PHY2- Advanced and Massive MIMO systems

Tuesday, 19 June 2018, 16:30-18:00, E1 hall
Session chair: Venceslav Kafedžiski (SS. Cyril and Methodius University, Macedonia, the former Yugoslav Republic of)


16:30 – Energy-efficient Sources and Relay Precoding Design for Two-way Two-hop MIMO-AF Systems

Fabien Héliot and Rahim Tafazolli (University of Surrey, United Kingdom (Great Britain))
This paper proposes a novel approach for maximising the energy efficiency (EE) of the two-way two-hop multiple-input-multiple-output (MIMO) amplify-and-forward (AF) system by optimising its sources and relay precoders. More precisely, we derive a closed-form expression of the optimal source precoders (for a known relay precoder) and obtain the optimal relay precoder (for known source precoders) through quasiconvex optimisation. An alternating optimisation process is then utilised to maximise the EE of the system. Simulation results show that our novel approach can improve the EE of two-way MIMO-AF systems by 50% or more when compared with existing EE-based one-way or two-way MIMO-AF precoding schemes.


16:48 – Energy-efficient Joint Source and Relay Precoding for Two-hop MIMO-AF Two-Relay Networks

Fabien Héliot and Rahim Tafazolli (University of Surrey, United Kingdom (Great Britain))
This paper focuses on improving the energy efficiency of two-hop multiple-input-multiple-output (MIMO) amplify-and-forward (AF) systems having two relays (in parallel) by relying on optimisation theory. Although the joint optimisation of source and relays precoding structures is non-convex by nature, we first formulate the energy consumption of the system as an optimisation function and prove that the latter is unimodal when the source and/or relays precoding matrix are known. In turn, we derive an optimal source and robust relays precoding structures; joint optimisation of the source and relays precoding matrices is then performed via an iterative process. Simulation results show that our novel approach can reduce the energy consumption of two-hop MIMO-AF systems by up to 30% when compared to the most relevant existing approaches.


17:06 – Amplitude Quantization for Type-2 Codebook Based CSI Feedback in New Radio System

Honglei Miao, Markus Dominik Mueck and Michael Faerber (Intel Deutschland GmbH, Germany)
In 3GPP new radio system, two types of codebook, namely Type-1 and Type-2 codebook, have been standardized for the channel state information (CSI) feedback in the support of advanced MIMO operation. Both types of codebook are constructed from 2-D DFT based grid of beams, and enable the CSI feedback of beam selection as well as PSK based co-phase combining between two polarizations. Moreover, Type-2 codebook based CSI feedback reports the wideband and subband amplitude information of the selected beams. As a result, it is envisioned that more accurate CSI shall be obtained from the Type-2 codebook based CSI feedback so that better precoded MIMO transmission can be employed by the network. To reduce the CSI feedback signaling, 1 bit based subband amplitude with only two quantization levels is supported in combination to 3 bits based wideband amplitude feedback. Typically, wideband amplitude shall be calculated as the linear average amplitude of the beam over all subbands. However, due to the coarse subband amplitude quantization, it has been observed in case of joint wideband and subband amplitude feedback, the average based wideband amplitude can lead to a large amplitude quantization errors. In this paper, we study two methods for joint wideband and subband amplitude calculations. Specifically, both optimal and sub-optimal methods are proposed. The optimal method can achieve the minimum amplitude quantization errors at the cost of a relatively large computation complexity. And by virtue of a derived scaling factor, the sub-optimal method exhibits clearly smaller quantization error than the conventional linear average based method especially for the channel with large frequency selectivity.


17:24 – Space-Time Shift Keying and Constant-Envelope OFDM: A New Solution for Future Mm-waveMIMO Multicarrier Systems

Talha Faizur Rahman and Claudio Sacchi (University of Trento, Italy)
Future 5G systems are envisioned to support wide range of applications, from narrowband to broadband, by occupying varied spectrum bands from sub-6GHz to higher millimeter wave (mmWave) bands. In this regard, significant challenges in design and implementation of 5G radio have to be addressed both in uplink and downlink under suitable propagation conditions. One of the challenges is related to the consideration of enabling physical layer (PHY) in the deployment of robust transmission systems able to strike a trade-off between power and spectral resources efficiently through the use of advanced MIMO techniques. In this paper, we address such challenge by proposing a novel technique based on Space-time shift keying (STSK)-aided constant envelope orthorgonal frequency division multiplexing (CEOFDM) broadband over frequency-selective mmWave channels. The main idea is to blend the benefits of STSK and CE-OFDM in mmWave transmissions by keeping complexity of the system as low as possible. We show that the STSK-CEOFDM outperforms the state-of-the-art STSK-OFDMcomprehensively in the presence of nonideal power amplifier. In particular, STSK-CE-OFDM can achieve a very large performance gain over the STSK-OFDM counterpart signal in the presence of nonlinear amplification. For this reason, STSK-CE-OFDM can strike a favorable and flexible tradeoff between power and spectral efficiency in the framework of 5G mm-wave wireless communications. The price to be paid is an overall bandwidth increase with respect to conventional STSK-OFDM, due to the need of modulation indexes up to 2.0 radians.


17:42 – Compressed Sensing Based Channel Estimation in FDD Multi-user Massive MIMO Using Angle Domain Sparsity and Transmit Antenna Correlation

Venceslav Kafedziski (SS. Cyril and Methodius University, Macedonia, the former Yugoslav Republic of)
We study channel estimation in downlink Frequency Division Duplex multi-user massive MIMO systems. Due to the large number of antennas in massive MIMO, both channel estimation at the users, and channel state information (CSI) feedback to the base station (BS) is a challenging task. Here we use sparsity in the angle domain and the transmit antenna correlation for compressed sensing based channel estimation. We use an approach where the measurements from a group of users with similar transmit covariance matrices are fed back to BS, and BS does joint channel estimation using the multiple measurement vector (MMV) approach. We start with the physical channel model with scatterer clusters and then reduce it to the virtual channel model, where a sparse channel matrix is used, together with the angular domain unitary matrices. The users that share common clusters have common sparsity and use joint (MMV) estimation. Due to the channel structure with scatterer clusters, resulting in groups of channel coefficients, we also use group sparsity and joint group sparsity to improve estimation error. We use antenna selection to transmit training symbols at selected antennas only, which, together with the virtual channel model, results in partial DFT measurement matrix.