Robust precoding for joint transmission in multicell multiuser downlink systems

In this chapter, an effective blind source separation (BSS) algorithm is applied to solve the co-channel interference problem in wireless communication systems. Algorithms developed for this purpose must not only have the capability of working in the complex domain and improving output signal to interference plus noise ratio (SINR), but also have relatively low computational complexity. We propose a fast Fourier transform (FFT)-based algorithm called feedback independent component analysis (FebICA) that is able to blindly separate complex modulated digital signals. By applying this algorithm to communication signals, it is observed that it has the advantages of SINR gain improvement as well as low computational complexity. The performance of the FebICA algorithm is shown to be better than the joint approximate diagonalization of eigen-matrices (JADE) algorithm in terms of the output SINR and requires lower computational complexity than the analytical constant modulus algorithm (ACMA). The algorithm is also shown to be more robust with increasing number of sources compared to other algorithms. The separation performance by using the collected field data has also been demonstrated.

In this paper, we investigate link adaption in massive Multiple-Input and Multiple-Output(MIMO) system, where the base station is equipped with a large number of antennas. Specifically for the linear minimum mean square error (MMSE) receiver at the base station, the achievable ergodic rate is used to calculate channel quality indicator (CQI) for each user equipment (UE). We propose a rate calculation method based on the free deterministic equivalent, which relies on the statistical channel state information (CSI). The proposed method has much lower computational complexity than the conventional link adaption method such as exponential effective signal to interference plus noise ratio (SINR) mapping (EESM). Simulation results show that the the spectral efficiency obtained by the proposed method can approach almost the same level as EESM, while much lower computational complexity is needed.

In the first part of the thesis we investigate bandwidth allocation in next generation cellular systems employing relays similar to LTE advanced systems with type-I relays. We jointly optimize the bandwidth and power usage under constraints on required rate, bandwidth and transmit power. We study scenarios wherein, the relay acts as a forwarder for multiple User Equipments (UEs/users) in both uplink and/or downlink. This includes scenarios when the relay has its own data to send along with forwarding the data of other users. We examine the weighted power minimization problem for relaying with multiple users. We also show specifi c results with N user scenario and also single user case in order to understand how the bandwidth and power are allocated. Numerical evaluations with N users on a three sector LTE-A cell employing Fractional Frequency Reuse (FFR) indicate that power savings of at least 3 dB can be achieved by optimizing over both bandwidth and power. Base stations with a large number of transmit antennas have the potential to serve a large number of users simultaneously at higher rates. They also promise a lower power consumption due to coherent combining at the receiver. However, the receiver processing in the uplink relies on the channel stimates which are known to su ffer from pilot contamination. In the second part of the thesis, we perform an uplink large system analysis of multi-cell multi-antenna system when the receiver employs a matched fi ltering and MMSE fi ltering with a pilot contaminated estimate. We find the asymptotic Signal to Interference plus Noise Ratio (SINR) as the number of antennas and number of users per base station grow large while maintaining a fixed ratio. To do this, we make use of the similarity of the uplink received signal in a multi-antenna system to the representation of the received signal in CDMA systems. The asymptotic SINR expression for both matched fi lter and the MMSE fi lter explicitly captures the e ffect of pilot contamination and that of interference averaging. This also explains the SINR performance of receiver processing schemes at di fferent regimes such as instances when the number of antennas are comparable to number of users as well as when antennas exceed greatly the number of users. Specifi cally, we explore the scenario where the number of users being served are comparable to the number of antennas at the base station. It is seen that MMSE fi lter is capable of suppressing the in-cell interference and that the interference power due to pilot contamination is the same as in a matched fi lter with a pilot contaminated estimate. We find the expression for the amount of interference suppression obtained using an MMSE filter which is an important factor when there are signi ficant number of users in the system as compared to the number of antennas. We validate the asymptotic expression through simulations and compare with an MMSE fi lter with a perfect estimate. Simulation results for achievable rate is close to theory for even a 10-antenna base station with 3 or more users per cell. In a typical set up, in terms of the five percentile SIR, the MMSE fi lter is shown to provide signifi cant gains over matched fi ltering and is within 5 dB of MMSE fi lter with perfect channel estimate. We also show that the achievable rates are within a 1 bit/symbol of the MMSE with perfect estimate when the number of users is comparable to the number of antennas.

Recently, the use of LTE in the unlicensed spectrum has gained a lot of attention to address bandwidth crunch. Due to restriction on the transmission power in the unlicensed spectrum, LTE-U/LAA cells will get deployed mostly inside residential and office buildings to provide high data rates for indoor User Equipments (UEs). The UEs with high Signal-to-Interference plus Noise Ratio (SINR) experience good throughput, but the SINR decreases significantly due to walls and other obstacles that are present in the communication path. The coverage difference of licensed and unlicensed spectrum leads to different SINRs so, efficient placement of LAA Base Stations (BSs) inside the buildings is very crucial for attaining desirable SINR for the indoor UEs. Our primary objective is to find the optimal number of LAA/LTE-U BSs with minimum SINR requirements in the unlicensed band, and the secondary objective is to maximize the minimum SINR inside the building in the licensed band with power control. To do this, we formulate an optimization model (MinLF) to find the minimum number of LAA/LTE-U BSs along with their positions in indoor scenarios which resulted in Mixed Integer Linear Programming (MILP) problem. Further, to maximize the minimum SINR in the licensed spectrum, we formulate MinLPower model to find the optimal transmission power of each LTE-U/LAA cell inside the building which resulted in Mixed Integer Non-Linear Programming (MINLP) problem. Our proposed models achieve optimal placement with a maximum energy saving of 88% compared to the maximum power scheme.

In a conventional cellular network, a terminal receives signals not only from the base station of that cell, but also from other cell base stations. This inter-cell interference has more impact on the cell-edge users, in multi cell environment. The inter-cell interference degrades the performance of wireless systems. Using Base Station Cooperation, the ability of a mobile station to receive signals from multiple base stations can be utilized as an opportunity to improve the spectral efficiency and to get higher data rates for cell edge users. In multi cell environment using base station cooperation overall interference can be minimized marginally, whereas the interference within the cooperation region is largely reduced. This leads to a question whether it is worth doing cooperation all the time. However increase in terms of throughput may not always be enough to increase the throughput of each of the user .Hence for such a scenario the distinct cooperation scheme is used. The distinct cooperation is a joint transmission scheme in which the selection criterion is based on user’s signal to interference plus noise ratio (SINR) is proposed and the capacity achieved through cooperation is shared equally among the cell-edge users. Here we will analyze the normal operation, full cooperation and distinct cooperation scheme in the downlink environment for cell edge users in cellular network and we will compare and see which method improves the user capacity and data rate.KeywordsCooperative transmissionfull cooperationDistinct cooperationsignal to interference plus noise ratio (SINR)

This paper investigates max-min beamforming for the multicell downlink transmission, where multiple base stations (BSs) cooperatively optimize their transmit beamforming vectors using long-term channel statistics. Based on Lagrangian duality, we reformulate the original problem into a dual uplink problem, which is expected to achieve the same optimal signal to interference plus noise ratio (SINR) as the primal downlink problem. The normalized dual uplink beamformer is the dominant eigenvector of a generalized eigenvalue decomposition (GEVD) problem, and the dual uplink transmit power can be solved iteratively. According to the duality theory, we develop an iterative algorithm and compare it with the beamformer-power iterative algorithm extended from [1]. Simulation results verify the computational efficiency of the proposed algorithm.

The second part of the study of beamforming issues, based on positioning in ultra-dense millimeter wave radio access networks, is devoted to the formalization and software implementation of a complex simulation model of the functioning of a set of directional radio links. Each directional radio link between a base station (gNodeB – gNB), equipped with an antenna array, and a user equipment (UE), operating in omnidirectional mode, is formed according to the location of the UE, known at the gNB. The set of gNB→UE directional radio links, simultaneously operating in a common frequency range, is studied as a set of traffic beams, that implement space division multiple access (SDMA). Spatial multiplexing is implemented through three-dimensional beamforming at the gNB and makes it possible to compensate for propagation losses and high levels of interference. In the first part of the study, it was shown that the problem of practical implementation of SDMA in ultra-dense radio access networks is a significant (tens of dB) spread in the signal to interference plus noise ratio (SINR), depending on the arrangement of two devices. The purpose of this study is to establish the dependence of SINR on 1) the beamwidth of the gNB sector in the direction of the user equipment in the radio link of the signal of interest (SOI); 2) uncertainty of the UE location; 3) interference from radio links of signal not of interest (SNOI): a) within its sector, b) other sectors of its cell and c) other cells in the network. The simulation model developed and implemented in software in this work for the first time made it possible to establish the interdependence of the UE positioning error factors and the required width of the traffic beam for its service. In particular, it was found, that as the positioning error decreases from 10 to 1 m, the required beam width in the horizontal and vertical planes narrows to 3 °, which makes it possible to increase the SINR to 25 dB. A simultaneous transmission multiplexing study showed that for 64 spatially multiplexed UEs, as the cell size increases from 20 to 300 m, the SINR increases by approximately 30 dB, subject to a beamwidth constraint of 3°. Unlike similar studies, in this model, the contribution from interference from simultaneously operating traffic beams within its sector, other sectors of its cell and other cells in the network is shown separately for the first time, which allows to differentiate the origin of interference and use scientifically based beamwidth control for their compensation.

The aim of this paper is to present a non‐linear statistical model to fit and forecast the signal‐to‐interference plus noise ratio (SINR) in two‐tier heterogeneous cellular networks which consist of macrocells and femtocells. Since in these networks the number and locations of femtocell base stations (FBS) are variable, SINR forecasting can be useful in some areas such as power control and handover management. So far, linear autoregressive (AR) models have commonly been used in forecasting the received signal strength (rss) in macrocellular networks. However, AR modelling results in high mean square error (MSE) when data are non‐linear. This paper focuses on SINR which takes into account signal strength, interference and noise effects. Moreover, macro‐femto cellular network is considered. The F ‐test results show that the SINR data are non‐linear, leading to use non‐linear models instead of AR model. A non‐linear logistic smooth threshold AR (LSTAR) model is utilised to model and forecast the SINR data. Kolmogorov–Smirnov (K‐S) test demonstrates that LSTAR provides good fitness to the SINR samples. The results indicate that LSTAR model achieves much better performance in modelling and forecasting of SINR data than the AR model.

The Third Generation Partnership Project (3GPP) has been studying dynamic allocation of sub-frames to uplink (UL) or downlink (DL) in Time Division Duplex (TDD), called ‘Dynamic TDD,’ since the Long Term Evolution (LTE) Rel. 11 timeframe. At the same time, 3GPP is also standardizing Enhanced Local Area (eLA) small-cell heterogeneous architectures for inclusion in LTE-B (LTE Rel. 12) as a solution offering high data rate to user terminals (UEs) along with high system capacity through spatial reuse of spectrum. In this paper, we focus on a particular eLA architecture proposed by DOCOMO, called the Phantom Cell architecture, that has the option to support dynamic TDD. For an arbitrarily-located UE in an eLA cell network, we apply results from stochastic geometry to derive expressions for the distribution of DL signal to interference plus noise ratio (SINR) at an arbitrary UE and the distribution of UL SINR at its serving eLA base station (BS). We use these results to study aspects of eLA cell system design, and the sensitivity of SINR to the extent of coordination across eLA cells employing dynamic TDD.

We consider heterogeneous network (HetNet) systems comprising of two different types of base-stations (BSs): macro and small BSs. In the previous studies for HetNet, it is observed that the distribution of signal to interference plus noise ratio (SINR) is independent of the BS density. This implies that network throughput increases linearly with the number of BSs. In this paper, however, we have identified key practical factors which degrade the SINR distribution for dense HetNet. On the contrary to the previous studies, limited network throughput is verified by evaluating the areal capacity with respect to the density of BS under the practical channel model. The observation is made regardless of the deployment scenario whether the two types of BSs share the same frequency band or not. Based on the analysis and the simulation results provided in this paper, we present principal guidelines for cell deployment and system operation to improve the areal capacity of dense HetNet systems.

In this paper, we propose a robust transceiver design for MIMO interfering broadcast channels taking imperfect channel knowledge at each base station into consideration. By considering the worst-case transceiver design which maximizes the minimum signal-to-interference-plus-noise ratio (SINR), the proposed design can provide better sum rate and worst user rate performance compared to the conventional schemes. From simulation results, we show the resilience of the proposed design against imperfect channel knowledge.

In this paper we present an algebraic analytical approach to estimate area spectrum efficiency (ASE) of interference-limited cellular mobile systems with small reuse distance ratio. In an ultra-dense network (UDN), the co-channel inter-site distance is very small (e.g. 2 times of cell radius). However, to the best of our knowledge, an accurate performance framework for cellular mobile network with small number of co-channel inter-site distance ratio is rarely seen in the literature. To improve the accuracy of expression, we take interference signals from the all interfering BSs on two-dimension and area spectral efficiency (ASE) into account, and derive the close-form expression for the downlink signal-to-interference plus noise ratio (SINR) by Newton's generalized binomial equation and triangle identities of a base station (BS) geometry model. Based on the SINR expression, we further derive tight approximation formulas of downlink spectral efficiency (SE)s and ASE. Moreover, the SINR close-form expression and the tight approximation for SE and ASE with small reuse distance ratio are also presented and validated.

Based on a stationary Poisson point process, a wireless network model with\nrandom propagation effects (shadowing and/or fading) is considered in order to\nexamine the process formed by the signal-to-interference-plus-noise ratio\n(SINR) values experienced by a typical user with respect to all base stations\nin the down-link channel. This SINR process is completely characterized by\nderiving its factorial moment measures, which involve numerically tractable,\nexplicit integral expressions. This novel framework naturally leads to\nexpressions for the k-coverage probability, including the case of random SINR\nthreshold values considered in multi-tier network models. While the k-coverage\nprobabilities correspond to the marginal distributions of the order statistics\nof the SINR process, a more general relation is presented connecting the\nfactorial moment measures of the SINR process to the joint densities of these\norder statistics. This gives a way for calculating exact values of the coverage\nprobabilities arising in a general scenario of signal combination and\ninterference cancellation between base stations. The presented framework\nconsisting of mathematical representations of SINR characteristics with respect\nto the factorial moment measures holds for the whole domain of SINR and is\namenable to considerable model extension.\n

In dense deployment area of femtocell networks, interference becomes major problem normally decreasing signal to interference plus noise ratio (SINR) of connection between Femto User Equipment (FUE) and Femto Base Station (FBS). Thus, FUE must select appropriate FBS to get high SINR value in order for achieving high data rates. In this paper, we investigate cell selection process in Femtocell networks and problem related to interference. We propose cell selection method using bacterial foraging optimization algorithm (BFOA) to find the most appropriate FBS and resource blocks (RBs) for FUE. Simulation results show that proposed method has capability to converge in finding the best solution, i.e. FBS and RBs that will provide high link capacity for FUE.

As a supplement of mobile communication in Fifth generation mobile networks (5G), Unmanned Aerial Vehicles (UAVs) are playing an important role in user coverage. When base stations (BSs) are not able to serve all users in hot spots, deployable UAVs are required to mitigate the burden of BSs and maximize the access number of users. Besides, thanks to the controllable mobility, UAVs can provide better coverage for the mobile users with high velocity or fixed direction. Therefore, it is important to solve the problem of the trajectory planing of multiple UAVs. In this paper, we propose a UAV trajectory optimization strategy to maximize the access number of moving users. Firstly, according to the mobility of UAVs and the Signal to Interference plus Noise Ratio (SINR) requirements of users, we formulate the UAV's trajectory optimization problem, which is an atypical mixed integer linear programming problem (MILP). Then, a convex optimization problem is obtained by correlation relaxation of the previous atypical MILP. Finally, we propose a practicable trajectory optimization algorithm with limited times of iteration. The simulation results show that the proposed algorithm can maximize the access number of users while maintaining enough SINR.