Space-time processing for wireless mobile communications

Intersymbol interference (ISI) and co-channel interference (CCI) are two major obstacles to high speed data transmission in wireless cellular communications systems. Unlike thermal noise, their effects cannot be removed by increasing the signal power and are time-varying due to the relative motion between the transmitters and receivers. Space-time processing offers a signal processing framework to optimally integrate the spatial and temporal properties of the signal for maximal signal reception and at the same time, mitigate the ISI and CCI impairments. In this thesis, we focus on the development of this emerging technology to combat the undesirable effects of ISI and CCL We first develop a convenient mathematical model to parameterize the space-time multipath channel based on signal path power, directions and times of arrival. Starting from the continuous time-domain, we derive compact expressions of the vector space-time channel model that lead to the notion of block space-time manifold, Under certain identifiability conditions, the noiseless vector-channel outputs will lie on a subspace constructed from a set. of basis belonging to the block space-time manifold. This is an important observation as many high resolution array processing algorithms Can be applied directly to estimate the multi path channel parameters. Next we focus on the development of semi-blind channel identification and equalization algorithms for fast time-varying multi path channels. Specifically. we develop space-time processing algorithms for wireless TDMA networks that use short burst data formats with extremely short training data. sequences. Due to the latter, the estimated channel parameters are extremely unreliable for equalization with conventional adaptive methods. We approach the channel acquisition, tracking and equalization problems jointly, and exploit the richness of the inherent structural relationship between the channel parameters and the data sequence by repeated use of available data through a forward- backward optimization procedure. This enables the fuller exploitation of the available data. Our simulation studies show that significant performance gains are achieved over conventional methods. In the final part of this thesis, we address the problem identifying and equalizing multi path communication channels in the presence of strong CCl. By considering CCI as stochasic processes, we find that temporal diversity can be gained by observing the channel outputs from a tapped delay line. Together with the assertion that the finite alphabet property of the information sequences can offer additional information about the channel parameters and the noise-plus-covariance matrix, we develop a spatial temporal algorithm, iterative reweighting alternating minimization, to estimate the channel parameters and information sequence in a weighted least squares framework. The proposed algorithm is robust as it does not require knowledge of the number of CCI nor their structural information. Simulation studies demonstrate its efficacy over many reported methods

Optimizing time and space MIMO antenna system for frequency selective fading channels

Smart or adaptive antennas promise to provide significant increases in system capacity and performance in wireless communication systems. In this paper, we investigate the use of adaptive antennas at the base and mobile stations, operating jointly, to maximize the average signal-to-interference and noise ratio (SINR) of each packet in the system for frequency selective channels with prior knowledge of the channel at the transmitter. Our approach is based on deriving an analytic formula for the average packet SINR and using the Lagrange multiplier method to determine an optimum. We derive necessary conditions for an optimum solution and propose an analytical expression for the optimum. Our analytical expression is not guaranteed to be the global optimum but it does satisfy the derived necessary conditions and, in addition for frequency flat channels, our results reduce to expressions for optimal weights previously published. To demonstrate the potential of the proposed system, we provide Monte Carlo simulation results of the system bit-error rates and make comparisons with other adaptive antenna systems. These show that significant improvements in performance are possible in a wireless communications context

Achieving Ultra-Low Latency in 5G Millimeter Wave Cellular Networks

The IMT 2020 requirements of 20 Gbps peak data rate and 1 millisecond latency present significant engineering challenges for the design of 5G cellular systems. Use of the millimeter wave (mmWave) bands above 10 GHz --- where vast quantities of spectrum are available --- is a promising 5G candidate that may be able to rise to the occasion. However, while the mmWave bands can support massive peak data rates, delivering these data rates on end-to-end service while maintaining reliability and ultra-low latency performance will require rethinking all layers of the protocol stack. This papers surveys some of the challenges and possible solutions for delivering end-to-end, reliable, ultra-low latency services in mmWave cellular systems in terms of the Medium Access Control (MAC) layer, congestion control and core network architecture

Application of array processing for mobile communications

Digital Signal Processing (DSP) is about a mathematical equation and mathematical operations. It is described by the significations of discrete period, discrete frequency, or supplementary discrete area signals by a order of numbers or signals and the processing of all the signals that related. Digital Signal Processing applications consist of the signal processing for communication. For example is the array processing for the mobile communications. Signal processing is a extensive area of scrutiny that extends from the easiest form of 1-D signal processing to the convoluted form of M-D and array signal processing. This report presents th

A combined channel-modified adaptive array MMSE canceller and viterbi equalizer

In this thesis, a very simple scheme is proposed which couples a maximum-likelihood sequence estimator (MLSE) with a X-element canceller. The method makes use of the MLSE\u27s channel estimator to modify the locally generated training sequence used to calculate the antenna array weights. This method will increase the array\u27s degree of freedom for interference cancellation by allowing the dispersive, desired signal to pass through the array undisturbed. Temporal equalization of the desired signal is then accomplished using maximum-likelihood sequence estimation. The T-spaced channel estimator coefficients and the array weights are obtained simultaneously using the minimum mean square error criteria. The result is a X-element receiver structure capable of canceling X- 1 in-band interferences without compromising temporal equalization

Adaptive Beamforming and Adaptive Modulation-Assisted Network Performance of Multiuser Detection-Aided FDD and TDD CDMA Systems

The network performance of a frequency division duplex and time division duplex (TDD) code division multiple access (CDMA)-based system is investigated using system parameters similar to those of the Universal Mobile Telecommunication System. The new call blocking and call dropping probabilities, the probability of low-quality access, and the required average transmit power are quantified both with and without adaptive antenna arrays (AAAs), as well as when subjected to shadow fading. In some of the scenarios investigated, the system’s user capacity is doubled with the advent of adaptive antennas. The employment of adaptive modulation techniques in conjunction with AAAs resulted in further significant network capacity gains. This is particularly so in the context of TDD CDMA, where the system’s capacity becomes poor without adaptive antennas and adaptive modulation owing to the high base station (BS) to BS interference inflicted as a consequence of potentially using all time slots in both the uplink and downlink of the emerging wireless Internet. Index Terms—Adaptive beamforming, adaptive modulation, code division multiple access (CDMA) systems, Universal Mobile Telecommunication System Terrestrial Radio Access (UTRA), wireless network performance

Multiuser MIMO-OFDM for Next-Generation Wireless Systems

This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems