Noise uncertainty in cognitive radio sensing analytical modeling and detection performance

2012 International ITG Workshop on Smart Antennas (WSA)

Noise Uncertainty in Cognitive Radio Sensing:
Analytical Modeling and Detection Performance
Marwan A. Hammouda and Jon W. Wallace
Jacobs University Bremen
Campus Ring 1, 28759 Bremen, Germany
E-mail: m.hammouda@jacobs-university.de, wall@ieee.org

Abstract—Methods for primary user detection in cognitive antenna. Thus, the noise level can be periodically measured by
radio may be severely impaired by noise uncertainty (NU) and switching the receiver input to a matched load where primary
the associated SNR wall phenomenon. The ability to avoid the signal is not present. Even then, the noise measurement may
SNR wall is proposed herein by detailed statistical modeling of
the noise process when NU is present. A Gaussian model for the have error, but it is shown herein that with proper modeling
inverse noise standard deviation is proposed, and good agreement of that error, the SNR wall can be reduced dramatically.
with the more common lognormal distribution is demonstrated The remainder of the paper is organized as follows. Sec-
for low to moderate noise uncertainty. Closed-form pdfs for a tion II provides some background on energy detection and re-
single noise sample and the energy of multiple noise samples views the SNR wall phenomenon. Section III defines the noise
are derived, allowing an optimal Neyman-Pearson detector to
be employed when NU is present, thus avoiding the SNR wall uncertainty model and derives closed-form noise pdfs in the
effect. Initial measurements are presented that explore energy presence of NU. Section IV provides numerical examples that
detection at low SNR in a practical system, showing that the noise illustrate how noise calibration can provide useful detection
distribution can be easily calibrated (learned) using a switch and performance, whereas ignoring detection performance leads to
matched load in the receiver. Useful detection performance down an SNR wall. Section V presents initial measurements showing
to -16 dB with energy detection is demonstrated, and it is found
that noise uncertainty is not significant for an instrument-grade that energy detection at very low SNR is practically possible
low-noise amplifier (LNA) for sub-minute acquisition times. with the noise calibration technique. Finally, Section VI pro-
vides some concluding remarks.
I. I NTRODUCTION
Cognitive radio [1] is an interesting emerging paradigm II. BACKGROUND
for radio networks, where radios are able to sense and ex- This section briefly reviews concepts on optimal detection,
ploit unused spectral resources, ideally improving spectrum energy detection, and noise uncertainty that are required for
utilization and allowing networks to operate in a more decen- the remainder of the paper.
tralized fashion. In the absence of cooperating primary users The problem of primary detection in cognitive radio is
or beacons that indicate local spectrum usage, overlay-based usually treated using classical detection theory [3], where a
cognitive radios must have sensing hardware and algorithms decision must be made among two hypotheses: (H0 ) only noise
that are robust in the sense of providing very low missed is present, or (H1 ) signal plus noise is present. The received
detection rates at low SNR, thus impacting existing licensed waveform xn under these two hypotheses is
users negligibly.
Assuming an ideal noise model and given enough sensing H0 : xn = wn , n = 1, 2, . . . , N
(1)
time, simple methods like energy detection can theoretically H1 : xn = wn + sn , n = 1, 2, . . . , N
discriminate the presence of a primary transmitter, even at
where wn and sn are the nth real noise and signal samples,
very low SNR. In [2] the important effect of uncertainty in
respectively, and the detector must select H0 or H1 based only
the noise distribution was identified and studied in detail,
on observation of xn for n = 1, . . . , N . Given a decision
proving that when noise variance is confined to an interval but
rule, Pd is the probability of detection, or the probability
otherwise unknown, an SNR wall exists, below which useful
that the detector correctly declares H1 , whereas Pfa is the the
detection performance cannot be guaranteed regardless of the
probability of false alarm, or the probability that the detector
observation time.
declares H1 when the true hypothesis is H0 .
The purpose of this paper is to study whether the SNR
When the pdfs of the received waveform xn under hy-
wall phenomenon can be eased by more detailed modeling
potheses H0 and H1 are known, the Neyman-Pearson (N-P)
of the noise uncertainty (NU) and to explore the impact of
detector provides optimal detection performance in the sense
NU through direct measurement. It is argued in [2] that noise
of providing maximum Pd for fixed Pfa . The N-P detector
calibration to learn the noise model is not possible in cognitive
employs a likelihood ratio test (LRT), given by
radio since a primary may be present that corrupts any noise
measurements. However, the dominant noise in RF systems fH1 (x)
L(x) = , (2)
is typically generated in the front-end amplifier and not the fH0 (x)

978-1-4577-1924-0/12/$26.00 ©2012 IEEE 287

where fH (x) is the joint pdf of the observed samples for The distribution of p conditioned on α is given by the Chi-
hypothesis H. For a selected threshold λ, the detector declares Squared distribution, or
H1 when L(x) ≥ λ, otherwise it declares H0 . The threshold α2
can be computed by fixing Pfa and inverting the cdf for the f (p|α) = (α2 p)N/2−1 exp{−α2 p/2}, (7)
2N/2 Γ(N/2)
H0 (noise only) hypothesis.
When noise and signal are both i.i.d. Gaussian, the energy and the marginal distribution f (p) therefore becomes
of the signal, given by 1 ∞
f (p) = f (α)α2 (α2 p)N/2−1 exp{−α2 p/2} dα.
N 2N/2 Γ(N/2) 0
(8)
p= x2 ,
n (3)
The idea of this paper is to choose a distribution for the inverse
n=1
noise level f (α) that not only can be used to calibrate a
is a sufficient statistic. Detection based on p is known as practical system, but also has a simple form allowing (8) to
energy detection, and the distribution of p is given by the be derived in closed form.
Chi-Squared distribution, allowing the required LRT threshold The lognormal distribution is often proposed for modeling
and resulting detection performance to be computed in closed the variance of fading and noise processes, in which case f (σ)
2
form. Given that the variance σ0 = Var(wn ) is known, the is expressed as
energy detector can eventually provide near-perfect detection 1 1
if N is made large enough, even at very low SNR. fLN (σ) = √ exp − (log σ − µLN )2 /σLN . (9)
2
σ 2πσLN 2
Unfortunately, a practical system will only have an estimate
2
of σ0 , and this imperfect knowledge is referred to as noise where µLN and σLN are the mean and standard deviation of
uncertainty (NU). The NU concept was identified and studied log σ. Expressed in dB units µLN = δµdB and σLN = δσdB ,
in detail in [2], where noise variance is assumed to be confined where δ = log(10)/20. Letting α = 1/σ, (9) can be
2 2
to the interval [σlo , σhi ] but otherwise unknown. In this case, transformed to
worst-case detection performance for the N-P detector can be 1 1
fLN (α) = √ exp − (log α + µLN )2 /σLN , 2
computed by assuming ασLN 2π 2
(10)
2
2 σhi , under H0 , which differs from (9) only in the sign of µLN . A major draw-
σ0 = 2 (4)
σlo , under H1 , back of the lognormal distribution, however, is that closed-
form analysis is often difficult.
thus providing the minimum separation of the H0 and H1 pdfs.
For small levels of noise uncertainty, we consider a much
For a given noise interval, as the SNR is lowered a threshold is
simpler model, where f (α) is assumed to be Gaussian which
reached below which the worst-case energy detector exhibits
is fit to (10) using closed-form expressions for the mean and
Pd < Pfa regardless of the number of samples. This complete
variance of (10) given by
detection failure is referred to as the SNR wall.
2
µα = E {α} = exp{−µLN + σLN /2}, (11)
III. N OISE U NCERTAINTY M ODELING σα = Std{α} = 2
[exp(σLN ) − 1] exp(−2µLN + 2
σLN ), (12)
The main idea of this paper is to overcome the SNR where Std(·) denotes standard deviation. The pdf f (α) is then
wall phenomenon by more detailed modeling of the noise given by
uncertainty. In this work, noise and signal are modeled as 
conditional Gaussian processes where a single real sample xn  1 1 (α − µα )2
√ exp − , α > 0,
f (α) = 2 σα2
has the conditional distribution  Cα 2πσα
0, otherwise,
α
f (xn |α) = √ exp{−α2 x2 /2},
n (5) (13)
2π where the rescaling constant Cα = erfc[−µα /( 2σα )]/2 2

α = 1/σ, and σ 2 is the variance. Note that the choice of results from the truncation of the left tail of the Gaussian at
using α rather than σ as the modeled noise parameter in this α = 0 and erfc(·) is the complementary error function. Note
work avoids having integration variables in the denominator, Cα ≈ 1 is omitted from later derivations, but it should be
thus simplifying closed-form analysis. Given an i.i.d. process included if exact expressions are required.
where α is fixed for a short time consisting of N samples, the A. Single Sample: Marginal Distribution
marginal pdf of the vector x is Assuming the Gaussian model for f (α), the marginal distri-
∞
N bution of a single real sample is f (x) = 0 f (α)f (x|α)dα,
1 ∞
α2
f (x) = f (α)αN exp − x2
n dα, (6) or
(2π)N/2 0 2 n=1 1 ∞
2 2 2 2
f (x) = αe−α x /2 e−(α−µα ) /(2σα ) dα, (14)
where f (α) is the pdf of the unknown noise parameter α. Since 2πσα 0
the energy p = N x2 is a sufficient decision statistic here, 1 ∞
2
n=1 n = αe−[aα −bα+c] dα, (15)
we concentrate on this parameter. 2πσα 0

288

where respectively, followed by the substitution u = c1 α2 :
c2
x2 1 2
a= + 2, (16) I1 = (−1)N −k αN −k e−c1 α dα, (35)
2 2σα 0
2 c1 c2
b = µα /σα , (17) (−1)N −k 2
= u(N −k−1)/2 e−u du, (36)
c = µ2 /(2σα ).
α
2
(18) 2cLk
1 0

The integral is of the form of the error function, which can Γ(Lk , c1 c2 )Γ(Lk )
2
be obtained by completing the square, resulting in where Lk = (N + 1 − k)/2, and
2 x
e−c3 e−c1 c2 π √ 1
f (x) = + erfc(− c1 c2 )c2 , (19) Γ(a, x) = e−t ta−1 dt (37)
4πσα c1 c1 Γ(a) 0

is the incomplete Gamma function. Similarly,
where ∞
2 1
I2 = αN −k e−c1 α dα = Γ(Lk ). (38)
c1 = a, (20) 0 2cLk
1
c2 = µα /(σα x2 + 1),
2
(21) Combining results in
1 µ2α 1 N
c3 = 2
1− 2 2 . (22) c0 e−c3 N ck
2 σα σα x + 1 f (p)= 2
Γ(Lk ) 1+(−1)N −k Γ Lk , c1 c2 .
k cL k 2
2 1
k=0
B. Multiple Samples: Energy Distribution (39)
For multiple independent samples, we will consider only
C. Comparison of Gaussian and Lognormal
the distribution of the energy p, which is a sufficient statistic
whose distribution (8) becomes It is instructive to consider in what situations the Gaus-
sian assumption for f (α) provides a reasonable model. Fig-
1 ure 1 plots f (α) side-by-side with f (x) (single sample) for
f (p) = (23)
2N/2 Γ(N/2) µdB = 0 dB and different values of σdB ∈ {0.5 dB, 1.0 dB,
∞
1 2 2 2 2.0 dB}. A log scale is used for f (x) to highlight the small
× √ e−(α−µα ) /(2σα ) α2 (α2 p)N/2−1 e−α p/2 dα,
0 2πσα differences in the distribution tails. For small and moderate
(24) levels of noise uncertainty, the Gaussian approximation for
∞
2 f (α) is very close to the lognormal model. Also, for low noise
= c0 αN e−(aα −bα+c)
dα, (25) uncertainty, the small mismatch in f (α) results in negligible
0
∞
2
error in the marginal density f (x). For larger noise uncertainty,
= c0 αN e−c1 (α−c2 ) −c3
dα, (26) significant differences in the two models can be seen.
0
∞
2 IV. D ETECTION W ITH NU
= c0 e−c3 (α + c2 )N e−c1 α dα, (27)
−c2 In this section we demonstrate with a simple example how
N ∞ having a model of the noise uncertainty can increase detection
N k 2
= c0 e−c3 c2 αN −k e−c1 α dα, (28) performance and remove the SNR wall. In this example,
k −c2
k=0 α is considered to be an unknown parameter following a
I lognormal distribution with µdB =0 dB and σdB =1 dB, which
is subsequently fit using a Gaussian distribution. Signal and
where
noise variance are assumed to be equal (SNR=0 dB) and
p 1 N = 20 samples are used for detection.
a = c1 = + 2, (29)
2 2σα First, a worst-case analysis like that presented in [2] is
2 considered. Here, only bounds are set on the noise level, and
b = µα /σα , (30)
c= µ2 /(2σα ),
2
(31) the structure of the noise variation is ignored. It is assumed
α
N/2−1
that the worst-case values for α are µα ± 1.5σα , which is
p conservative since the α will sometimes fall outside of these
c0 = √ (32)
2 N/2 Γ(N/2) 2πσ
α bounds. Figure 2 shows the Chi-Squared pdfs for the worst
2
c2 = µα /(σα p + 1) (33) case assumption (4), indicating that detection is not possible
1 µ2 1 since the H0 curve is actually to the right of the H1 curve.
α
c3 = 2
1− 2 . (34) Next, the structure of the noise error in (39) is taken into ac-
2 σα σα p + 1
count, producing the pdfs in Figure 3 and indicating sufficient
The integral I = I1 + I2 can be evaluated by letting I1 and I2 separation for useful detection. Figure 4 shows the detection
be the contribution from α on the negative and positive axes, performance from the worst case analysis and the case that

289

7 0
(a) 0.5 dB Gauss
6 LogNorm
-10
5

log f (x)
4 -20
f (α)

3 -30
2 MC
1 -40 Gauss
No NU
0 -50
0.7 0.8 0.9 1 1.1 1.2 1.3 -10 -5 0 5 10
α x
3.5 0
(b) 1.0 dB Gauss
3 LogNorm
-10
2.5

log f (x)
2 -20
f (α)

1.5 -30
1 MC
0.5 -40 Gauss
No NU
0 -50
0.6 0.8 1 1.2 1.4 1.6 -10 -5 0 5 10
α x
1.8 0
1.6 (c) 2.0 dB Gauss
LogNorm
1.4 -10
1.2
log f (x)

-20
f (α)

1
0.8
0.6 -30
0.4 MC
-40 Gauss
0.2
0 No NU
0 0.5 1 1.5 2 2.5 -50
-10 -5 0 5 10
α x

Fig. 1. A comparison of the lognormal distribution on α and a Gaussian approximation of the same distribution for different levels of noise uncertainty:
σdB ∈ {0.5 dB, 1.0 dB, 2.0 dB}. The noise uncertainty pdf f (α) is plotted on the left, and the corresponding marginal single sample pdf f (x) is plotted
on the right for each level of noise uncertainty. MC gives the results of Monte-Carlo simulations of the exact distribution with lognormal NU, compared with
the Gaussian NU approximation (Gauss), and no noise uncertainty (No NU).

exploits the noise error pdf. In the worst-case analysis, the the primary is present or not. However, since most of the noise
SNR wall has clearly been crossed, since Pd < Pfa . On the in a true receiver comes from the front-end low-noise-ampliﬁer
other hand, exploiting the known statistics of the noise error (LNA), the simple architecture depicted in Figure 5(a) can be
allows useful detection even when the exact noise level is used for noise calibration. To learn the noise distribution, the
uncertain. cognitive radio node periodically switches the receive channel
away from the antenna to the matched termination to sample
V. N OISE C ALIBRATION M EASUREMENT and learn the noise distribution.
In this section we present the results of an experiment that This idea was tested using the experimental setup shown
tests the possibility of energy detection at low SNR using schematically in Figure 5(b). The setup employs a custom
practical hardware. As indicated in [2], noise calibration can be multiple-input multiple-output (MIMO) channel sounder that
difﬁcult in traditional wireless receivers where it is unknown if is basically equivalent to that presented in [4], with the

290

0.06 1
fH0 (p) 0.9
0.05 fH1 (p) 0.8
0.04 0.7
0.6
f (p)

Pd
0.03 0.5
0.4
0.02
0.3
0.01 0.2 Modeled NU
0.1 Worst Case NU
0 0
0 20 40 60 80 100 120 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
p Pfa

Fig. 2. Distribution of f (p) for noise (H0 ) and signal plus noise (H1 )
Fig. 4. Probability of detection Pd versus probability of false alarm Pfa for
assuming a worst-case model on the noise variation
the worst-case assumption and the proposed NU model

0.06
fH0 (p)
0.05 fH1 (p)
0.04
f (p)

0.03
0.02
0.01
0
0 20 40 60 80 100 120
p

Fig. 3. Distribution of f (p) for noise (H0 ) and signal plus noise (H1 ) using
the proposed Gaussian NU model

exception that custom FPGA-based data acquisition is used
in the present system.
The transmit (TX) node simulates the primary user, where a
baseband Gaussian signal with a flat W = 20 MHz bandwidth
is generated in 100 µs frames with the arbitrary waveform
generator (AWG), up-converted to 2.55 GHz, power amplified
to 23 dBm, and fed to either the active transmit channel (TX1)
or a matched load (TX0). The channel is a simple direct cable
Fig. 5. Measurement setup for experimental study: (a) envisioned cognitive
connection from the transmitter to receiver, where different radio employing noise calibration, (b) channel sounder based acquisition
fixed attenuators are inserted giving loss L and producing system for experiment, (c) acquisition frame structure
different SNR levels at the receiver.
The receive (RX) node simulates the cognitive radio that
employs a switch to feed its single receive chain either from Four phases are used in each record to probe all four switch
the channel (RX1) or from a matched load (RX0). The receive combinations. Within a single phase, the channel is acquired
chain consists of a 40 dB wideband LNA, down-conversion for T = 100 µs followed by a delay of TD , where TD = 0 can
to a 50 MHz IF, followed by FPGA-based fs = 200 MS/s be used for back-to-back acquisition. During post-processing,
data-acquisition. For this experiment, the raw IF samples are only Ns samples within each acquisition window are used,
stored, passed to a PC, down-converted to complex baseband, thus spanning time Ts = Ns /fs in order to simulate different
and filtered (20 MHz bandwidth) using MATLAB before integration windows in a cognitive radio energy detector. We
performing energy detection. will denote the nth filtered complex-baseband sample, of the
A total of M data records are acquired during each mea- kth phase, in the mth record as xm,k,n . The four phases are
surement, where the mth record is depicted in Figure 5(c). denoted symbolically as k ∈{TX1 RX1, TX0 RX1, TX1 RX0,

291

2 1
1.8 TX1 RX1 800 6400
1.6 TX0 RX1 0.8
Prob. Density

1.4 TX1 RX0
TX0 RX0 100
1.2 0.6

Pd
1
0.8 0.4
0.6
0.4 0.2
0.2
0 0
0 0.5 1 1.5 2 2.5 3 0 0.2 0.4 0.6 0.8 1
Norm. Energy Pfa

Fig. 6. Empirical pdfs (solid lines) for the four measurement phases Fig. 7. Probability of detection (Pd ) versus probability of false alarm (Pfa )
with parameters: TD =0, L=110 dB (ρ = −6 dB), Ns = 100, and for TD = 0, L = 110 dB (ρ = −6 dB), and Ns ∈{100,800,6400}.
M = 800 realizations. Fitted stationary Chi-Squared distributions are also
shown (dashed lines).
2
1.8 TX1 RX1
1.6 TX0 RX1
TX0 RX0}.

Prob. Density
1.4 TX1 RX0
The energy in each record is computed using a variable TX0 RX0
window size Ns according to simple integration, or 1.2
1
N1 +Ns
0.8
pm,k = |xm,k,n |2 , (40) 0.6
n=N1 +1
0.4
where N1 = 50 samples are always skipped at the beginning 0.2
of each frame to avoid artifacts from the switching operations. 0
Empirical distributions of the noise and signal plus noise 0 0.5 1 1.5 2 2.5 3
energy are finally computed with a histogram using the M Norm. Energy
energy snapshots in (40).
Empirical signal and noise pdfs are also compared with ideal Fig. 8. Empirical pdfs (solid lines) for the four measurement phases with
Chi-Squared pdfs with N = 2W Ns /fs degrees of freedom parameters: TD =24 ms, L=110 dB (ρ = −6 dB), Ns = 100, and M =
with sample variance estimated according to 600 realizations. Fitted stationary Chi-Squared distributions are also shown
(dashed lines).
M
2 1
σk = pm,k . (41)
Ns M m=1 measuring the channel with the transmitter not present (TX0
The SNR (ρ) in dB is estimated at an attenuation level of RX1). Finally, good separation of the pdfs for the H0 phase
L =80 dB (high SNR) using and H1 phase (TX1 RX1) is demonstrated, indicating that
2 useful detection with the direct noise measurement is possible.
σTX1RX1
ρ(L = 80 dB) = 10 log10 2 , (42) Figure 7 plots probability of detection Pd versus probability
σTX1RX0 of false alarm Pfa for the same case, computed directly
and SNR at lower attenuation levels is computed using ρ(L) = from the empirical pdfs, where phase TX1 RX0 is used to
ρ(L = 80 dB) + 80 − L. Note that for convenience, pdfs estimate hypothesis H0 . The result shows that when the noise
are normalized with respect to the expected noise energy distribution is measured, near perfect detection is possible if
2
σTX1RX0 Ns . the sample size is made large enough.
Figure 6 plots empirical noise/signal pdfs for the four phases Figure 8 plots the empirical pdfs of the four phases for
compared with Chi-Squared pdfs for TD = 0 (back-to-back ac- the same case, but with a longer delay between acquisitions
quisition), L = 110 dB (ρ = −6 dB), and Ns = 100 samples TD = 24 ms. Although we expected that increased noise
(N = 40). First, the simple Chi-Squared distribution (no noise uncertainty would result and spread the pdfs for the longer
uncertainty) provides a good fit to the empirical pdfs for all acquisition time of 4(TD + T )M = 58 s, they still exhibit
cases, suggesting that the noise parameter α is fairly constant an excellent fit to the simple Chi-Squared distribution with
over the total acquisition time of 4T M = 320 ms. Also, there no noise uncertainty. Possible reasons that the noise process
is no apparent difference in the energy pdfs for the three noise- is so stable is that an instrument-grade LNA is used and the
only (H0 ) phases, indicating that having the RX connected to temperature in the channel sounder was likely very constant.
a matched load (TX1 RX0 and TX0 RX0) is equivalent to In the future, we intend to study inexpensive consumer-grade

292

18 more likely to be observed. However, these results suggest
16 TX1 RX1
TX0 RX1 that detection at SNRs of -16 dB or lower should be possible
14 using simple noise calibration.
Prob. Density

TX1 RX0
12 TX0 RX0
10 VI. C ONCLUSION
8 Although noise uncertainty can be a severe impairment to
6 robust detection in cognitive radio at low SNR, this paper
4 has shown that the SNR wall effect can be overcome by
proper specification of the noise variation. A closed-form pdf
2
0 that assumes a Gaussian distribution for the inverse noise
0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 standard deviation was derived and it was shown that the
Norm. Energy model provides a good fit to the more commonly assumed
lognormal pdf for low to moderate noise uncertainty. A
simulation example was presented, confirming that by properly
Fig. 9. Empirical pdfs (solid lines) for the four measurement phases with
parameters: TD =24 ms, L = 120 dB (ρ = −16 dB), Ns = 6400, and modeling the noise uncertainty, the SNR wall phenomenon can
M = 600 realizations. Fitted stationary Chi-Squared distributions are also be avoided, providing useful energy detection performance at
shown (dashed lines). very low SNR.
1 Initial experimental measurements were also presented that
explore energy detection performance in a true receiver using
0.8 practical hardware. Detection performance based on empir-
ically measured pdfs indicated that useful detection down
6400 to at least -16 dB is possible with energy detection using
0.6 800 sufficient integration time. Measured noise distributions over
Pd

100
0.4 short (0.3 s) and moderate (58 s) acquisition times showed
negligible deviation from a Chi-Squared distribution, suggest-
0.2 ing that the noise level in our system is very stable and that
detailed modeling of the noise uncertainty is unnecessary for
sub-minute integration times.
0
0 0.2 0.4 0.6 0.8 1 Since only an expensive instrument-grade LNA was consid-
Pfa ered in this work, future work will explore noise variation in
low-cost commercial-grade amplifiers that may exhibit noise
statistics that are much less stable. Additionally, due to existing
Fig. 10. Probability of detection (Pd ) versus probability of false alarm (Pfa )
for TD = 0, L = 120 dB (ρ = −16 dB), and Ns ∈{100,800,6400} samples limitations of the system acquisition firmware, we could not
explore the performance of very long acquisition times, which
will also be the subject of future investigations.
LNAs under varying environmental conditions, where noise
uncertainty is more likely to occur and noise calibration is R EFERENCES
more challenging. [1] Mitola, J., III and Maguire, G. Q., Jr., “Cognitive radio: Making software
Figure 9 shows pdfs for the case of lower SNR L = 120 dB radios more personal,” IEEE Personal Commun. Magazine, vol. 6, pp.
(ρ = −16 dB), TD = 24 ms, and Ns = 6400 samples. 13–18, Aug. 1999.
[2] R. Tandra and A. Sahai, “SNR walls for signal detection,” IEEE J.
Even though the separation of the pdfs is poorer in this case, Selected Topics Signal Processing, vol. 2, pp. 4–17, Feb. 2008.
the detection performance plotted in Figure 10 indicates that [3] S. M. Kay, Fundamentals of Statistical Signal Processing: Detection
the SNR wall effect is avoided, and useful detection is still Theory, vol. II, Prentice-Hall, 1998.
[4] B. T. Maharaj, J. W. Wallace, M. A. Jensen, and L. P. Linde, “A low-
possible with a long enough integration time. cost open-hardware wideband multiple-input multiple-output (MIMO)
In the future, the system will be modified to allow longer wireless channel sounder,” IEEE Trans. Instrum. Meas., vol. 57, pp.
integration times where the effects of noise uncertainty are 2283 – 2289, Oct. 2008.

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