Joint Pilot Allocation and Robust Transmission Design for Ultra-Dense User-Centric TDD C-RAN With Imperfect CSI

This paper considers the unavailability of complete channel state information (CSI) in ultra-dense cloud radio access networks (C-RANs). The user-centric cluster is adopted to reduce the computational complexity, while the incomplete CSI is considered to reduce the heavy channel training overhead, where only large-scale inter-cluster CSI is available. Channel estimation for intra-cluster CSI is also considered, where we formulate a joint pilot allocation and user equipment (UE) selection problem to maximize the number of admitted UEs with fixed number of pilots. A novel pilot allocation algorithm is proposed by considering the multi-UE pilot interference. Then, we consider robust beam-vector optimization problem subject to UEs' data rate requirements and fronthaul capacity constraints, where the channel estimation error and incomplete inter-cluster CSI are considered. The exact data rate is difficult to obtain in closed form, and instead we conservatively replace it with its lower-bound. The resulting problem is non-convex, combinatorial, and even infeasible. A practical algorithm, based on UE selection, successive convex approximation (SCA) and semi-definite relaxation approach, is proposed to solve this problem with guaranteed convergence. We strictly prove that semidefinite relaxation is tight with probability 1. Finally, extensive simulation results are presented to show the fast convergence of our proposed algorithm and demonstrate its superiority over the existing algorithms.Comment: Under revision in IEEE TW

User-centric C-RAN Architecture for Ultra-dense 5G Networks: Challenges and Methodologies

Ultra-dense networks (UDN) constitute one of the most promising techniques of supporting the fifth generation (5G) mobile system. By deploying more small cells in a fixed area, the average distance between users and access points can be significantly reduced, hence a dense spatial frequency reuse can be exploited. However, severe interference is the major obstacle in UDNs. Most of the contributions investigate the interference by designing distributed algorithms based on cooperative game theory. This paper advocates the application of dense user-centric cloud radio access network (CRAN) philosophy to UDNs, thanks to the recent development of cloud computing techniques. Under dense C-RAN architectures, centralized signal processing can be invoked for supporting Coordinated Multiple Points Transmission/Reception (CoMP). We summarize the main challenges in dense usercentric C-RANs. One of the most challenging issues is the requirement of the global CSI for the sake of cooperative transmission. We investigate this requirement by only relying on partial channel state information (CSI), namely, on inter-cluster large-scale CSI. Furthermore, the estimation of the intracluster CSI is considered, including the pilot allocation and robust transmission. Finally, we highlight several promising research directions to make the dense user-centric C-RAN become a reality, with special emphasis on the application of the ‘big data’ techniques

The Non-Coherent Ultra-Dense C-RAN Is Capable of Outperforming Its Coherent Counterpart at a Limited Fronthaul Capacity

The weighted sum rate maximization problem of ultra-dense cloud radio access networks (C-RANs) is considered, where realistic fronthaul capacity constraints are incorporated. To reduce the training overhead, pilot reuse is adopted and the transmit-beamforming used is designed to be robust to the channel estimation errors. In contrast to the conventional C-RAN where the remote radio heads (RRHs) coherently transmit their data symbols to the user, we consider their non-coherent transmission, where no strict phase-synchronization is required. By exploiting the classic successive interference cancellation (SIC) technique, we first derive the closed-form expressions of the individual data rates from each serving RRH to the user and the overall data rate for each user that is not related to their decoding order. Then, we adopt the reweighted l1 -norm technique to approximate the l0 -norm in the fronthaul capacity constraints as the weighted power constraints. A low-complexity algorithm based on a novel sequential convex approximation (SCA) algorithm is developed to solve the resultant optimization problem with convergence guarantee. A beneficial initialization method is proposed to find the initial points of the SCA algorithm. Our simulation results show that in the high fronthaul capacity regime, the coherent transmission is superior to the non-coherent one in terms of its weighted sum rate. However, significant performance gains can be achieved by the non-coherent transmission over the non-coherent one in the low fronthaul capacity regime, which is the case in ultra-dense C-RANs, where mmWave fronthaul links with stringent capacity requirements are employed

Robust Beamforming with Pilot Reuse Scheduling in a Heterogeneous Cloud Radio Access Network

© 1967-2012 IEEE. This paper considers a downlink ultradense heterogeneous cloud radio access network, which guarantees seamless coverage and can provide high date rates. In order to reduce channel state information (CSI) feedback overhead, incomplete intercluster CSI is considered, i.e., each remote radio head or macro base station only measures the CSI from user equipments (UEs) in its serving cluster. To reduce pilot consumption, pilot reuse among UEs is assumed, resulting in imperfect intracluster CSI. A two-stage optimization problem is then formulated. In the first stage, a pilot scheduling algorithm is proposed to minimize the sum mean square error (MSE) of all channel estimates. Specifically, the minimum number of required pilots along with a feasible pilot allocation solution are first determined by applying the Dsatur algorithm, and adjustments based on the defined level of pilot contamination are then carried out for further improvement. Based on the pilot allocation result obtained in the first stage, the second stage aims at maximizing the sum spectral efficiency (SE) of the network by optimizing the beam vectors. Due to incomplete intercluster CSI and imperfect intracluster CSI, an explicit expression of each UE's achievable rate is unavailable. Hence, a lower bound on the achievable rate is derived based on Jensen's inequality, and an alternative robust transmission design algorithm along with its distributed realization are then proposed to maximize the derived tight lower bound. Simulation results show that compared with the existing algorithms, the system performance can be greatly improved by the proposed algorithms in terms of both sum MSE and sum SE

A Comprehensive Survey on Resource Allocation for CRAN in 5G and Beyond Networks

The diverse service requirements coming with the advent of sophisticated applications as well as a large number of connected devices demand for revolutionary changes in the traditional distributed radio access network (RAN). To this end, Cloud-RAN (CRAN) is considered as an important paradigm to enhance the performance of the upcoming fifth generation (5G) and beyond wireless networks in terms of capacity, latency, and connectivity to a large number of devices. Out of several potential enablers, efficient resource allocation can mitigate various challenges related to user assignment, power allocation, and spectrum management in a CRAN, and is the focus of this paper. Herein, we provide a comprehensive review of resource allocation schemes in a CRAN along with a detailed optimization taxonomy on various aspects of resource allocation. More importantly, we identity and discuss the key elements for efficient resource allocation and management in CRAN, namely: user assignment, remote radio heads (RRH) selection, throughput maximization, spectrum management, network utility, and power allocation. Furthermore, we present emerging use-cases including heterogeneous CRAN, millimeter-wave CRAN, virtualized CRAN, Non- Orthogonal Multiple Access (NoMA)-based CRAN and fullduplex enabled CRAN to illustrate how their performance can be enhanced by adopting CRAN technology. We then classify and discuss objectives and constraints involved in CRAN-based 5G and beyond networks. Moreover, a detailed taxonomy of optimization methods and solution approaches with different objectives is presented and discussed. Finally, we conclude the paper with several open research issues and future directions

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Pilot Allocation and Sum-Rate Analysis in Cell-Free Massive MIMO Systems

This paper deals with the challenging issue of the unaffordable channel training overhead in the dense cell-free massive multi-input multi-output (MIMO) system when a high number of users are being simultaneously served. By adopting the user-centric cluster method, a dynamic pilot reuse (DPR) scheme is proposed to allow a pair of users to share a single pilot sequence. Specifically, the proposed reuse scheme is achieved with the objective of maximizing the uplink achievable sum-rate subject to users' signal to interference plus noise ratio (SINR) requirements and pilot resources constraints. On this basis, the SINR expression is derived for any user sharing its pilot with another by utilizing both minimum mean squared error (MMSE) detection and channel estimation. A low complexity pilot reuse algorithm is then developed based on the separation distance between users. The iterative grid search (IGS) method is employed to find the threshold that can be utilized in the proposed algorithm to maximize the sum-rate. Finally, simulation results are presented to show the effectiveness of the DPR scheme with the optimized threshold in terms of the uplink achievable sum-rate

Scalable Cell-Free Massive MIMO Systems

Imagine a coverage area with many wireless access points that cooperate to jointly serve the users, instead of creating autonomous cells. Such a cell-free network operation can potentially resolve many of the interference issues that appear in current cellular networks. This ambition was previously called Network MIMO (multiple-input multiple-output) and has recently reappeared under the name Cell-Free Massive MIMO. The main challenge is to achieve the benefits of cell-free operation in a practically feasible way, with computational complexity and fronthaul requirements that are scalable to large networks with many users. We propose a new framework for scalable Cell-Free Massive MIMO systems by exploiting the dynamic cooperation cluster concept from the Network MIMO literature. We provide a novel algorithm for joint initial access, pilot assignment, and cluster formation that is proved to be scalable. Moreover, we adapt the standard channel estimation, precoding, and combining methods to become scalable. A new uplink and downlink duality is proved and used to heuristically design the precoding vectors on the basis of the combining vectors. Interestingly, the proposed scalable precoding and combining outperform conventional maximum ratio processing and also performs closely to the best unscalable alternatives.Comment: To appear in IEEE Transactions on Communications, 14 pages, 6 figure