Delay aware optimal resource allocation in MU MIMO-OFDM using enhanced spider monkey optimization

In multiple users MIMO- OFDM system allocates the available resources to the optimal users is a difficult task. Hence the scheduling and resource allocation become the major problem in the wireless network mainly in case of multiple input and multiple output method that has to be made efficient. There is various method introduced to give an optimal solution to the problem yet it has many drawbacks. So we propose this paper to provide an efficient solution for resource allocation in terms of delay and also added some more features such as high throughout, energy efficient and fairness. To make optimal resource allocation we introduce optimization algorithm named spider monkey with an enhancement which provides the efficient solution. In this optimization process includes the scheduling and resource allocation, the SNR values, channel state information (CSI) from the base station. To make more efficient finally we perform enhanced spider - monkey algorithm hence the resource allocation is performed based on QoS requirements. Thus the simulation results in our paper show high efficiency when compared with other schedulers and techniques

Transmit antenna selection and user selection in multiuser MIMO downlink systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonMultiuser multiple input multiple output (MU-MIMO) systems play essential role in improving throughput performance and link reliability in wireless communications. This improvement can be achieved by exploiting the spatial domain and without the need of additional power and bandwidth. In this thesis, three main issues which are of importance to the data rate transmission have been investigated. Firstly, antenna selection in MU-MIMO downlink systems has been considered, where this technique can be e fficiently used to reduce the complexity and cost caused by radio frequency chains, associated with antennas, while keeping most of the diversity advantages of the system. We proposed a transmit antenna selection algorithm which can select an optimal set of antennas for transmission in descending order depending on the product of eigenvalues of users' effective channels. The capacity achieved by the proposed algorithm is about 99:6% of the capacity of the optimum search method, with much lower complexity. Secondly, user selection technology in MU-MIMO downlink systems has been studied. Based on the QR decomposition, we proposed a greedy suboptimal user selection algorithm which adopts the product of singular values of users' effective channels as a selection metric. The performance achieved by the proposed algorithm is identical to that of the capacity-based algorithm, with significant reduction in complexity. Finally, a proportional fairness scheduling algorithm for MU-MIMO downlink systems has been proposed. By utilising the upper triangular matrix obtained by applying the QRD on the users' effective channel matrices, two selection metrics have been proposed to achieve the scheduling process. The first metric is based on the maximum entry of the upper triangular matrix, while the second metric is designed using the ratio between the maximum and minimum entries of the triangular matrix multiplied by the product of singular values of effective channels. The two metric provide significant degrees of fairness. For each of these three issues, a different precoding method has been used in order to cancel the interuser interference before starting the selection process. This allows to investigate each precoding design separately and to evaluate the computational burden required for each design.Ministry of Higher Education-Ira

Resource Allocation Algorithm for MU-MIMO Systems with Double-Objective Optimization under the Existence of the Rank Deficient Channel Matrix

© 2013 IEEE. This paper proposes a double-objective optimization resource allocation algorithm for the multi-user multiple-input/multiple-output (MU-MIMO) system in the general wireless environment and demonstrates the maximum number of simultaneously supportable users and the achievable bit rates of users in the general wireless environment with full rank and rank-deficient channels. The double-objective joint optimization algorithm proposed in this paper simultaneously optimizes energy efficiency and system throughput by user selection and power allocation. On this basis, the proposed algorithm guarantees the different QoS requirements of various services, including rate requirements and delay requirements

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

A survey on hybrid beamforming techniques in 5G : architecture and system model perspectives

The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers' structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks. We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain

Resource Allocation for Coordinated Multipoint Joint Transmission System and Received Signal Strength Based Positioning in Long Term Evolution Network

The Long-Term Evolution Advanced (LTE-A) system are expected to provide high speed and high quality services, which are supported by emerging technologies such as Coordinated Multipoint (CoMP) transmission and reception. Dynamic resource allocation plays a vital role in LTE-A design and planning, which is investigated in this thesis. In addition, Received Signal Strength (RSS) based positioning is also investigated in orthogonal frequency division multiplexing (OFDM) based wireless networks, which is based on an industry project. In the first contribution, a physical resource blocks (PRB) allocation scheme with fuzzy logic based user selection is proposed. This work considers three parameters and exploit a fuzzy logic (FL) based criterion to categorize users. As a result, it enhances accuracy of user classification. This work improves system capacity by a ranking based PRBs allocation schemes. Simulation results show that proposed fuzzy logic based user selection scheme improves performance for CoMP users. Proposed ranking based greedy allocation algorithm cut complexity in half but maintain same performance. In the second contribution, a two-layer proportional-fair (PF) user scheduling scheme is proposed. This work focused on fairness between CoMP and Non-CoMP users instead of balancing fairness in each user categories. Proposed scheme jointly optimizes fairness and system capacity over both CoMP and Non-CoMP users. Simulation results show that proposed algorithm significantly improves fairness between CoMP and Non-CoMP users. In the last contribution, RSS measurement method in LTE system is analyzed and a realizable RSS measurement method is proposed to fight against multipath effect. Simulation results shows that proposed method significantly reduced measurement error caused by multipath. In RSS based positioning area, this is the first work that consider exploiting LTE’s own signal strength measurement mechanism to enhance accuracy of positioning. Furthermore, the proposed method can be deployed in modern LTE system with limited cost

Energy-aware resource allocation in next generation wireless networks : application in large-scale MIMO Systems

In this thesis, we investigate the resource allocation problem for wireless networks that incorporate large-scale multiple-input multiple-output (MIMO) systems. These systems are considered as key technologies for future 5G wireless networks and are based on using few hundreds of antennas simultaneously to serve tens of users in the same time-frequency resource. The gains obtained by large-scale MIMO systems cannot be fully exploited without adequate resource allocation strategies. Hence, the aim of this thesis is to develop energy-aware resource allocation solutions for large-scale MIMO systems that take into consideration network power cost. Firstly, this thesis investigates the downlink of a base station equipped with large-scale MIMO system while taking into account a non-negligible transmit circuit power consumption. This consumption involves that activating all RF chains does not always necessarily achieve the maximum sum-rate. Thus, we derive the optimal number of activated RF chains. In addition, efficient antenna selection, user scheduling and power allocation algorithms in term of instantaneous sum-rate are proposed and compared. Also, fairness is investigated by considering equal receive power among users. Secondly, this thesis investigates a large-scale MIMO system that incorporates energy harvesting that is a promising key technology for greening future wireless networks since it reduces network operation costs and carbon footprints. Hence, we consider distributed large-scale MIMO systems made up of a set of remote radio heads (RRHs), each of which is powered by both an independent energy harvesting source and the grid. The grid energy source allows to compensate for the randomness and intermittency of the harvested energy. Optimal on-line and off-line energy management strategies are developed. In addition, on-line energy management algorithm based on energy prediction is devised. The feasibility problem is addressed by proposing an efficient link removal algorithm and for better energy efficiency, RRH on/off operation is investigated. Thirdly, wireless backhauling was proposed as an alternative solution that enable low-cost connection between the small base stations and the macro base station in heterogeneous networks (HetNets). The coexistence of massive MIMO, HetNets and wireless backhauling is a promising research direction since massive MIMO is a suitable solution to enable wireless backhauling. Thus, we propose a new transmission technique that is able to efficiently manage the interference in heterogeneous networks with massive MIMO wireless backhaul. The optimal time splitting parameter and the allocated transmit power are derived. The proposed transmission technique is shown to be more efficient in terms of transmit power consumption than the conventional reverse time division duplex with bandwidth splitting. In this thesis, we developed efficient resource allocation solutions related to system power for wireless networks that incorporate large-scale MIMO systems under different assumptions and network architectures. The results in this thesis can be expanded by investigating the research problems given at the end of the dissertation

Multi-User Ultra-Massive MIMO for very high frequency bands (mmWave and THz): a resource allocation problem

A dynamic subarray allocation for multi-user massive MIMO systems working in very high frequency bands (mmWave and THz) is proposed as a promising technique to unleash the capacity limits in future cellular networks capable of supporting high consuming bandwidth applications