References of "Tsinos, Christos G."
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See detailDownlink Transmit Design in Massive MIMO LEO Satellite Communications
Li, Ke-Xin; You, Li; Want, Jiaheng et al

in IEEE Transactions on Communications (2021)

Low earth orbit (LEO) satellite communication systems have attracted extensive attention due to their smaller pathloss, shorter round-trip delay and lower launch cost compared with geostationary ... [more ▼]

Low earth orbit (LEO) satellite communication systems have attracted extensive attention due to their smaller pathloss, shorter round-trip delay and lower launch cost compared with geostationary counterparts. In this paper, the downlink transmit design for massive multiple-input multiple-output (MIMO) LEO satellite communications is investigated. First, we establish the massive MIMO LEO satellite channel model where the satellite and user terminals (UTs) are both equipped with the uniform planar arrays. Then, the rank of transmit covariance matrix of each UT is shown to be no larger than one to maximize ergodic sum rate, which reveals the optimality of single-stream precoding for each UT. The minorization-maximization algorithm is used to compute the precoding vectors. To reduce the computation complexity, an upper bound of ergodic sum rate is resorted to produce a simplified transmit design, where the rank of optimal transmit covariance matrix of each UT is also shown to not exceed one. To tackle the simplified precoder design, we derive the structure of precoding vectors, and formulate a Lagrange multiplier optimization (LMO) problem building on the structure. Then, a low-complexity algorithm is devised to solve the LMO, which takes much less computation effort. Simulation results verify the performance of proposed approaches. [less ▲]

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See detailMassive MIMO Downlink Transmission for LEO Satellite Communications
Li, Ke-Xin; You, Li; Wang, Jiaheng et al

Poster (2021, September)

We investigate the downlink (DL) transmit strategy for massive multiple-input multiple-output (MIMO) low-earthorbit (LEO) satellite communication (SATCOM) systems, in which only the slow-varying ... [more ▼]

We investigate the downlink (DL) transmit strategy for massive multiple-input multiple-output (MIMO) low-earthorbit (LEO) satellite communication (SATCOM) systems, in which only the slow-varying statistical channel state information is known at the transmitter side. First, we establish the massive MIMO LEO satellite channel model, in which the uniform planar arrays are deployed at both the satellite and user terminals (UTs). Building on the rank-one property of satellite channel matrices, we show that transmitting a single data stream to each UT is optimal for the ergodic sum rate maximization. This result is of great importance for massive MIMO LEO SATCOM systems, since the sophisticated design of transmit covariance matrices is turned into that of precoding vectors, with no loss of optimality at all. Furthermore, we conceive an algorithm to compute the precoding vectors. Simulation results show the significant performance gains of the proposed approaches over the previous schemes. [less ▲]

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See detailA Novel Learning-based Hard Decoding Scheme and Symbol-Level Precoding Countermeasures
Mayouche, Abderrahmane UL; Alves Martins, Wallace UL; Tsinos, Christos G. et al

in IEEE Wireless Communications and Networking Conference (WCNC), Najing 29 March to 01 April 2021 (2021)

In this work, we consider an eavesdropping scenario in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve). In ... [more ▼]

In this work, we consider an eavesdropping scenario in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve). In this setting, we exploit machine learning (ML) tools to design a hard decoding scheme by using precoded pilot symbols as training data. Within this, we propose an ML framework for a multi-antenna hard decoder that allows an Eve to decode the transmitted message with decent accuracy. We show that MU-MISO systems are vulnerable to such an attack when conventional block-level precoding is used. To counteract this attack, we propose a novel symbol-level precoding scheme that increases the bit-error rate at Eve by obstructing the learning process. Simulation results validate both the ML-based attack as well as the countermeasure, and show that the gain in security is achieved without affecting the performance at the intended users. [less ▲]

Detailed reference viewed: 105 (1 UL)