Exploiting deep learning in limited-fronthaul cell-free massive MIMO uplink

A cell-free massive multiple-input multiple-output (MIMO) uplink is considered, where quantize-and-forward (QF) refers to the case where both the channel estimates and the received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU) whereas in combine-quantize-and-forward (CQF), the APs send the quantized version of the combined signal to the CPU. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is exploited to convert the power allocation problem into a standard geometric programme (GP). We exploit the knowledge of the channel statistics to design the power elements. Employing large-scale fading (LSF) with a deep convolutional neural network (DCNN) enables us to determine a mapping from the LSF coefficients and the optimal power through solving the sum rate maximization problem using the quantized channel. Four possible power control schemes are studied, which we refer to as i) small-scale fading (SSF)-based QF; ii) LSF-based CQF; iii) LSF use-and-then-forget (UatF)-based QF; and iv) LSF deep learning (DL)-based QF, according to where channel estimation is performed and exploited and how the optimization problem is solved. Numerical results show that for the same fronthaul rate, the throughput significantly increases thanks to the mapping obtained using DCNN

Uplink Spectral and Energy Efficiency of Cell-Free Massive MIMO with Optimal Uniform Quantization

This paper investigates the performance of limited-fronthaul cell-free massive multiple-input multiple-output (MIMO) taking account the fronthaul quantization and imperfect channel acquisition. Three cases are studied, which we refer to as Estimate&Quantize, Quantize&Estimate, and Decentralized, according to where channel estimation is performed and exploited. Maximum-ratio combining (MRC), zero-forcing (ZF), and minimum mean-square error (MMSE) receivers are considered. The Max algorithm and the Bussgang decomposition are exploited to model optimum uniform quantization. Exploiting the optimal step size of the quantizer, analytical expressions for spectral and energy efficiencies are presented. Finally, an access point (AP) assignment algorithm is proposed to improve the performance of the decentralized scheme. Numerical results investigate the performance gap between limited fronthaul and perfect fronthaul cases, and demonstrate that exploiting relatively few quantization bits, the performance of limited-fronthaul cell-free massive MIMO closely approaches the perfect-fronthaul performance

Towards versatile access networks (Chapter 3)

Compared to its previous generations, the 5th generation (5G) cellular network features an additional type of densification, i.e., a large number of active antennas per access point (AP) can be deployed. This technique is known as massive multipleinput multiple-output (mMIMO) [1]. Meanwhile, multiple-input multiple-output (MIMO) evolution, e.g., in channel state information (CSI) enhancement, and also on the study of a larger number of orthogonal demodulation reference signal (DMRS) ports for MU-MIMO, was one of the Release 18 of 3rd generation partnership project (3GPP Rel-18) work item. This release (3GPP Rel-18) package approval, in the fourth quarter of 2021, marked the start of the 5G Advanced evolution in 3GPP. The other items in 3GPP Rel-18 are to study and add functionality in the areas of network energy savings, coverage, mobility support, multicast broadcast services, and positionin

Centralized Cell-Free Massive MIMO with Low-Resolution Fronthaul

The increasingly new data-hungry applications in our digital society now might no longer be handled efficiently by the current cellular networks. Cell-free massive MIMO network comes to resolve the traditional way of deploying wireless networks by blurring the cell boundaries. The network comprises a large number of access points (APs) which connect the users to a central processing unit (CPU) via fronthauls for coherent transmission and reception. It is expected that this network can provide a uniformly high data rate per user and per unit area. In this thesis, we study a centralized approach to cell-free massive MIMO that can further exploit its potential with considering a practical issue of limited-capacity fronthauls. We develop different schemes as well as strategies that make the centralized approach feasible. Thereby, we propose the use of low-resolution fronthauls and analyse its performance by making use of Bussgang theorem. The first part of this thesis considers a cell-free network with single-antenna APs, where a coarse scalar uniform quantizer is devised as an interface to the fronthauls. In the second part of this thesis, we extend the network to the case of multi-antenna APs, where two different processing schemes at the APs are studied: individual processing and joint processing. For each part, two strategies for acquiring the channel state information (CSI) under low-resolution fronthaul constraint are developed: estimate-and-quantize (EQ) and quantize-and-estimate (QE). We analyse the performance of both strategies and take them into account for deriving the achievable rate of the systems. Moreover, the scalability of the centralized approach is also discussed in terms of fronthaul load and AP processing. In the last part, we propose the use of a lattice vector quantizer at multi-antenna APs for the high-mobility and high-density scenario, in which two procedures for constructing the lattice codebook are developed

A White Paper on Broadband Connectivity in 6G

Executive Summary This white paper explores the road to implementing broadband connectivity in future 6G wireless systems. Different categories of use cases are considered, from extreme capacity with peak data rates up to 1 Tbps, to raising the typical data rates by orders-of-magnitude, to support broadband connectivity at railway speeds up to 1000 km/h. To achieve these goals, not only the terrestrial networks will be evolved but they will also be integrated with satellite networks, all facilitating autonomous systems and various interconnected structures. We believe that several categories of enablers at the infrastructure, spectrum, and protocol/algorithmic levels are required to realize the intended broadband connectivity goals in 6G. At the infrastructure level, we consider ultra-massive MIMO technology (possibly implemented using holographic radio), intelligent reflecting surfaces, user-centric and scalable cell-free networking, integrated access and backhaul, and integrated space and terrestrial networks. At the spectrum level, the network must seamlessly utilize sub-6 GHz bands for coverage and spatial multiplexing of many devices, while higher bands will be used for pushing the peak rates of point-to-point links. The latter path will lead to THz communications complemented by visible light communications in specific scenarios. At the protocol/algorithmic level, the enablers include improved coding, modulation, and waveforms to achieve lower latencies, higher reliability, and reduced complexity. Different options will be needed to optimally support different use cases. The resource efficiency can be further improved by using various combinations of full-duplex radios, interference management based on rate-splitting, machine-learning-based optimization, coded caching, and broadcasting. Finally, the three levels of enablers must be utilized not only to deliver better broadband services in urban areas, but also to provide full-coverage broadband connectivity must be one of the key outcomes of 6G

Cell-Free Massive MIMO: Challenges and Promising Solutions

Along with its primary mission in fulfilling the communication needs of humans as well as intelligent machines, fifth generation (5G) and beyond networks will be a virtual fundamental component for all parts of life, society, and industries. These networks will pave the way towards realizing the individuals’ technological aspirations including holographic telepresence, e-Health, pervasive connectivity in smart environments, massive robotics, three-dimensional unmanned mobility, augmented reality, virtual reality, and internet of everything. This new era of applications brings unprecedented challenging demands to wireless network, such as high spectral efficiency, low-latency, high-reliable communication, and high energy efficiency. One of the major technological breakthroughs that has recently drawn the attention of researchers from academia and industry to cope with these unprecedented demands of wireless networks is the cell-free (CF) massive multiple-input multiple-output (mMIMO) systems. In CF mMIMO, a large number of spatially distributed access points are connected to a central processing unit (CPU). The CPU operates all APs as a single mMIMO network with no cell boundaries to serve a smaller number of users coherently on the same time-frequency resources. The system has shown substantial gains in improving the network performance from different perspectives, especially for cell-edge users, compared it other candidate technologies for 5G networks, \ie co-located mMIMO and small-cell (SC) systems. Nevertheless, the full picture of a practical scalable deployment of the system is not clear yet. In this thesis, we provide more in-depth investigations on the CF mMIMO performance under various practical system considerations. Also, we provide promising solutions to fully realize the potential of CF mMIMO in practical scenarios. In this regard, we focus on three vital practical challenges, namely hardware and channel impairments, malicious attacks, and limited-capacity fronthaul network. Regarding the hardware and channel impairments, we analyze the CF mMIMO performance under such practical considerations and compare its performance with SC systems. In doing so, we consider that both APs and user equipment (UE)s are equipped with non-ideal hardware components. Also, we consider the Doppler shift effect as a source of channel impairments in dynamic environments with moving users. Then, we derive novel closed-form expressions for the downlink (DL) spectral efficiency of both systems under hardware distortions and Doppler shift effect. We reveal that the effect of non-ideal UEs is more prominent than the non-ideal APs. Also, while increasing the number of deployed non-ideal APs can limit the hardware distortion effect in CF mMIMO systems, this leads to an extra performance loss in SC systems. Besides, we show that the Doppler shift effect is more harsh in SC systems. In addition, the SC system operation is more suitable for low-velocity users, however, it is more beneficial to adopt CF mMIMO system for network operation under high-mobility conditions. Capitalizing on the latter, we propose a hybrid CF mMIMO/SC system that can significantly outperforms both CF mMIMO and SC systems by providing different mobility conditions with high data rates simultaneously. Towards a further improvement in the CF mMIMO performance under high mobility scenarios, we propose a novel framework to limit the performance degradation due to the Doppler shift effect. To this end, we derive novel expressions for tight lower bound of the average DL and uplink (UL) data rates. Capitalizing on the derived analytical results, we provide an analytical framework that optimizes the frame length to minimize the Doppler shift effect on DL and UL data rates according to some criterion. Our results reveal that the optimal frame lengths for maximizing the DL and UL data rates are different and depend mainly on the users' velocities. Besides, adapting the frame length according to the velocity conditions significantly limits the Doppler shift effect, compared to applying a fixed frame length. To empower the CF mMIMO systems with secure transmission against malicious attacks, we propose two different approaches that significantly increases the achievable secrecy rates. In the first approach, we introduce a novel secure DL transmission technique that efficiently limits the eavesdropper (Eve) capability in decoding the transmitted signals to legitimate users. Differently, in the second approach, we adopt the distinctive features of Reconfigurable intelligent surfaces (RIS)s to limit the information leakage towards the Eve. Regarding the impact of limited capacity of wired-based fronthaul links, we drive the achievable DL data rates assuming two different CF mMIMO system operations, namely, distributed and centralized system operations. APs and CPU are the responsible entities for carrying out the signal processing functionalities in the distributed and centralized system operations, respectively. We show that the impact of limited capacity fronthaul links is more prominent on the centralized system operation. In addition, while the distributed system operation is more preferable under low capacities of fronthaul links, the centralized counterpart attains superior performance at high capacities of fronthaul links. Furthermore, considering the distributed and centralized system operations, and towards a practical and scalable operation of CF mMIMO systems, we propose a wireless-based fronthaul network for CF mMIMO systems under three different operations, namely, microwave-based, mmWave-based, and hybrid mmWave/microwave. Our results show that the integration between the centralized operation and the hybrid-based fronthaul network provides the highest DL data rates when APs are empowered with signal decoding capabilities. However, integrating the distributed operation with the microwave-based fronthaul network achieves ultimate performance when APs are not supported with decoding capabilities