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Table Of Integrals Series And Products

Matrix Differential Calculus with Applications in Statistics and Econometrics

The potential of using of millimeter wave (mmWave) frequency for future wireless cellular communication systems has motivated the study of large-scale antenna arrays for achieving highly directional beamforming. However, the conventional fully digital beamforming methods which require one radio frequency (RF) chain per antenna element is not viable for large-scale antenna arrays due to the high cost and high power consumption of RF chain components in high frequencies. To address the challenge of this hardware limitation, this paper considers a hybrid beamforming architecture in which the overall beamformer consists of a low-dimensional digital beamformer followed by an RF beamformer implemented using analog phase shifters. Our aim is to show that such an architecture can approach the performance of a fully digital scheme with much fewer number of RF chains. Specifically, this paper establishes that if the number of RF chains is twice the total number of data streams, the hybrid beamforming structure can realize any fully digital beamformer exactly, regardless of the number of antenna elements. For cases with fewer number of RF chains, this paper further considers the hybrid beamforming design problem for both the transmission scenario of a point-to-point multiple-input multiple-output (MIMO) system and a downlink multi-user multiple-input single-output (MU-MISO) system. For each scenario, we propose a heuristic hybrid beamforming design that achieves a performance close to the performance of the fully digital beamforming baseline. Finally, the proposed algorithms are modified for the more practical setting in which only finite resolution phase shifters are available. Numerical simulations show that the proposed schemes are effective even when phase shifters with very low resolution are used.

Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays

This paper gives an overview of the majorization-minimization (MM) algorithmic framework, which can provide guidance in deriving problem-driven algorithms with low computational cost. A general introduction of MM is presented, including a description of the basic principle and its convergence results. The extensions, acceleration schemes, and connection to other algorithmic frameworks are also covered. To bridge the gap between theory and practice, upperbounds for a large number of basic functions, derived based on the Taylor expansion, convexity, and special inequalities, are provided as ingredients for constructing surrogate functions. With the pre-requisites established, the way of applying MM to solving specific problems is elaborated by a wide range of applications in signal processing, communications, and machine learning.

Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning

The block coordinate descent (BCD) method is widely used for minimizing a continuous function $f$ of several block variables. At each iteration of this method, a single block of variables is optimized, while the remaining variables are held fixed. To ensure the convergence of the BCD method, the subproblem of each block variable needs to be solved to its unique global optimal. Unfortunately, this requirement is often too restrictive for many practical scenarios. In this paper, we study an alternative inexact BCD approach which updates the variable blocks by successively minimizing a sequence of approximations of $f$ which are either locally tight upper bounds of $f$ or strictly convex local approximations of $f$. The main contributions of this work include the characterizations of the convergence conditions for a fairly wide class of such methods, especially for the cases where the objective functions are either nondifferentiable or nonconvex. Our results unify and extend the existing convergence results ...

https://works.bepress.com/mingyi_hong/1/download/

A unified convergence analysis of block successive minimization methods for nonsmooth optimization

Consider the MIMO interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to the users in other cells. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper we propose a linear transceiver design algorithm for weighted sum-rate maximization that is based on iterative minimization of weighted mean squared error (MSE). The proposed algorithm only needs local channel knowledge and converges to a stationary point of the weighted sum-rate maximization problem. Furthermore, we extend the algorithm to a general class of utility functions and establish its convergence. The resulting algorithm can be implemented in a distributed asynchronous manner. The effectiveness of the proposed algorithm is validated by numerical experiments.

An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel

Consider the multiple-input multiple-output (MIMO) interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to each other. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper, we propose a linear transceiver design algorithm for weighted sum-rate maximization that is based on iterative minimization of weighted mean-square error (MSE). The proposed algorithm only needs local channel knowledge and converges to a stationary point of the weighted sum-rate maximization problem. Furthermore, the algorithm and its convergence can be extended to a general class of sum-utility maximization problem. The effectiveness of the proposed algorithm is validated by numerical experiments.

An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel

Node localization is essential to most applications of wireless sensor networks (WSNs). In this paper, we consider both range-based node localization and range-free node localization with uncertainties in range measurements, radio range, and anchor positions. First, a greedy optimization algorithm, named sequential greedy optimization (SGO) algorithm, is presented, which is more suitable for distributed optimization in networks than the classical nonlinear Gauss-Seidel algorithm. Then a unified optimization framework is proposed for both range-based localization and range-free localization, and two convex localization formulations are obtained based on semidefinite programming (SDP) relaxation techniques. By applying the SGO algorithm to the edge-based SDP relaxation formulation, we propose a second-order cone programming (SOCP)-based distributed node localization algorithm. Two distributed refinement algorithms are also proposed by using the SGO algorithm to nonconvex localization formulations. The proposed localization algorithms all can be implemented partially asynchronously in networks. Finally, extensive simulations are conducted to demonstrate the efficiency and accuracy of the proposed distributed localization algorithms.

Distributed Wireless Sensor Network Localization Via Sequential Greedy Optimization Algorithm

In this paper, a novel interference coordination scheme based softer frequency reuse (SerFR) to mitigate inter-cell interference in Orthogonal Frequency Division Multiple Access (OFDMA) cellular systems is presented. In OFDMA systems, inter-cell interference is the most important interference source. Inter-cell interference coordination is considered to improve coverage and increase date rate at cell edge. Soft Frequency Reuse (SFR) interference coordination scheme is proposed in 3GPP Long Term Evolution (LTE). But the scheme cause frequency selective scheduling gain loss, and the peak rate for cell edge users is low because the cell edge users only can use a fraction of the entire frequency band. However, in SerFR scheme, cell edge users can use the entire frequency band, so there are more frequency selective scheduling garn and more peak date rate than SFR scheme.

Inter-cell interference coordination based on softer frequency reuse in OFDMA cellular systems

In this paper, we propose a novel vertical handoff decision algorithm for overlay wireless networks consisting of cellular and wireless local area networks (WLANs). The target network is selected using a fuzzy logic-based normalized quantitative decision algorithm which, in addition to usual parameters such as the current received signal strength (RSS) and the available bandwidth, also takes a prediction of the RSS into account, resulting in a more accurate handoff. The RSS prediction is obtained using a differential prediction algorithm that has good accuracy. Furthermore, to reduce system load, a pre-decision method is employed before actual handoff decision to filter out users with high mobility or low RSS from using the WLAN. Simulation results show that the proposed algorithm can reduce the call-dropping probability as well as unnecessary handoffs in heterogeneous network environments.

/pdf/a-novel-fuzzy-logic-vertical-handoff-algorithm-with-aid-of-2hcd2lcbc2.pdf

Chen He

Papers

An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel

An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel

Distributed Wireless Sensor Network Localization Via Sequential Greedy Optimization Algorithm

Inter-cell interference coordination based on softer frequency reuse in OFDMA cellular systems

A Novel Fuzzy Logic Vertical Handoff Algorithm with Aid of Differential Prediction and Pre-Decision Method