The recently introduced 5G New Radio is the first wireless standard natively designed to support critical and massive machine type communications (MTC). However, it is already becoming evident that some of the more demanding requirements for MTC cannot be fully supported by 5G networks. Alongside, emerging use cases and applications towards 2030 will give rise to new and more stringent requirements on wireless connectivity in general and MTC in particular. Next generation wireless networks, namely 6G, should therefore be an agile and efficient convergent network designed to meet the diverse and challenging requirements anticipated by 2030. This paper explores the main drivers and requirements of MTC towards 6G, and discusses a wide variety of enabling technologies. More specifically, we first explore the emerging key performance indicators for MTC in 6G. Thereafter, we present a vision for an MTC-optimized holistic end-to-end network architecture. Finally, key enablers towards (1) ultra-low power MTC, (2) massively scalable global connectivity, (3) critical and dependable MTC, and (4) security and privacy preserving schemes for MTC are detailed. Our main objective is to present a set of research directions considering different aspects for an MTC-optimized 6G network in the 2030-era.

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Massive number of internet of things (IoT) devices are expected to simultaneously connect to the mMTC and beyond future generations of wireless network, posing severe challenge to aspects such as RACH procedure, user equipment detection and channel estimation. Although spatial combining has provided significant gains in conventional grant-based transmission, this technique is stuck in dilemma when it comes to autonomous grant-free transmission tailored for IoT use cases. To address this, blind spatial combining and its incorporation in the dataonly MUD are elaborated in this paper answering to both the academic and industry's concern in the overloading potential of autonomous grant-free (AGF) transmission. Blind spatial combining could be interpreted as blind receive beamforming heuristically. Simulation results show that the blind spatial combining enhanced data-only MUD performance for AGF transmission is rather impressive.

Cell-free massive MIMO provides ubiquitous connectivity for multiple users, and implementation using radio stripes is very efficient. This paper proposes a parallel scheme for radio stripes to allow access point to do signal processing simultaneously. Simple maximum ratio (MR) processing works in this scheme, but its performance is bad. Therefore, this paper further proposes two efficient parallel schemes to gain better performance. The first is interferenceaware MR processing with a tailored user-centric strategy. The second is distributed regularized zero-forcing (D-RZF) algorithm which derives from LMMSE. Simulation results show that the proposed parallel schemes gain better performance than existing works. Keywords-Cell-free massive MIMO, radio stripes, parallel schemes, distributed regularized zero-forcing I. INTRODUCTIONMassive multi-input multi-output (MIMO) is an efficient method to achieve extremely high spectral efficiency, and has been proved to be a very successful 5G technology. It still plays an important role in beyond 5G and 6G [1] to fulfill the key performance indicators of higher spectral efficiency and higher connection density. Many novel schemes using massive MIMO are proposed to further improve the performance. Cell-free massive MIMO [2] is a promising one among them.Conventional massive MIMO usually deploys a large number of antennas at base station (BS). It is named collocated massive MIMO, which has relatively low deployment cost. However, far-end users suffer from large path loss. To solve this problem, cell-free massive MIMO [2] was proposed. Cell-free massive MIMO is one kind of distributed antenna system where a large number of access points (APs) are deployed at different positions. Each AP can have single or several antennas. In the uplink, AP simply processes the received signal and then delivers it to a central processing unit (CPU). Then, CPU recovers transmit data from different users. Cell-free massive MIMO is able to gain diversity against path loss and provide uniformly good performance for different users.One classical type of cell-free massive MIMO is that APs connect to CPU via their own front-hauls. In early times, MR processing was advocated for its simplicity [2]. Usercentric (UC) strategy [3] can also be applied in MR to reduce the front-haul. Using UC, each user only requires signals from a fraction of APs, and the performance can be even better than original MR in some special cases. [4] compared different levels of cell-free implementations and found that a centralized scheme with optimal linear minimum-meansquare-error (LMMSE) processing achieves both high performance and low front-haul loading. Furthermore, low-resolution analog-to-digital converters (ADCs) was also suggested to reduce both power consumption and cost of cell-free massive MIMO [5] .Another cell-free implementation method using radio stripes [6] was also proposed for dense scenarios, e.g., stadiums, stations and malls. Multiple APs share one fronthaul cable for synchronization, data transmission a...