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This book presents an alternative and simplified approaches for the robust adaptive detection and beamforming in wireless communications. It adopts several systems models including DS/CDMA, OFDM/MIMO with antenna array, and general antenna arrays beamforming model. It presents and analyzes recently developed detection and beamforming algorithms with an emphasis on robustness. In addition, simplified and efficient robust adaptive detection and beamforming techniques are presented and compared with exiting techniques. Practical examples based on the above systems models are provided to exemplify the developed detectors and beamforming algorithms. Moreover, the developed techniques are implemented using MATLAB—and the relevant MATLAB scripts are provided to help the readers to develop and analyze the presented algorithms. em style="mso-bidi-font-style: normal;"Simplified Robust Adaptive Detection and Beamforming for Wireless Communications starts by introducing readers to adaptive signal processing and robust adaptive detection. It then goes on to cover Wireless Systems Models. The robust adaptive detectors and beamformers are implemented using the well-known algorithms including LMS, RLS, IQRD-RLS, RSD, BSCMA, CG, and SD. The robust detection and beamforming are derived based on the existing detectors/beamformers including MOE, PLIC, LCCMA, LCMV, MVDR, BSCMA, and MBER. The adopted cost functions include MSE, BER, CM, MV, and SINR/SNR.
Adaptive techniques play a key role in modern wireless communication systems. The concept of adaptation is emphasized in the Adaptation in Wireless Communications Series through a unified framework across all layers of the wireless protocol stack ranging from the physical layer to the application layer, and from cellular systems to next-generation wireless networks. This specific volume, Adaptive Signal Processing in Wireless Communications is devoted to adaptation in the physical layer. It gives an in-depth survey of adaptive signal processing techniques used in current and future generations of wireless communication systems. Featuring the work of leading international experts, it covers adaptive channel modeling, identification and equalization, adaptive modulation and coding, adaptive multiple-input-multiple-output (MIMO) systems, and cooperative diversity. It also addresses other important aspects of adaptation in wireless communications such as hardware implementation, reconfigurable processing, and cognitive radio. A second volume in the series, Adaptation and Cross-layer Design in Wireless Networks(cat no.46039) is devoted to adaptation in the data link, network, and application layers.
This book provides the reader with a complete coverage of radio resource management for 3G wireless communications Systems Engineering in Wireless Communications focuses on the area of radio resource management in third generation wireless communication systems from a systems engineering perspective. The authors provide an introduction into cellular radio systems as well as a review of radio resource management issues. Additionally, a detailed discussion of power control, handover, admission control, smart antennas, joint optimization of different radio resources , and cognitive radio networksis offered. This book differs from books currently available, with its emphasis on the dynamical issues arising from mobile nodes in the network. Well-known control techniques, such as least squares estimation, PID control, Kalman filers, adaptive control, and fuzzy logic are used throughout the book. Key Features: Covers radio resource management of third generation wireless communication systems at a systems level First book to address wireless communications issues using systems engineering methods Offers the latest research activity in the field of wireless communications, extending to the control engineering community Includes an accompanying website containing MATLABTM/SIMULINKTM exercises Provides illustrations of wireless networks This book will be a valuable reference for graduate and postgraduate students studying wireless communications and control engineering courses, and R&D engineers.
The latest research and developments in robust adaptivebeamforming Recent work has made great strides toward devising robust adaptivebeamformers that vastly improve signal strength against backgroundnoise and directional interference. This dynamic technology hasdiverse applications, including radar, sonar, acoustics, astronomy,seismology, communications, and medical imaging. There are alsoexciting emerging applications such as smart antennas for wirelesscommunications, handheld ultrasound imaging systems, anddirectional hearing aids. Robust Adaptive Beamforming compiles the theories and work ofleading researchers investigating various approaches in onecomprehensive volume. Unlike previous efforts, these pioneeringstudies are based on theories that use an uncertainty set of thearray steering vector. The researchers define their theories,explain their methodologies, and present their conclusions. Methodspresented include: * Coupling the standard Capon beamformers with a spherical orellipsoidal uncertainty set of the array steering vector * Diagonal loading for finite sample size beamforming * Mean-squared error beamforming for signal estimation * Constant modulus beamforming * Robust wideband beamforming using a steered adaptive beamformerto adapt the weight vector within a generalized sidelobe cancellerformulation Robust Adaptive Beamforming provides a truly up-to-date resourceand reference for engineers, researchers, and graduate students inthis promising, rapidly expanding field.
The cellular network operators have witnessed significant growth in data traffic in the past few decades. This growth occurs due to the increases in the number of connected mobile devices, and further, the emerging mobile applications developed for rendering video-based on-demand services. As the frequency bandwidth for cellular communication is limited, significant effort was dedicated to improve the utilization of the available spectrum and increase the system performance via new technologies. For example, 3G and 4G networks were designed to facilitate high data traffic in cellular networks in past decades. Nevertheless, there is a necessity for new cellular network technologies to accommodate the ever-growing data traffic demand. 5G is behind the corner to deal with the tremendous data traffic requirements that will appear in cellular networks in the next decade. Massive MIMO (multiple-input-multi-output) is one of the backbone technologies in 5G networks. Massive MIMO originated from the concept of multi-user MIMO. It consists of base stations (BSs) implemented with a large number of antennas to increase the signal strengths via adaptive beamforming and concurrently serving many users on the same time-frequency blocks. As an outcome of using Massive MIMO technology, there is a notable enhancement of both sum spectral efficiency (SE) and energy efficiency (EE) in comparison with conventional MIMO based cellular networks. Resource allocation is an imperative factor to exploit the specified gains of Massive MIMO. It corresponds to properly allocating resources in the time, frequency, space, and power domains for cellular communication. Power control is one of the resource allocation methods to deliver high spectral and energy efficiency of Massive MIMO networks. Power control refers to a scheme that allocates transmit powers to the data transmitters such that the system maximizes some desirable performance metric. In the first part of this thesis, we investigate reusing the resources of a Massive MIMO system, for direct communication of some specific user pairs known as device-to-device (D2D) underlay communication. D2D underlay can conceivably increase the SE of traditional Massive MIMO systems by enabling more simultaneous transmissions on the same frequencies. Nevertheless, it adds additional mutual interference to the network. Consequently, power control is even more essential in this scenario in comparison with conventional Massive MIMO systems to limit the interference that is caused between the cellular network and the D2D communication, thereby enabling their coexistence. In this part, we propose a novel pilot transmission scheme for D2D users to limit the interference to the channel estimation phase of cellular users in comparison with the case of sharing pilot sequences for cellular and D2D users. We also introduce a novel pilot and data power control scheme for D2D underlaid Massive MIMO systems. This method aims at assuring that D2D communication enhances the SE of the network in comparison with conventional Massive MIMO systems. In the second part of this thesis, we propose a novel power control approach for multi-cell Massive MIMO systems. The new power control approach solves the scalability issue of two well-known power control schemes frequently used in the Massive MIMO literature, which are based on the network-wide max-min and proportional fairness performance metrics. We first explain the scalability issue of these existing approaches. Additionally, we provide mathematical proof for the scalability of our proposed method. Our scheme aims at maximizing the geometric mean of the per-cell max-min SE. To solve this optimization problem, we prove that it can be rewritten in a convex form and then be solved using standard optimization solvers.
This research is the array processing help wireless communication techniques to increase the signal accuracy. This technique has an important part of prevalent applications. The wireless communication system, radar, and sonar. Beamforming is one of methods in array processing that filters signals based on their capture time at each element in an array of antennas spatially. Numerous studies in adaptive array processing have been proposed in the last several decades, which are divided in two parts. The first one related to non-adaptive beamforming techniques and the next one related to digitally adaptive Beamforming methods. The trade-off between computational complexity and performance make them different. In this thesis, we concentrate on the expansion of array processing algorithms in both non-adaptive and adaptive ones with application of beamforming in 4G mobile antenna and radar systems. The conventional and generalized side-lobe canceller (GSC) structures beamforming algorithms were employed with a phase array antenna that changed the phase of arrivals in array antenna with common phased array structure antennas. An eight-element uniform linear array (ULA), consisting of di-pole antennas, represented as the antenna array. An anechoic chamber measures the operation of beamforming algorithms performance. An extended modified Kaiser weighting function is proposed to make a semi-adaptive structure in phased array beamforming. This technique is extended to low complexity functions like hyperbolic cosine and exponential functions. Furthermore, these algorithms are used in GSC beamforming. The side-lobe levels were so lower than other algorithms in conventional beamforming around -10 dB. On the other hand, a uniform linear arrays for smart antenna purposes designed to utilize in implementing and testing the proposed algorithms. In this thesis, performance of smart antenna with rectangular aperture coupled microstrip linear array which experimental investigations carried out for obtaining X-band operation of rectangular microstrip antenna by using aperture coupled feeding technique. Frequency range set at approximately 8.6 to 10.9 GHz, by incorporating frequency range of the antenna resonates for single wideband with an impedance bandwidth of 23%. The enhancement of impedance bandwidth and gain does not affect the nature of broadside radiation characteristics. This thesis describes the design, operation, and realization of the beamforming such as Sidelobe level (SLL) control and null forming array antenna are examined with the prototype. An antenna radiation pattern beam maximum can be simultaneously placed towards the intended user or Signal of interest (SOl), and, ideally nulls can be positioned towards directions of interfering signals or signals not of interest (SNOIs). Finally, we focused on the adaptive digitally algorithms in compact antenna that faces with mutual coupling. The variable step-size normalized lease mean square (VS-NLMS) algorithm is implemented in beamforming. This algorithm utilizes continuous adaptation. The weights are attuned that the final weight vector to the most satisfied result. The gradient vector can be achieved by iterative beamforming algorithm from the available data. This algorithm is compared with LMS, NLMS, VSS-NLMS algorithms, it is determined that the VSS-NLMS algorithm is better performance to other algorithms. Finally, we introduced novel adaptive IP-NNLMS beamformer. This beamformer reaches to faster convergence and lower error floor than the previous adaptive beamformers even at low SNRs in presence of mutual coupling. The experimental results verified the simulation results that the proposed technique has better performance than other algorithms in various situations.
Of Non-Wraparound Network Performance -- Wrap-around Network Performance Results -- Performance Results over a LOS Channel -- Performance Results over a Multipath Channel -- Performance over a Multipath Channel using Power Control -- Performance of an AQAM based Network using Power Control -- UTRA, Adaptive Arrays and Adaptive Modulation -- Direct Sequence Code Division Multiple Access -- UMTS Terrestrial Radio Access -- Spreading and Modulation -- Common Pilot Channel -- Power Control -- Uplink Power Control -- Downlink Power Control -- Soft Handover -- Signal-to-Interference plus Noise Ratio Calculations -- Downlink -- Uplink -- Multi-User Detection -- Simulation Results -- Simulation Parameters -- The Effect of Pilot Power on Soft Handover Results -- Fixed Received Pilot Power Thresholds without Shadowing -- Fixed Received Pilot Power Thresholds with 0.5 Hz Shadowing -- Fixed Received Pilot Power Thresholds with 1.0 Hz Shadowing -- Relative Received Pilot Power Thresholds without Shadowing -- Relative Received Pilot Power Thresholds with 0.5 Hz Shadowing -- Relative Received Pilot Power Thresholds with 1.0 Hz Shadowing -- E[subscript c]/I[subscript o] Power Based Soft Handover Results -- Fixed E[subscript c]/I[subscript o] Thresholds without Shadowing -- Fixed E[subscript c]/I[subscript o] Thresholds with 0.5 Hz Shadowing -- Fixed E[subscript c]/I[subscript o] Thresholds with 1.0 Hz Shadowing -- Relative E[subscript c]/I[subscript o] Thresholds without Shadowing.
Feedback, power control and beamforming are important PHY and MAC layer issues in wireless communication systems. This dissertation is concerned with theoretical studies and algorithmic developments related to these issues in the context of and in support of the design of situational aware wireless networks. The envisioned situational aware wireless networks adapt system parameters and algorithms design to the channel attributes, user attributes, and system attributes, which constitute the wireless environment and network situations. The research topics in this dissertation regarding feedback, power control and beamforming are motivated by issues that arise from considering different types of awareness. We first propose an adaptive feedback design based on the heterogeneous spectral channel statistics among users, which advocates the awareness of channel attributes. We leverage the multi-cluster subband fading model to develop an analytical framework to investigate the impact of partial feedback and potential imperfections including channel estimation error and feedback delay on system performance. Next, we examine partial feedback in a heterogeneous multicell, and propose a heterogeneous feedback design based on heterogeneous user densities and large scale channel effects, which advocates the awareness of user attributes. The cumulative distribution function (CDF)-based scheduling policy is employed to obtain multiuser diversity gain while maintaining scheduling fairness. We derive a closed form expression as well as asymptotic approximation for the sum rate. In addition, the CDF-based scheduling policy is leveraged in a random beamforming framework to address several open problems. We develop the notion of individual sum rate to study the rate scaling for each individual user. We theoretically examine the randomness of multiuser diversity incurred by selective feedback to further establish the individual scaling laws under different feedback schemes. We then investigate joint beamforming and power control in a multiuser interference network, and propose the usage of spatial channel statistics for algorithm design, which advocates the awareness of channel attributes. With the outage event induced by the utilization of spatial channel statistics, we present decentralized and fast convergent algorithms to achieve outage balancing in the interference network. Finally, we study joint beamforming and power control in a coordinated multicell downlink and employ the max-min formulation to enforce egalitarian user fairness. In order to design efficient algorithm that scales well with the system dimension, we leverage the large system structure and advocate the awareness of system attributes. In our proposed algorithm design, the asymptotic power is computed using statistical channel information and the instantaneous beamformer is obtained in a non-iterative manner. We also establish the effective network to characterize and interpret the asymptotic solution.