to the deterministic nature of satellite movement this information is easily obtained and no inter-satellite information exchange is required during transmission. Based on the underlying geometrical channel approximation, the optimal inter-satellite distance is obtained analytically. Numerical evaluations show that the proposed scheme is, on average, within 99.8 % of the maximum achievable rate for instantaneous CSI and perfect cooperation Index Terms-low Earth orbit (LEO), small-satellite swarms, MIMO satellite communications, distributed precoding, angle division multiple access

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Section: B Inter-satellite Distancementioning

confidence: 53%

Beamspace MIMO for Satellite Swarms

Röper¹,

Matthiesen²,

Wübben³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Authors in [268] investigate a pragmatic approach for massive MIMO implementation at a system and satellite payload level, which they extended in [269] to a heuristic and computationally efficient solution. In [270], broad coverage transmission is investigated for massive MIMO satellite systems, and a precoder design is proposed based on the min-max criterion. Further, Hybrid beamforming is investigated in [271] for LEO massive MIMO satellite systems to design an energy-efficient scheme that is robust CSI errors.…”

Section: B Massive Mimomentioning

confidence: 99%

Precoding for High Throughput Satellite Communication Systems: A Survey

Khammassi¹,

Kammoun²,

Alouini³

2022

Preprint

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With the expanding demand for high data rates and extensive coverage, high throughput satellite (HTS) communication systems are emerging as a key technology for future communication generations. However, current frequency bands are increasingly congested. Until the maturity of communication systems to operate on higher bands, the solution is to exploit the already existing frequency bands more efficiently. In this context, precoding emerges as one of the prolific approaches to increasing spectral efficiency. This survey presents an overview and a classification of the recent precoding techniques for HTS communication systems from two main perspectives: 1) a problem formulation perspective and 2) a system design perspective. From a problem formulation point of view, precoding techniques are classified according to the precoding objective, group, and level. From a system design standpoint, precoding is categorized based on the system architecture, the precoding implementation, and the type of the provided service. Further, practical system impairments are discussed, and robust precoding techniques are presented. Finally, future trends in precoding for satellites are addressed to spur further research.

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“…In [12], a zero-forcing (ZF) equalizer at the ground terminal is proposed to simultaneously receive transmission from two satellites. In [15], [22], [23], beamspace MIMO is adapted for ground to satellite communications, focusing on scenarios involving a single satellite. In [15], a precoder for DL transmission and an equalizer for receiving in the uplink (UL), for a single satellite communicating with multiple devices on ground, is proposed.…”

Section: A Related Literaturementioning

confidence: 99%

Distributed Downlink Precoding and Equalization in Satellite Swarms

Röper¹,

Matthiesen²,

Wübben³

et al. 2022

Preprint

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In this paper, we propose a novel approach for downlink transmission from a satellite swarm towards a ground station (GS). These swarms have the benefit of much higher spatial separation in the transmit antennas than traditional big satellites with antenna arrays, promising a massive increase in spectral efficiency. The resulting precoder and equalizer have very low demands on computational complexity, inter-satellite coordination and channel estimation. This is achieved by taking knowledge about the geometry between satellites and GS into account. For precoding, each satellite only requires its angle of departure (AoD) towards the GS and it turns out that almost optimal data rates can be achieved if the satellites transmit independent data streams. For the equalizer, the GS requires only knowledge about the angles of arrival (AoAs) from all satellites. Based on the underlying geometrical channel approximation, the optimal inter-satellite distance is obtained analytically. We show, that, by choosing a proper intersatellite distance, the proposed low-complexity approach achieves the theoretical upper bound in terms of data rate. Furthermore, a novel approach to increase the robustness of the proposed precoder and equalizer against imperfect AoD and AoA knowledge is proposed, by exploiting the statistics of the estimation error. Parts of this paper was presented at 2022 IEEE Wireless Communications and Networking Conference [1].

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Broad Coverage Precoder Design for Synchronization in Satellite Massive MIMO Systems

Cited by 11 publications

References 32 publications

Beamspace MIMO for Satellite Swarms

Beamspace MIMO for Satellite Swarms

Precoding for High Throughput Satellite Communication Systems: A Survey

Distributed Downlink Precoding and Equalization in Satellite Swarms

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