Massive Access in Media Modulation Based Massive Machine-Type Communications

The massive machine-type communications (mMTC) paradigm based on media modulation in conjunction with massive MIMO base stations (BSs) is emerging as a viable solution to support the massive connectivity for the future Internet-of-Things, in which the inherent massive access at the BSs poses significant challenges for device activity and data detection (DADD). This paper considers the DADD problem for both uncoded and coded media modulation based mMTC with a slotted access frame structure, where the device activity remains unchanged within one frame. Specifically, due to the slotted access frame structure and the adopted media modulated symbols, the access signals exhibit a doubly structured sparsity in both the time domain and the modulation domain. Inspired by this, a doubly structured approximate message passing (DS-AMP) algorithm is proposed for reliable DADD in the uncoded case. Also, we derive the state evolution of the DS-AMP algorithm to theoretically characterize its performance. As for the coded case, we develop a bit-interleaved coded media modulation scheme and propose an iterative DS-AMP (IDS-AMP) algorithm based on successive inference cancellation (SIC), where the signal components associated with the detected active devices are successively subtracted to improve the data decoding performance. In addition, the channel estimation problem for media modulation based mMTC is discussed and an efficient data-aided channel state information (CSI) update strategy is developed to reduce the training overhead in block fading channels. Finally, simulation results and computational complexity analysis verify the superiority of the proposed algorithms in both uncoded and coded cases. Also, our results verify the validity of the proposed data-aided CSI update strategy.Comment: Accepted by IEEE Transactions on Wireless Communications. The codes and some other materials about this work may be available at https://gaozhen16.github.i

Dynamic Pilot Design for Multicast in the Internet of Vehicles Running at Different Speeds

High mobility of vehicles causes time-frequency selective fading over physical channels within the Internet of vehicles (IoV). To improve the resource utilisation efficiency, a novel transmission strategy, based on dynamic pilot design, is proposed in this paper to reduce the pilot consumption in doubly selective channel estimation for the multicast to vehicles running at different speeds. As the channel coherence time is mainly influenced by the receiver mobility in the multicast from a base station to vehicles, we define a multicast block as the channel coherence time of the slowest vehicle in the multicast group, where common pilot symbols are shared. Then, the multicast data destined for different vehicles are loaded into the block according to their own channel coherence times. To evaluate the performance and resource utilisation of our dynamic pilot design, the metrics of overhead rate, spectral efficiency, and energy efficiency are formulated for the IoV multicast using multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) transmissions. In terms of these three metrics, illustrative numerical results on the comparisons between our dynamic pilot design and the conventional counterpart are provided, which not only substantiate that the former outperforms the latter but also present useful tools and specifications for the pilot design in the IoV multicast using MIMO-OFDM transmissions over doubly selective channels

Low-Rank Channel Estimation for Millimeter Wave and Terahertz Hybrid MIMO Systems

Massive multiple-input multiple-output (MIMO) is one of the fundamental technologies for 5G and beyond. The increased number of antenna elements at both the transmitter and the receiver translates into a large-dimension channel matrix. In addition, the power requirements for the massive MIMO systems are high, especially when fully digital transceivers are deployed. To address this challenge, hybrid analog-digital transceivers are considered a viable alternative. However, for hybrid systems, the number of observations during each channel use is reduced. The high dimensions of the channel matrix and the reduced number of observations make the channel estimation task challenging. Thus, channel estimation may require increased training overhead and higher computational complexity. The need for high data rates is increasing rapidly, forcing a shift of wireless communication towards higher frequency bands such as millimeter Wave (mmWave) and terahertz (THz). The wireless channel at these bands is comprised of only a few dominant paths. This makes the channel sparse in the angular domain and the resulting channel matrix has a low rank. This thesis aims to provide channel estimation solutions benefiting from the low rankness and sparse nature of the channel. The motivation behind this thesis is to offer a desirable trade-off between training overhead and computational complexity while providing a desirable estimate of the channel

Channel Estimation for Massive MIMO Systems

Massive multiple input multiple output (MIMO) systems can significantly improve the channel capacity by deploying multiple antennas at the transmitter and receiver. Massive MIMO is considered as one of key technologies of the next generation of wireless communication systems. However, with the increase of the number of antennas at the base station, a large number of unknown channel parameters need to be dealt with, which makes the channel estimation a challenging problem. Hence, the research on the channel estimation for massive MIMO is of great importance to the development of the next generation of communication systems. The wireless multipath channel exhibits sparse characteristics, but the traditional channel estimation techniques do not make use of the sparsity. The channel estimation based on compressive sensing (CS) can make full use of the channel sparsity, while use fewer pilot symbols. In this work, CS channel estimation methods are proposed for massive MIMO systems in complex environments operating in multipath channels with static and time-varying parameters. Firstly, a CS channel estimation algorithm for massive MIMO systems with Orthogonal Frequency Division Multiplexing (OFDM) is proposed. By exploiting the spatially common sparsity in the virtual angular domain of the massive MIMO channels, a dichotomous-coordinate-decent-joint-sparse-recovery (DCD-JSR) algorithm is proposed. More specifically, by considering the channel is static over several OFDM symbols and exhibits common sparsity in the virtual angular domain, the DCD-JSR algorithm can jointly estimate multiple sparse channels with low computational complexity. The simulation results have shown that, compared to existing channel estimation algorithms such as the distributed-sparsity-adaptive-matching-pursuit (DSAMP) algorithm, the proposed DCD-JSR algorithm has significantly lower computational complexity and better performance. Secondly, these results have been extended to the case of multipath channels with time-varying parameters. This has been achieved by employing the basis expansion model to approximate the time variation of the channel, thus the modified DCD-JSR algorithm can estimate the channel in a massive MIMO OFDM system operating over frequency selective and highly mobile wireless channels. Simulation results have shown that, compared to the DCD-JSR algorithm designed for time-invariant channels, the modified DCD-JSR algorithm provides significantly better estimation performance in fast time-varying channels

Non-Coherent Massive MIMO-OFDM Down-Link Based on Differential Modulation

Orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) are wireless radio technologies adopted by the new Fifth Generation (5G) of mobile communications. A very large number of antennas (massive MIMO) is used to perform the beam-forming of the transmitted signal, either to reduce the multi-user interference (MUI), when spatially multiplexing several users, or to compensate the path-loss when higher frequencies than microwave are used, such as the millimeter-waves (mm-Waves). Usually, a coherent demodulation scheme (CDS) is used in order to exploit MIMO-OFDM, where the channel estimation and the pre/post-equalization processes are complex and time consuming operations, which require a considerable pilot overhead and also increase the latency of the system. As an alternative, non-coherent techniques based on a differential modulation scheme have been proposed for the up-link (UL). However, it is not straightforward to extend these proposals to the down-link (DL) due to the (usually) reduced number of antennas at the receiver side. In this paper we overcome this problem, and assuming that each user equipment (UE) is only equipped with one single antenna, we propose the combination of beam-forming with a differential modulation scheme for the DL, enhanced by the frequency diversity. The new transmission and reception schemes are described, and the signal-to-interference-plus-noise ratio (SINR) and the complexity are analysed. The numerical results verify the accuracy of the analysis and show that our proposal outperforms the existing CDS with a significant lower complexity.This work was supported by project TERESA-ADA (TEC2017-90093-C3-2-R) (MINECO/AEI/FEDER, UE)