Performance analysis of wireless power transfer in interference alignment networks

The explosive demand for high data rates and the need ubiquitous wireless connectivity has led to the phenomenon of network densification, the deployment of large number of base station within a geographic area. Since the radio spectrum is limited, wireless transmissions need to share a common resource which results in interference. Interference in turn creates a bottleneck on the communication rate. Recently three breakthroughs have been made to increase the capacity of wireless networks while mitigating interference : multiple input multiple output (MIMO) beamforming, interference alignment (IA) and pattern reconfigurable antennas (PRA). While a great deal of progress has been made on understanding each individually, relatively little is known about how to use these techniques in combination to increase system performance. In this dissertation, we analyze and experimentally evaluate how these new technologies can be leveraged to enhance the rate performance of communication networks. Specifically, we address three problems: i) enhancing multi-user MIMO performance with PRA, ii) improving IA performance with PRA-based antenna selection, and iii) enabling practical blind IA with PRA. To understand the impact PRAs on MU-MIMO and IA, it is necessary to evaluate their performance through measurements. Simulation-based studies often reiterate over set of simplistic channel models and unrealistic assumptions. This dissertation focuses on a measurement-based evaluation approach. For each of these problems, a hardware testbed implementation and measurement methodology is developed for evaluating the performance of the aforementioned technologies through efficient and repeatable experiments. The first two major contributions of this dissertation focus on multi-user MIMO (MU-MIMO) and IA. While the theoretical rate gains of MU-MIMO and IA are substantial, the performance gains in practical systems and measured channels have been limited. One of the key limiting factors are spatially correlated user channels. In this work, we propose MU-MIMO and IA transmission schemes that leverage the pattern diversity provided by PRAs to mitigate the effects of spatial correlation in wireless channels. PRAs are capable of dynamically adjusting their radiation pattern, providing an additional degree of freedom that can be exploited to improve MU-MIMO and IA performance by treating the array configuration and radiation characteristics as additional components in the joint optimization of adaptive system parameters. We quantify the benefits of pattern diversity on the rate performance of MU-MIMO and IA systems through extensive measurements. Furthermore, we develop efficient antenna mode selection algorithms to realize those benefits without increasing the system complexity or overhead, making these techniques suitable for practical implementation. The third contribution of this dissertation focuses on a transmission technique known as blind IA. In contrast to MU-MIMO and conventional IA, blind IA is signaling scheme that suppresses interference in multi-user systems, without the knowledge of channel state information at the transmitter (CSIT). The key to performing IA without CSIT is the use of PRAs that are capable of dynamically switching among a fixed number of radiation patterns to introduce artificial fluctuations in the channel. In this dissertation, we develop a novel implementation of a blind IA system on a software defined radio platform where each of the receivers is equipped with a PRA. Our work provides the first experimental evaluation of PRA-based blind IA schemes and demonstrates significant gains in rate and reliability over traditional orthogonal transmission schemes such as TDMA.

Interference alignment (IA) is a novel technique to manage interference for multiuser wireless communications. The basic idea of IA in managing interference is based on the subspace separation. Since this property can be easily damaged, one might image that IA is sensitive to errors appeared in channel state information (CSI), which is needed at the transmitter side of each user. In this work, we will show some comparison results between IA and direct transmission (DT). In the DT scheme, the maximum likelihood (ML) receiver is used at each user. The simulation results show that IA systems are indeed very sensitive to CSI errors compared with the DT-ML system, while the complexity of the IA systems in implementation is comparable with the DT-ML systems if the constellation of transmitted symbols is of moderate cardinality.

This paper studies the impact of channel error on the achievable rate of symmetrical K-user multiple-input multiple-output linear interference alignment (IA) networks. The upper and lower bounds of the achievable sum rate are derived analytically with the assumption of orthonormal transmit precoders and receive filters designed from imperfect channel state information (CSI) over both the uncorrelated and correlated channels. For uncorrelated channels, quite tight lower and upper bounds are obtained. The impact of channel error on the degrees of freedom (DoF) and the DoF persistence conditions are also investigated. Results show that the DoF of IA networks persists only if the channel error decreases in an order higher than the signal-to-noise ratio. For correlated channel, the lower and upper bounds for one realization of IA are derived. The derived upper bound can be used to characterize the achievable rate approximately. Simulation results indicate that the achievable rate of IA network is influenced significantly by CSI uncertainty. The obtained analytical bounds provide an intuitive way to show the impact of channel error on the achievable rate and thus can help practical systems deign.

In this work we investigate the degrees of freedom (DoF) performance of the multiple-input multiple-output (MIMO) interference channel (IC) with limited channel state information (CSI) feedback. We generalise two space-time interference alignment (STIA) algorithms to the K user MIMO IC with a transmit power constraint and show how these schemes exploit both current and outdated CSI to achieve an improved DoF performance. We show that by combining the STIA schemes with traditional methods major DoF gain can be achieved for a variety of limited CSI feedback scenarios in comparison to what would be obtained via the use of the traditional techniques on their own. In particular we prove that optimal DoF can still be achieved in situations where current CSI is unavailable for a derived fraction of the time. Finally we validate the promised DoF results via system simulation and show how these DoF gains reflect in terms of sum rate achievable.

This thesis describes the analytical design and the performance analysis of optimum receivers for Multiple Input - Multiple Output (MIMO) fading channels. In particular, a novel Optimum Receiver for separately-correlated MIMO channels is proposed. This novel pilot-aided receiver is able to process jointly the pilot symbols, transmitted within each time frame as a preamble, and the information symbols and to decode the transmitted data in a single step, avoiding the explicit estimation of the channel matrix. The optimum receiver is designed for the following two scenarios, corresponding to different transmission schemes and channel models: 1) Narrowband Rician fading MIMO channel with spatial separate correlation; 2) MIMO-OFDM Rician fading channel with space and frequency separate correlation. For each system the performance of the optimum receiver is studied in detail under different channel conditions. The optimum receiver is compared with: - the ideal Genie Receiver, knowing perfectly the Channel State Information (CSI) at no cost; - the standard Mismatched Receiver, estimating the CSI in a first step, then using this imperfect estimate in the ideal channel metric. Since the optimum receiver requires the knowledge of the channel parameters for the decoding process, an estimation algorithm is proposed and tested. Moreover, a complexity analysis is carried out and methods for complexity reduction are proposed. Furthermore, the narrowband receiver is tested in realistic conditions using measured channel samples. Finally, a blind version of the receiver is proposed

Interference alignment (IA) is a multi-user wireless communication strategy that enables several transmitters simultaneous communicate with their paired receivers without interfering each other. IA is a multiplexing gain optimal strategy at the expense of requiring the global channel state information (CSI) at the transmitters. Most previous works assume the availability of perfect CSI which is not possible in practice. The performance of IA algorithms based on perfect CSI drop dramatically in circumstance where CSI errors exist. In this paper we propose a robust minimum mean square error (MMSE) IA algorithm with imperfect CSI. Based on a Gaussian statistic channel error model, we give the mean square error objective function with per transmitter power constraint. The objective function is not joint convex in precoders and decoders, but convex in precoders with fixed decoders and vice versa. We convert the problem to two convex sub problems and iteratively find the solutions. Numerical results are presented to validate the effectiveness of proposed algorithm when CSI errors exist.

In the presence of perfect channel state information (CSI), the achievable degrees of freedom (DoF) in wireless interference networks can be linearly scaled up with the number of users. Achievability is based on the idea of interference alignment (IA). However, in the presence of imperfect CSI, the sum rate becomes degraded, and full DoF may no longer be achievable. In this paper, the authors propose novel least squares (LS)- and minimum mean square error (MMSE)-based IA schemes that adaptively design beamformers by relying on the availability of imperfect CSI and knowledge of the channel estimation error variance in advance. Interestingly and unlike the other robust algorithms, the proposed adaptive schemes do not impose extra computational complexity compared to their nonadaptive counterparts. It is shown that the LS-based IA is able to outperform interference leakage minimization algorithms under both perfect and imperfect CSI. Furthermore, the authors compare the performance of the proposed MMSE-based IA with maximum signal-to-interference-plus-noise ratio (Max-SINR) algorithm. The authors show that while under perfect CSI, the MMSE-based IA achieves the same performance as that of Max-SINR, the former outperforms the latter under CSI mismatch. Meanwhile, it is shown that the proposed MMSE-based IA needs less CSI to be available and has less computational complexity compared to Max-SINR.

This chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance.

Accurate channel state information (CSI) at base station (BS) is the key to achieve full multiplexing and diversity gain in traditional multiple-input multiple-output (MIMO) systems. In frequency-division-duplex (FDD) systems, it is obtained through downlink training and uplink feedback. However, the training and feedback overhead in massive MIMO systems is unacceptable for the large number of antennas equipped at BS. To reduce the CSI feedback overhead and improve CSI quantization accuracy, we propose a limited feedback scheme for spatio-temporal correlated massive MIMO channel. The proposed scheme jointly performs Karhunen-Loève transform (KLT) and polar-cap quantization on CSI. For the compactly deployed antennas at BS, there exists spatial correlation in massive MIMO channel. When the user equipment (UE) moves slowly, adjacent channels feature temporal correlation. The high dimensional and spatial correlated CSI is compressed through KLT. Then the compressed CSI is normalized to be isotropic to perform polar-cap quantization. Simulation results show that the proposed scheme is capable of improving the sum-rate performance with limited feedback bits for massive MIMO systems.

A 2-D shooting-and-bouncing (SBR) ray-tracing method considering ray reflect, transmission and diffraction is used to analyze antenna spacing effect on capacity complementary cumulative distribution functions(CCDFS) of indoor non-line-of-sight (NLOS) MIMO channel and indoor multiple-input multiple-output (MIMO) channel with line-of-sight (LOS) path. The results show that the capacities of MIMO channel with LOS path are less than NLOS MIMO channel capacities in general. But whether in indoor MIMO channel with LOS path or in NLOS indoor MIMO channel, capacities CCDFS of indoor MIMO systems with antenna spacing of 5/spl times/ can reach the theoretical independent and identically distribution (i.i.d) Rayleigh capacities nearly fully. With the decrease of antenna spacing, the capacities dropped also. And the less antenna spacing, the more difference between indoor NLOS MIMO channel capacities and capacities of MIMO channel with LOS path.

In wireless interference networks, orthogonal medium access techniques like time division multiple access (TDMA) and frequency division multiple access (FDMA) achieve merely one degree of freedom (DoF). However, by using interference alignment (IA) the achievable DoF can be linearly scaled up with the number of users. Nevertheless, this outperformance of IA heavily depends on the availability of perfect channel state information (CSI), which is not a realistic assumption in practice. Since under imperfect CSI, the performance of IA may become severely degraded, design of enhanced IA algorithms by relying on the availability of merely imperfect CSI is of particular interest. In this paper, we propose a least squares (LS)-based IA algorithm which adaptively designs the beamformers based on the knowledge of the channel estimation error variance, which is possible to be known in advance. Then, we compare the performance of the proposed scheme with interference leakage minimization algorithms. It is shown that under both perfect and imperfect CSI, the proposed LS-based IA outperforms interference leakage minimization algorithms.

A novel interference alignment (IA) scheme based on the errors-in-variables (EIV) mathematic model has been proposed to overcome the channel state information (CSI) estimation error for the MIMO interference channels. By solving an equivalently unconstrained optimization problem, the proposed IA scheme employing a weighted total least squares (WTLS) algorithm can obtain the solution to a constrained optimization problem for transmit precoding (TPC) matrices and minimizes the distortion caused by imperfect CSI according to the EIV model. It is shown that the design of TPC matrices can be realized through an efficient iterative algorithm. The convergence of the proposed scheme is presented as well. Simulation results show that the proposed IA scheme is robust over MIMO interference channels with imperfect CSI, which yields significantly better sum rate performance than the existing IA schemes such as distributed iterative IA, maximum signal-to-interference-plus-noise ratio (Max SINR), and minimum mean square error (MMSE) schemes.

This paper researches the capacity of multiple-input multiple-output (MIMO) channels jointly-correlated spatially on both sides in the low signal-to-noise (SNR) regime. We shall derive two key low SNR metrics: the minimum required $E_b/N_0$ and the wideband slope, assuming two types of channel state information (CSI) at the transmitter. We obtain closed-from solutions of these low SNR parameters for Gaussian MIMO channels, which embrace many existing results in the literature as special cases. Monte-Carlo simulations are performed to validate our analytical results.

Interference alignment (IA) is an effective method that can eliminate interferences in wireless networks, and has been applied to spectrum sharing in cognitive radio (CR) networks recently. However, the availability of perfect network channel state information (CSI) is necessary for most existing IA schemes, which is not practical in general due to the realistic communication scenarios and deployment challenges. In this paper, we apply IA to cognitive relay networks under CSI mismatch where the variance of the CSI measurement error depends on signal-to-noise ratio (SNR). An adaptive Max-SINR IA algorithm has been introduced to improve the performance of the secondary network by using the knowledge of CSI error variance. Finally, we analyze the performance of the secondary network in terms of the end-to-end equivalent transmission rate and outage probability. Simulation results indicate that our proposed adaptive Max-SINR IA scheme can greatly improve the performance of the secondary network.

Interference alignment (IA) is a linear precoding strategy that can achieve optimal capacity scaling at high SNR in interference networks. Most of the existing IA designs require full channel state information (CSI) at the transmitters, which induces a huge CSI signaling cost. Hence it is desirable to improve the feedback efficiency for IA and in this paper, we propose a novel IA scheme with a significantly reduced CSI feedback. To quantify the CSI feedback cost, we introduce a novel metric, namely the feedback dimension. This metric serves as a first-order measurement of CSI feedback overhead. Due to the partial CSI feedback constraint, conventional IA schemes can not be applied and hence, we develop a novel IA precoder/decorrelator design and establish new IA feasibility conditions. Via dynamic feedback profile design, the proposed IA scheme can also achieve a flexible tradeoff between the degree of freedom (DoF) requirements for data streams, the antenna resources and the CSI feedback cost. We show by analysis and simulations that the proposed scheme achieves substantial reductions of CSI feedback overhead under the same DoF requirement in MIMO interference networks.