Power allocation and user selection in multi-cell: multi-user massive MIMO systems

Submitted in fulfilment of the academic requirements for the degree of Master of Science (Msc) in Engineering, in the School of Electrical and Information Engineering (EIE), Faculty of Engineering and the Built Environment, at the University of the Witwatersrand, Johannesburg, South Africa, 2017The benefits of massive Multiple-Input Multiple-Output (MIMO) systems have made it a solution for future wireless networking demands. The increase in the number of base station antennas in massive MIMO systems results in an increase in capacity. The throughput increases linearly with an increase in number of antennas. To reap all the benefits of massive MIMO, resources should be allocated optimally amongst users. A lot of factors have to be taken into consideration in resource allocation in multi-cell massive MIMO systems (e.g. intra-cell, inter-cell interference, large scale fading etc.) This dissertation investigates user selection and power allocation algorithms in multi-cell massive MIMO systems. The focus is on designing algorithms that maximizes a particular cell of interest’s sum rate capacity taking into consideration the interference from other cells. To maximize the sum-rate capacity there is need to optimally allocate power and select the optimal number of users who should be scheduled. Global interference coordination has very high complexity and is infeasible in large networks. This dissertation extends previous work and proposes suboptimal per cell resource allocation models that are feasible in practice. The interference is introduced when non-orthogonal pilots are used for channel estimation, resulting in pilot contamination. Resource allocation values from interfering cells are unknown in per cell resource allocation models, hence the inter-cell interference has to be modelled. To tackle the problem sum-rate expressions are derived to enable power allocation and user selection algorithm analysis. The dissertation proposes three different approaches for solving resource allocation problems in multi-cell multi-user massive MIMO systems for a particular cell of interest. The first approach proposes a branch and bound algorithm (BnB algorithm) which models the inter-cell interference in terms of the intra-cell interference by assuming that the statistical properties of the intra-cell interference in the cell of interest are the same as in the other interfering cells. The inter-cell interference is therefore expressed in terms of the intra-cell interference multiplied by a correction factor. The correction factor takes into consideration pilot sequences used in the interfering cells in relation to pilot sequences used in the cell of interest and large scale fading between the users in the interfering cells and the users in the cell of interest. The resource allocation problem is modelled as a mixed integer programming problem. The problem is NP-hard and cannot be solved in polynomial time. To solve the problem it is converted into a convex optimization problem by relaxing the user selection constraint. Dual decomposition is used to solve the problem. In the second approach (two stage algorithm) a mathematical model is proposed for maximum user scheduling in each cell. The scheduled users are then optimally allocated power using the multilevel water filling approach. Finally a hybrid algorithm is proposed which combines the two approaches described above. Generally in the hybrid algorithm the cell of interest allocates resources in the interfering cells using the two stage algorithm to obtain near optimal resource allocation values. The cell of interest then uses these near optimal values to perform its own resource allocation using the BnB algorithm. The two stage algorithm is chosen for resource allocation in the interfering cells because it has a much lower complexity compared to the BnB algorithm. The BnB algorithm is chosen for resource allocation in the cell of interest because it gives higher sum rate in a sum rate maximization problem than the two stage algorithm. Performance analysis and evaluation of the developed algorithms have been presented mainly through extensive simulations. The designed algorithms have also been compared to existing solutions. In general the presented results demonstrate that the proposed algorithms perform better than the existing solutions.XL201

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

A Dual-Functional Massive MIMO OFDM Communication and Radar Transmitter Architecture

In this study, a dual-functional radar and communication (RadCom) system architecture is proposed for application at base-stations (BSs), or access points (APs), for simultaneously communicating with multiple user equipments (UEs) and sensing the environment. Specifically, massive multiple-input multiple-output (mMIMO) communication and orthogonal frequency-division multiplexing (OFDM)-based MIMO radar are considered with the objective to jointly utilize channel diversity and interference. The BS consists of a mMIMO antenna array, and radar transmit and receive antennas. Employing OFDM waveforms for the radar allows the BS to perform channel state information (CSI) estimation for the mMIMO and radar antennas simultaneously. The acquired CSI is then exploited to predict the radar signals received by the UEs. While the radar transmits an OFDM waveform for detecting possible targets in range, the communication system beamforms to the UEs by taking into account the predicted radar interference. To further enhance the capacity of the communication system, an optimum radar waveform is designed. Moreover, the network capacity is mathematically analyzed and verified by simulations. The results show that the proposed RadCom can achieve higher capacity than conventional mMIMO systems by utilizing the radar interference while simultaneously detecting targets

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

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

A tutorial on the characterisation and modelling of low layer functional splits for flexible radio access networks in 5G and beyond

The centralization of baseband (BB) functions in a radio access network (RAN) towards data processing centres is receiving increasing interest as it enables the exploitation of resource pooling and statistical multiplexing gains among multiple cells, facilitates the introduction of collaborative techniques for different functions (e.g., interference coordination), and more efficiently handles the complex requirements of advanced features of the fifth generation (5G) new radio (NR) physical layer, such as the use of massive multiple input multiple output (MIMO). However, deciding the functional split (i.e., which BB functions are kept close to the radio units and which BB functions are centralized) embraces a trade-off between the centralization benefits and the fronthaul costs for carrying data between distributed antennas and data processing centres. Substantial research efforts have been made in standardization fora, research projects and studies to resolve this trade-off, which becomes more complicated when the choice of functional splits is dynamically achieved depending on the current conditions in the RAN. This paper presents a comprehensive tutorial on the characterisation, modelling and assessment of functional splits in a flexible RAN to establish a solid basis for the future development of algorithmic solutions of dynamic functional split optimisation in 5G and beyond systems. First, the paper explores the functional split approaches considered by different industrial fora, analysing their equivalences and differences in terminology. Second, the paper presents a harmonized analysis of the different BB functions at the physical layer and associated algorithmic solutions presented in the literature, assessing both the computational complexity and the associated performance. Based on this analysis, the paper presents a model for assessing the computational requirements and fronthaul bandwidth requirements of different functional splits. Last, the model is used to derive illustrative results that identify the major trade-offs that arise when selecting a functional split and the key elements that impact the requirements.This work has been partially funded by Huawei Technologies. Work by X. Gelabert and B. Klaiqi is partially funded by the European Union's Horizon Europe research and innovation programme (HORIZON-MSCA-2021-DN-0) under the Marie Skłodowska-Curie grant agreement No 101073265. Work by J. Perez-Romero and O. Sallent is also partially funded by the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreements No. 101096034 (VERGE project) and No. 101097083 (BeGREEN project) and by the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 under ARTIST project (ref. PID2020-115104RB-I00). This last project has also funded the work by D. Campoy.Peer ReviewedPostprint (author's final draft