ofdm信道特性
Channel transmission simulator
Channel transmission simulator
％
％ inputs:
％ sig2 - noise variance
％ Mt - number of Tx antennas
％ Mr - number of Rx antennas
％ x - vector of complex input symbols (for MIMO, this is a matrix, where each column
％ is the value of the antenna outputs at a single time instance)
％ H - frequency selective channel - represented in block-Toeplitz form for MIMO transmission
％ N - number of symbols transmitted in OFDM frame
％
％ outputs:
％ y - vector of channel outputs (matrix for MIMO again, just like x matrix)
％ create noise vector sequence (each row is a different antenna, each column is a
％ different time index) note: noise is spatially and temporally white
16點FFT VHDL源程序，The xFFT16 fast Fourier transform (FFT) Core computes a 16-point complex FFT. The input data
is a vector of 16 complex values represented as 16-bit 2’s complement numbers – 16-bits for
each of the real and imaginary component of a datum.
There are several problems related to the properties of
the triangular mesh representation that describes a
surface of an object. Sometimes, the surface is represented
just as a set of triangles without any other
information and the STL file format, which is used for
data exchanges, is a typicalexampl e of this situation.
function [U,V,num_it]=fcm(U0,X)
％ MATLAB (Version 4.1) Source Code (Routine fcm was written by Richard J.
％ Hathaway on June 21, 1994.) The fuzzification constant
％ m = 2, and the stopping criterion for successive partitions is epsilon =??????.
％*******Modified 9/15/04 to have epsilon = 0.00001 and fix univariate bug********
％ Purpose:The function fcm attempts to find a useful clustering of the
％ objects represented by the object data in X using the initial partition in U0.
Functions are mappings from one Manifold to another. Discrete Functions are functions which can be represented using a finite number of values. Given the finite extent of computer memory, algorithms which compute a function that satisfies some special properties are computing a discrete function which approximates a continuous function. Computing the function involves writing a set of equations that may be solved for the values representing the function.
The task in this assignment is to implement an airline routing system. Your
system should be able to read in a
ight network as a graph from a le, where
airports are represented as vertices and
ights between airports are represented
as edges, take as input two airports and calculate the shortest route (ie path)
between them.
Particle swarm optimization (PSO) was originally designed and introduced by Eberhart and
Kennedy (Ebarhart, Kennedy, 1995 Kennedy, Eberhart, 1995 Ebarhart, Kennedy, 2001). The
PSO is a population based search algorithm based on the simulation of the social behavior of
birds, bees or a school of fishes. This algorithm originally intends to graphically simulate the
graceful and unpredictable choreography of a bird folk. Each individual within the swarm is
represented by a vector in multidimensional search space.
This thesis is about wireless communication in shared radio spectrum. Its origin and
motivation is ideally represented by the two quotations from above. In this thesis, the
support of Quality-of-Service (QoS) in cognitive radio networks is analyzed. New
approaches to distributed coordination of cognitive radios are developed in different
spectrum sharing scenarios. The Wireless Local Area Network (WLAN) 802.11 proto-
col of the Institute of Electrical and Electronics Engineers (IEEE) (IEEE, 2003) with
its enhancement for QoS support (IEEE, 2005d) is taken as basis. The Medium Access
Control (MAC) of 801.11(e) is modified to realize flexible and dynamic spectrum
assignment within a liberalized regulation framework.
The “bottom-line” metrics of cash flow, demand, price, and return on investment
are driven by a second set of financial metrics represented by value to the
customer, cost, and the pace of innovation. Get them right relative to competition
and impressive bottom-line results should follow. Because of their importance, we
call value to the customer, variable cost, and the pace of innovation the
“fundamental metrics.”
