arXiv subjects

De Mi

Publications and source records attributed to De Mi.

23 records · Page 2Linked to original sources

arXiv2019

Pioneering Studies on LTE eMBMS: Towards 5G Point-to-Multipoint Transmissions

The first 5G (5th generation wireless systems) New Radio Release-15 was recently completed. However, the specification only considers the use of unicast technologies and the extension to point-to-multipoint (PTM) scenarios is not yet considered. To this end, we first present in this work a technical overview of the state-of-the-art LTE (Long Term Evolution) PTM technology, i.e., eMBMS (evolved Multimedia Broadcast Multicast Services), and investigate the physical layer performance via link-level simulations. Then based on the simulation analysis, we discuss potential improvements for the two current eMBMS solutions, i.e., MBSFN (MBMS over Single Frequency Networks) and SC-PTM (Single-Cell PTM). This work explicitly focus on equipping the current eMBMS solutions with 5G candidate techniques, e.g., multiple antennas and millimeter wave, and its potentials to meet the requirements of next generation PTM transmissions.

cs.NI↗

arXiv2019

Rate-Splitting for Multigroup Multicast Beamforming in Multicarrier Systems

In this paper, we consider multigroup multicast transmissions with different types of service messages in an overloaded multicarrier system, where the number of transmitter antennas is insufficient to mitigate all inter-group interference. We show that employing a rate-splitting based multiuser beamforming approach enables a simultaneous delivery of the multiple service messages over the same time-frequency resources in a non-orthogonal fashion. Such an approach, taking into account transmission power constraints which are inevitable in practice, outperforms classic beamforming methods as well as current standardized multicast technologies, in terms of both spectrum efficiency and the flexibility of radio resource allocation.

cs.IT↗

arXiv2018

Massive MIMO performance with imperfect channel reciprocity and channel estimation error

Channel reciprocity in time-division duplexing (TDD) massive multiple-input multiple-output (MIMO) systems can be exploited to reduce the overhead required for the acquisition of channel state information (CSI). However, perfect reciprocity is unrealistic in practical systems due to random radio-frequency (RF) circuit mismatches in uplink and downlink channels. This can result in a significant degradation in the performance of linear precoding schemes, which are sensitive to the accuracy of the CSI. In this paper, we model and analyse the impact of RF mismatches on the performance of linear precoding in a TDD multi-user massive MIMO system, by taking the channel estimation error into considerations. We use the truncated Gaussian distribution to model the RF mismatch, and derive closed-form expressions of the output signal-to-interference-plus-noise ratio for maximum ratio transmission and zero forcing precoders. We further investigate the asymptotic performance of the derived expressions, to provide valuable insights into the practical system designs, including useful guidelines for the selection of the effective precoding schemes. Simulation results are presented to demonstrate the validity and accuracy of the proposed analytical results.

cs.IT↗

arXiv2015

MmWave Massive MIMO Based Wireless Backhaul for 5G Ultra-Dense Network

Ultra-dense network (UDN) has been considered as a promising candidate for future 5G network to meet the explosive data demand. To realize UDN, a reliable, Gigahertz bandwidth, and cost-effective backhaul connecting ultra-dense small-cell base stations (BSs) and macro-cell BS is prerequisite. Millimeter-wave (mmWave) can provide the potential Gbps traffic for wireless backhaul. Moreover, mmWave can be easily integrated with massive MIMO for the improved link reliability. In this article, we discuss the feasibility of mmWave massive MIMO based wireless backhaul for 5G UDN, and the benefits and challenges are also addressed. Especially, we propose a digitally-controlled phase-shifter network (DPSN) based hybrid precoding/combining scheme for mmWave massive MIMO, whereby the low-rank property of mmWave massive MIMO channel matrix is leveraged to reduce the required cost and complexity of transceiver with a negligible performance loss. One key feature of the proposed scheme is that the macro-cell BS can simultaneously support multiple small-cell BSs with multiple streams for each smallcell BS, which is essentially different from conventional hybrid precoding/combining schemes typically limited to single-user MIMO with multiple streams or multi-user MIMO with single stream for each user. Based on the proposed scheme, we further explore the fundamental issues of developing mmWave massive MIMO for wireless backhaul, and the associated challenges, insight, and prospect to enable the mmWave massive MIMO based wireless backhaul for 5G UDN are discussed.

cs.IT↗

arXiv2015

A Novel Antenna Selection Scheme for Spatially Correlated Massive MIMO Uplinks with Imperfect Channel Estimation

We propose a new antenna selection scheme for a massive MIMO system with a single user terminal and a base station with a large number of antennas. We consider a practical scenario where there is a realistic correlation among the antennas and imperfect channel estimation at the receiver side. The proposed scheme exploits the sparsity of the channel matrix for the effective selection of a limited number of antennas. To this end, we compute a sparse channel matrix by minimising the mean squared error. This optimisation problem is then solved by the well-known orthogonal matching pursuit algorithm. Widely used models for spatial correlation among the antennas and channel estimation errors are considered in this work. Simulation results demonstrate that when the impacts of spatial correlation and imperfect channel estimation introduced, the proposed scheme in the paper can significantly reduce complexity of the receiver, without degrading the system performance compared to the maximum ratio combining.

cs.IT↗