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Register a new userAn Iterative Geometric Mean Decomposition Algorithm for MIMO Communications Systems
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Chiao-En Chen
Publication date
2013
fields
Informatics Engineering
and research's language is
English
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This paper presents an iterative geometric mean decomposition (IGMD) algorithm for multiple-input-multiple-output (MIMO) wireless communications. In contrast to the existing GMD algorithms, the proposed IGMD does not require the explicit computation of the geometric mean of positive singular values of the channel matrix and hence is more suitable for hardware implementation. The proposed IGMD has a regular structure and can be easily adapted to solve problems with different dimensions. We show that the proposed IGMD is guaranteed to converge to the perfect GMD under certain sufficient condition. Three different constructions of the proposed algorithm are proposed and compared through computer simulations. Numerical results show that the proposed algorithm quickly attains comparable performance to that of the true GMD within only a few iterations.
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This paper considers a scenario in which an Alice-Bob pair wishes to communicate in secret in the presence of an active Eve, who is capable of jamming as well as eavesdropping in Full-Duplex (FD) mode. As countermeasure, Bob also operates in FD mode, using a subset of its antennas to act as receiver, and the remaining antennas to act as jammer and transmit noise. With a goal to maximize the achievable secrecy degrees of freedom (S.D.o.F.) of the system, we provide the optimal transmit/receive antennas allocation at Bob, based on which we determine in closed form the maximum achievable S.D.o.F.. We further investigate the adverse scenario in which Eve knows Bobs transmission strategy and optimizes its transmit/receive antennas allocation in order to minimize the achievable S.D.o.F.. For that case we find the worst-case achievable S.D.o.F.. We also provide a method for constructing the precoding matrices of Alice and Bob, based on which the maximum S.D.o.F. can be achieved. Numerical results validate the theoretical findings and demonstrate the performance of the proposed method in realistic settings.
Here we define a procedure for evaluating KL-projections (I- and rI-projections) of channels. These can be useful in the decomposition of mutual information between input and outputs, e.g. to quantify synergies and interactions of different orders, as well as information integration and other related measures of complexity. The algorithm is a generalization of the standard iterative scaling algorithm, which we here extend from probability distributions to channels (also known as transition kernels).
We experimentally demonstrate a software-defined 2x2 MIMO VLC system employing link adaptation of spatial multiplexing and diversity. The average error-free spectral efficiency of 12 b/s/Hz is achieved over 2 meters indoor transmission after an obstruction.
We investigate the optimality and power allocation algorithm of beam domain transmission for single-cell massive multiple-input multiple-output (MIMO) systems with a multi-antenna passive eavesdropper. Focusing on the secure massive MIMO downlink transmission with only statistical channel state information of legitimate users and the eavesdropper at base station, we introduce a lower bound on the achievable ergodic secrecy sum-rate, from which we derive the condition for eigenvectors of the optimal input covariance matrices. The result shows that beam domain transmission can achieve optimal performance in terms of secrecy sum-rate lower bound maximization. For the case of single-antenna legitimate users, we prove that it is optimal to allocate no power to the beams where the beam gains of the eavesdropper are stronger than those of legitimate users in order to maximize the secrecy sum-rate lower bound. Then, motivated by the concave-convex procedure and the large dimension random matrix theory, we develop an efficient iterative and convergent algorithm to optimize power allocation in the beam domain. Numerical simulations demonstrate the tightness of the secrecy sum-rate lower bound and the near-optimal performance of the proposed iterative algorithm.
The performance of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems is limited by the sparse nature of propagation channels and the restricted number of radio frequency (RF) chains at transceivers. The introduction of reconfigurable antennas offers an additional degree of freedom on designing mmWave MIMO systems. This paper provides a theoretical framework for studying the mmWave MIMO with reconfigurable antennas. Based on the virtual channel model, we present an architecture of reconfigurable mmWave MIMO with beamspace hybrid analog-digital beamformers and reconfigurable antennas at both the transmitter and the receiver. We show that employing reconfigurable antennas can provide throughput gain for the mmWave MIMO. We derive the expression for the average throughput gain of using reconfigurable antennas in the system, and further derive the expression for the outage throughput gain for the scenarios where the channels are (quasi) static. Moreover, we propose a low-complexity algorithm for reconfiguration state selection and beam selection. Our numerical results verify the derived expressions for the throughput gains and demonstrate the near-optimal throughput performance of the proposed low-complexity algorithm.
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