# TAILIEUCHUNG - Adaptive WCDMA (P3)

## Code acquisition OPTIMUM SOLUTION In this case, the theory starts with a simple problem where, for a received signal r(t) = s(t, θ ) + n(t), we have to estimate a generalized time invariant vector of parameters θ (frequency, phase, delay, data, . . .) of a signal s(t, θ ) in the presence of Gaussian noise ˆ n(t). The best that we can do is to ﬁnd an estimate θ of the parameter θ for which ˆ /r) is maximum; hence the name maximum aposterior the aposterior probability p(θ probability (MAP) estimate. In other words, the chosen estimate based on. | Adaptive WCDMA Theory And Practice. Savo G. Glisic Copyright 2003 John Wiley Sons Ltd. ISBN 0-470-84825-1 3 Code acquisition OPTIMUM SOLUTION In this case the theory starts with a simple problem where for a received signal r t s t 0 n t we have to estimate a generalized time invariant vector of parameters 0 frequency phase delay data . of a signal s t 0 in the presence of Gaussian noise n t . The best that we can do is to find an estimate of the parameter 0 for which the aposterior probability p 0 r is maximum hence the name maximum aposterior probability MAP estimate. In other words the chosen estimate based on the received signal r is correct for the highest probability. Practical implementation requires us to locally generate a number of trial values 0 to evaluate p 0 r for each such value and then to choose 0 for which p r is maximum. In this chapter we focus only on code acquisition and parameter 0 will include only code delay 0 r and become a scalar. Analytically this can be expressed as MAP 0 arg maxp 0 r Very often in practice evaluation of p 0 r in closed form is not possible. By using the Bayesian rule for the joint probability distribution function p r 0 p r p 0 r p 0 p r 0 and assuming a uniform prior distribution of 0 maximizing p 0 r becomes equivalent to maximizing p r 0 a function that can be determined more easily. This algorithm is known as maximum likelihood ML estimation and can be defined analytically as ML 0 arg max p r 0 It is straightforward to show that in the case of Gaussian noise the ML principle necessitates the search for that value of 0 that would maximize the likelihood function defined as - f - f L 0 r t s t 0 dt s2 t 0 dt 44 CODE ACQUISITION where s is the locally generated replica of the signal with a trial value 9. For the given signal power the second term in the previous equation is a constant so that the maximization is equivalent to the maximization of the first term only. This can be expressed as k 9

TÀI LIỆU LIÊN QUAN
9    94    0
31    246    23
1    170    13
89    111    6
80    201    27
95    124    1
51    96    0
1    132    2
85    110    3
91    94    0
TÀI LIỆU XEM NHIỀU
8    389836    14
3    7174    93
14    6526    301
8    5900    1787
2    3466    28
9    3248    8
24    3177    61
3    3153    41
35    3112    149
21    3019    213
TỪ KHÓA LIÊN QUAN
TÀI LIỆU MỚI ĐĂNG
32    15    1    28-09-2021
145    46    0    28-09-2021
5    35    0    28-09-2021
10    16    2    28-09-2021
46    52    1    28-09-2021
2    25    1    28-09-2021
10    37    0    28-09-2021
42    50    0    28-09-2021
36    36    0    28-09-2021
8    8    2    28-09-2021
TÀI LIỆU HOT
8    5900    1787
112    2062    832
561    987    356
122    1949    344
14    6526    301
20    2494    270
35    1818    267
36    2056    229
21    3019    213
171    1340    206
Đã phát hiện trình chặn quảng cáo AdBlock
Trang web này phụ thuộc vào doanh thu từ số lần hiển thị quảng cáo để tồn tại. Vui lòng tắt trình chặn quảng cáo của bạn hoặc tạm dừng tính năng chặn quảng cáo cho trang web này.