Multi-Antenna Techniques for Next Generation Cellular Communications

Future cellular communications are expected to offer substantial improvements for the pre- existing mobile services with higher data rates and lower latency as well as pioneer new types of applications that must comply with strict demands from a wider range of user types. All of these tasks require utmost efficiency in the use of spectral resources. Deploying multiple antennas introduces an additional signal dimension to wireless data transmissions, which provides a significant alternative solution against the plateauing capacity issue of the limited available spectrum. Multi-antenna techniques and the associated key enabling technologies possess unquestionable potential to play a key role in the evolution of next generation cellular systems. Spectral efficiency can be improved on downlink by concurrently serving multiple users with high-rate data connections on shared resources. In this thesis optimized multi-user multi-input multi-output (MIMO) transmissions are investigated on downlink from both filter design and resource allocation/assignment points of view. Regarding filter design, a joint baseband processing method is proposed specifically for high signal-to-noise ratio (SNR) conditions, where the necessary signaling overhead can be compensated for. Regarding resource scheduling, greedy- and genetic-based algorithms are proposed that demand lower complexity with large number of resource blocks relative to prior implementations. Channel estimation techniques are investigated for massive MIMO technology. In case of channel reciprocity, this thesis proposes an overhead reduction scheme for the signaling of user channel state information (CSI) feedback during a relative antenna calibration. In addition, a multi-cell coordination method is proposed for subspace-based blind estimators on uplink, which can be implicitly translated to downlink CSI in the presence of ideal reciprocity. Regarding non-reciprocal channels, a novel estimation technique is proposed based on reconstructing full downlink CSI from a select number of dominant propagation paths. The proposed method offers drastic compressions in user feedback reports and requires much simpler downlink training processes. Full-duplex technology can provide up to twice the spectral efficiency of conventional resource divisions. This thesis considers a full-duplex two-hop link with a MIMO relay and investigates mitigation techniques against the inherent loop-interference. Spatial-domain suppression schemes are developed for the optimization of full-duplex MIMO relaying in a coverage extension scenario on downlink. The proposed methods are demonstrated to generate data rates that closely approximate their global bounds

Two-tier channel estimation aided near-capacity MIMO transceivers relying on norm-based joint transmit and receive antenna selection

We propose a norm-based joint transmit and receive antenna selection (NBJTRAS) aided near-capacity multiple-input multiple-output (MIMO) system relying on the assistance of a novel two-tier channel estimation scheme. Specifically, a rough estimate of the full MIMO channel is first generated using a low-complexity, low-training-overhead minimum mean square error based channel estimator, which relies on reusing a modest number of radio frequency (RF) chains. NBJTRAS is then carried out based on this initial full MIMO channel estimate. The NBJTRAS aided MIMO system is capable of significantly outperforming conventional MIMO systems equipped with the same modest number of RF chains, while dispensing with the idealised simplifying assumption of having perfectly known channel state information (CSI). Moreover, the initial subset channel estimate associated with the selected subset MIMO channel matrix is then used for activating a powerful semi-blind joint channel estimation and turbo detector-decoder, in which the channel estimate is refined by a novel block-of-bits selection based soft-decision aided channel estimator (BBSB-SDACE) embedded in the iterative detection and decoding process. The joint channel estimation and turbo detection-decoding scheme operating with the aid of the proposed BBSB-SDACE channel estimator is capable of approaching the performance of the near-capacity maximumlikelihood (ML) turbo transceiver associated with perfect CSI. This is achieved without increasing the complexity of the ML turbo detection and decoding process

Energy efficiency and interference management in long term evolution-advanced networks.

Doctoral Degree. University of KwaZulu-Natal, Durban.Cellular networks are continuously undergoing fast extraordinary evolution to overcome technological challenges. The fourth generation (4G) or Long Term Evolution-Advanced (LTE-Advanced) networks offer improvements in performance through increase in network density, while allowing self-organisation and self-healing. The LTE-Advanced architecture is heterogeneous, consisting of different radio access technologies (RATs), such as macrocell, smallcells, cooperative relay nodes (RNs), having various capabilities, and coexisting in the same geographical coverage area. These network improvements come with different challenges that affect users’ quality of service (QoS) and network performance. These challenges include; interference management, high energy consumption and poor coverage of marginal users. Hence, developing mitigation schemes for these identified challenges is the focus of this thesis. The exponential growth of mobile broadband data usage and poor networks’ performance along the cell edges, result in a large increase of the energy consumption for both base stations (BSs) and users. This due to improper RN placement or deployment that creates severe inter-cell and intracell interferences in the networks. It is therefore, necessary to investigate appropriate RN placement techniques which offer efficient coverage extension while reducing energy consumption and mitigating interference in LTE-Advanced femtocell networks. This work proposes energy efficient and optimal RN placement (EEORNP) algorithm based on greedy algorithm to assure improved and effective coverage extension. The performance of the proposed algorithm is investigated in terms of coverage percentage and number of RN needed to cover marginalised users and found to outperform other RN placement schemes. Transceiver design has gained importance as one of the effective tools of interference management. Centralised transceiver design techniques have been used to improve network performance for LTE-Advanced networks in terms of mean square error (MSE), bit error rate (BER) and sum-rate. The centralised transceiver design techniques are not effective and computationally feasible for distributed cooperative heterogeneous networks, the systems considered in this thesis. This work proposes decentralised transceivers design based on the least-square (LS) and minimum MSE (MMSE) pilot-aided channel estimations for interference management in uplink LTE-Advanced femtocell networks. The decentralised transceiver algorithms are designed for the femtocells, the macrocell user equipments (MUEs), RNs and the cell edge macrocell UEs (CUEs) in the half-duplex cooperative relaying systems. The BER performances of the proposed algorithms with the effect of channel estimation are investigated. Finally, the EE optimisation is investigated in half-duplex multi-user multiple-input multiple-output (MU-MIMO) relay systems. The EE optimisation is divided into sub-optimal EE problems due to the distributed architecture of the MU-MIMO relay systems. The decentralised approach is employed to design the transceivers such as MUEs, CUEs, RN and femtocells for the different sub-optimal EE problems. The EE objective functions are formulated as convex optimisation problems subject to the QoS and transmit powers constraints in case of perfect channel state information (CSI). The non-convexity of the formulated EE optimisation problems is surmounted by introducing the EE parameter substractive function into each proposed algorithms. These EE parameters are updated using the Dinkelbach’s algorithm. The EE optimisation of the proposed algorithms is achieved after finding the optimal transceivers where the unknown interference terms in the transmit signals are designed with the zero-forcing (ZF) assumption and estimation errors are added to improve the EE performances. With the aid of simulation results, the performance of the proposed decentralised schemes are derived in terms of average EE evaluation and found to be better than existing algorithms

D 3. 3 Final performance results and consolidated view on the most promising multi -node/multi -antenna transmission technologies

This document provides the most recent updates on the technical contributions and research challenges focused in WP3. Each Technology Component (TeC) has been evaluated under possible uniform assessment framework of WP3 which is based on the simulation guidelines of WP6. The performance assessment is supported by the simulation results which are in their mature and stable state. An update on the Most Promising Technology Approaches (MPTAs) and their associated TeCs is the main focus of this document. Based on the input of all the TeCs in WP3, a consolidated view of WP3 on the role of multinode/multi-antenna transmission technologies in 5G systems has also been provided. This consolidated view is further supported in this document by the presentation of the impact of MPTAs on METIS scenarios and the addressed METIS goals.Aziz, D.; Baracca, P.; De Carvalho, E.; Fantini, R.; Rajatheva, N.; Popovski, P.; Sørensen, JH.... (2015). D 3. 3 Final performance results and consolidated view on the most promising multi -node/multi -antenna transmission technologies. http://hdl.handle.net/10251/7675

고속 이동 환경에서 대규모 다중안테나 시스템에 관한 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 이용환.Advanced cellular communication systems may obtain high array gain by employing massive multi-input multi-output (m-MIMO) systems, which may require accurate channel state information (CSI). When users are in high mobility, it may not be easy to get accurate CSI. When we transmit signal to users in high mobility, we may experience serious performance loss due to the inaccuracy of outdated CSI, associated with so-called channel aging effect. This problem may be alleviated by exploiting channel correlation matrix (CCM) in spatial domain. However, it may require an additional process for the estimation of CCM, which may require high signaling overhead in m-MIMO environments. In this dissertation, we consider signal transmission to multiple users in high mobility in m-MIMO environments. We consider the estimation of CSI with reduced signaling overhead. The signaling overhead for the CSI estimation is a challenging issue in m-MIMO environments. We may reduce the signaling overhead for the CSI estimation by using pilot signal transmitted by means of beamforming with a weight determined by eigenvectors of CCM. To this end, we need to estimate the CCM, which may still require large signaling overhead. We consider the estimation of CCM with antennas in a uniform linear array (ULA). Since pairs of antennas with an equal distance may experience spatial channel correlation similar to each other in ULA antenna environments, we may jointly estimate the spatial channel correlation. We estimate the mean-square error (MSE) of elements of estimated CCM and then discard the elements whose MSE is higher than a reference value for the improvement of CCM estimation. We may estimate the CSI from the estimated CCM with reduced signaling overhead. We consider signal transmission robust to the presence of channel aging effect. Users in different mobility may differently experience the channel aging effect. This means that they may differently suffer from transmission performance loss. To alleviate this problem, we transmit signal to maximize the average signal-to-leakage-plus-noise ratio, making it possible to individually handle the channel aging effect. We consider the signal transmission to the eigen-direction of a linear combination of CSI and CCM. Analyzing the transmission performance in terms of signal-to-interference-plus-noise ratio, we control the transmit power by using an iterative water-filling technique. Finally, we consider the allocation of transmission resource in the presence of channel aging effect. We design a sub-optimal greedy algorithm that allocates the transmission resource to maximize the sum-rate in the presence of channel aging effect. We may estimate the sum-rate from the beam weight and a hypergeometric function (HF) that represents the effect of outdated CSI on the transmission performance. However, it may require very high computational complexity to calculate the beam weight and the HF in m-MIMO environments. To alleviate the complexity problem, we determine the beam weight in dominant eigen-direction of CCM and approximate the HF as a function of temporal channel correlation. Since we may estimate the sum-rate by exploiting spatial and temporal channel correlation, we may need to update the resource allocation only when the change of CCM or temporal channel correlation is large enough to affect the sum-rate. Simulation results show that the proposed scheme provides performance similar to a greedy algorithm based on accurate sum-rate, while significantly reducing the computational complexity.기지국이 수많은 안테나를 활용하여 높은 전송 이득을 얻을 수 있는 대규모 다중 안테나(massive MIMO) 시스템이 차세대 무선 통신 시스템으로 각광받고 있다. 이를 위해서는 정확한 채널 정보(channel state information)를 기반으로 하는 신호 전송 및 자원 관리 기술이 필수적이다. 하지만 사용자가 고속으로 이동하는 환경에서는 기지국이 추정한 채널 정보와 실제 전송 채널이 크게 달라지는 채널 변화 효과(channel aging effect)가 발생하여, 시스템 전송 성능이 심각하게 하락할 수 있다. 위 문제를 해결하기 위하여, 상대적으로 사용자 이동성에 느리게 변화하는 공간 상관도 행렬(channel correlation matrix)을 활용할 수 있다. 하지만 대규모 다중 안테나 시스템에서는 기지국이 공간 상관도 행렬을 추정하는 과정에서 큰 파일럿(pilot) 신호 부담이 발생할 수 있다. 본 논문은 고속 이동 환경에서의 대규모 다중 안테나 시스템에서 다중 사용자에 대한 신호 전송을 고려한다. 우선, 낮은 파일럿 신호 부담을 갖는 채널 정보 추정 방법을 제안한다. 대규모 다중 안테나 시스템에서 채널 정보 추정은 큰 파일럿 신호 부담을 야기할 수 있다. 이때 공간 상관도 행렬을 활용한 파일럿 신호 설계를 통하여 채널 정보 추정으로 인한 신호 부담을 효과적으로 감소시킬 수 있다. 하지만 이를 위해서는 공간 상관도 행렬을 추정해야 하며, 이 과정에서 큰 신호 부담이 야기될 수 있다. 제안 기법은 기지국이 균일한 선형 안테나 배열(uniform linear array)을 가지고 있는 환경에서, 같은 거리의 안테나 쌍들의 채널 간 공간 상관도가 유사하다는 특징을 활용하여, 상기 안테나 쌍들의 채널 간 공간 상관도를 최소자승추정법(least-square estimation)을 활용하여 추정한다. 그리고 추정된 공간 상관도의 평균제곱오차(mean-square error)를 추정하여, 상기 평균제곱오차가 큰 공간 상관도를 0으로 치환하여 공간 상관도 행렬의 추정 정확도를 높인다. 또한 상기 추정한 공간 상관도 행렬을 활용하여 낮은 신호 부담으로 채널 정보를 추정할 수 있는 것을 보인다. 둘째로, 사용자 이동성에 의한 채널 변화에 강인한 신호 전송 방법을 제안한다. 사용자들이 서로 다른 속도로 이동하는 환경에서는 채널 변화에 의한 신호 전송 성능 저하 역시 사용자마다 다르게 나타날 수 있다. 위 문제를 해결하기 위하여, 각 사용자에 대한 채널 변화 효과를 개별적으로 고려하면서 평균 신호 대 누수간섭 및 잡음비(signal-to-leakage-plus-noise ratio)를 최대화하는 전송 빔 가중치를 설계한다. 제안 기법은 사용자들의 채널 정보와 공간 상관도 행렬의 선형 결합의 고유방향(eigen-direction)으로 신호를 전송한다. 또한 제안 기법을 사용할 때의 신호 대 간섭 및 잡음비(signal-to-interference-plus-noise ratio)를 분석하고, 이를 기반으로 하는 전송 전력 분배 방법을 제안한다. 끝으로, 사용자 이동성에 따른 채널 변화를 고려하는 자원 할당 방법을 제안한다. 이를 위하여, 상기 채널 변화를 고려하여 시스템 전송 성능(sum-rate)을 최대화하는 탐욕(greedy) 알고리듬 기반의 자원 할당 기술을 설계한다. 고속 이동 환경에서 시스템 전송 성능을 추정하기 위해서는 사용자들에 대한 전송 빔 가중치와 행렬에 대한 초기하 함수(hypergeometric function of a matrix argument)와 관련된 복잡한 연산이 필요하다. 이 문제를 해결하기 위하여, 빔 가중치를 공간 상관도 행렬의 고유방향으로 결정하고, 초기하 함수를 채널 시간 상관도에 대한 함수로 근사한다. 상기 전송 성능 추정 방법이 채널의 공간 및 시간 상관도에만 의존한다는 점을 활용하여, 채널 공간 및 시간 상관도가 크게 변화한 사용자가 존재할 때에 한하여 사용자들에 대한 자원 할당 상태를 갱신하는 방법을 제안한다. 실험을 통하여, 제안 기법이 복잡한 시스템 전송 성능을 기반으로 하는 자원 할당 방법과 유사한 자원 할당 성능을 보이면서도 계산 복잡도를 획기적으로 줄이는 것을 보인다.Abstract i Contents v List of Figures vii List of Tables ix Chapter 1. Introduction 1 Chapter 2. M-MIMO systems in the presence of channel aging effect 9 Chapter 3. Estimation of channel correlation matrix 13 3.1. Previous works 14 3.2. Proposed scheme 19 3.3. Performance evaluation 29 Chapter 4. Mobility-aware signal transmission in m-MIMO systems 43 4.1. Previous works 44 4.2. Proposed scheme 46 4.3. Performance evaluation 62 Chapter 5. Mobility-aware resource allocation in m-MIMO systems 73 5.1. Sum-rate-based greedy algorithm 74 5.2. Proposed scheme 76 5.3. Performance evaluation 88 Chapter 6. Conclusions 99 Appendix 103 References 105 Korean Abstract 115 Acknowledgement 119Docto

Massive MIMO for Internet of Things (IoT) Connectivity

Massive MIMO is considered to be one of the key technologies in the emerging 5G systems, but also a concept applicable to other wireless systems. Exploiting the large number of degrees of freedom (DoFs) of massive MIMO essential for achieving high spectral efficiency, high data rates and extreme spatial multiplexing of densely distributed users. On the one hand, the benefits of applying massive MIMO for broadband communication are well known and there has been a large body of research on designing communication schemes to support high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT) is still a developing topic, as IoT connectivity has requirements and constraints that are significantly different from the broadband connections. In this paper we investigate the applicability of massive MIMO to IoT connectivity. Specifically, we treat the two generic types of IoT connections envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC). This paper fills this important gap by identifying the opportunities and challenges in exploiting massive MIMO for IoT connectivity. We provide insights into the trade-offs that emerge when massive MIMO is applied to mMTC or URLLC and present a number of suitable communication schemes. The discussion continues to the questions of network slicing of the wireless resources and the use of massive MIMO to simultaneously support IoT connections with very heterogeneous requirements. The main conclusion is that massive MIMO can bring benefits to the scenarios with IoT connectivity, but it requires tight integration of the physical-layer techniques with the protocol design.Comment: Submitted for publicatio

Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks