Interference Mitigation between Ultra-Wideband Sensor Network and Other Legal Systems
- 1k Downloads
Ultra-wideband impulse radio (UWB-IR) sensor network has intensive military and commercial applications. However, the interference between UWB and other existed networks should be casually investigated. In this paper, we consider interference mitigation in UWB sensors in the context of cognitive radio (CR). Firstly, we suggest a general state transition model to characterize the working states evolution of legal networks, also referred to as primary users (PU). Spectrum sensing, used to identify the state of PU, is formulated as detection of a corresponding state sequence. Maximum posterior probability (MAP) criterion is adopted to perform spectrum sensing. By exploring potential gain of state transitions, detection probability for nearby networks is improved significantly. Subsequently, based on the radius basis function neural network (RBF), we present a novel spectrum sculptor to design UWB waveforms. Attributed to the excellent reconfiguration of RBF, our scheme can produce UWB waveforms tracing available spectrums. The designed waveforms can entirely utilize multiple unoccupied bands to maintain uninterrupted communications. Also, sufficient spectral attenuation can be generated in specific bands to mitigate mutual interference between UWB sensors and other networks. Besides, orthogonal waveforms can be easily derived, which either improves transmission performance or provides a flexible accessing strategy for UWB sensors.
KeywordsPrimary User Energy Detector False Alarm Probability Legal Network Idle Spectrum
Although the traditional Doppler radars have been commonly applied in perimeter monitoring systems, they will fail to detect the target and create coverage shadows when the protected area has obstacles or in a foliage. Additionally, a large object moving outside of the range of interest can create false alarms because of the limited range resolution of narrow-band radars, which cannot distinguish a nearby small target from another larger longer-range one. With a capability of excellent range resolution and penetration, on the other hand, UWB sensor radars have attached extensive investigations in recent years .
The emitted UWB signal occupies a tremendous bandwidth typically of several Gigahertz (GHz). Its fractional bandwidth is also very large, usually greater than 0.2, resulting in a sensor with exceptional resolution that also has the ability to penetrate many common materials. More importantly, such UWB sensors would be independent of Doppler shifts but would detect intrusion by measuring changes in the impulse response of environments. In [2, 3], UWB through-wall motion sensing radars and UWB ground penetrating radars (GPRs) are introduced to meet the requirements of special war field and the probe and rescue after a natural disaster. Recently, Liang et al. initiated the target detection in foliage using UWB radars and proposed that the log-logistic model was much suitable to represent UWB propagation channel in the foliage [4, 5]. Then, the sense-through-foliage target detection using UWB sensors is investigated in [6, 7, 8]. These researches significantly benefit the sense-through-wall and other subsurface sensing problems , which has become asymmetric threats in current and future military operational environments. Although the rapid progress in UWB research is originally inspired by radar sensors to a great extent, UWB sensor networks have also been widely recommended for different applications. For example, UWB network is an ideal candidate for short-range high-data-rate transmission which has been fully discussed in the IEEE standard of wireless personal area network (WPAN), owing to its extremely wide bandwidth . The energy efficiency issue in UWB network is also addressed through medium access control (MAC) protocol design in .
As the demand of data acquisitions and transmissions in various applications continues to grows, such as environment pollution sensing, intelligent traffic guiding, and remote medical monitoring, the current spectrum has become overcrowded and it is hard to allocate fixed frequency band to these new services. Accordingly, the trend of many networks for different purposes operating in nearby geographical regions seems inevitable. So, the interference mitigation and coexistence issues between UWB sensors and other networks should be carefully addressed. The UWB emission power regulation campaign error, launched by U.S. Federal Communications Commission, has been preceded for some years intending for interference mitigation . However, this simple power control strategy does not seem sufficient for antagonistic cooperation between these networks .
Spectrum Sensing. A key element of UWB sensor network, in a cognitive paradigm, is the ability to measure, sense, and be aware of parameters related to the radio channel characteristics and availability of the spectrum. There have been some sensing algorithms including the energy detector (ED) , the matched filtering , and the cyclostationary detection , which have different requirements and advantages. ED is robust and simple but is unfavorable in the presence of noise uncertainty and interference . Cyclostationary detection can differentiate PU from interference and noise; nevertheless, exhaustive search for unknown cyclic frequency makes it extremely computational and hence impractical. In matched detector, specific pilots are employed to achieve optimum detection SNR; however, perfect timing usually can hardly be achieved, which may greatly worsen detection performance. Recently, wavelet-based sensing algorithm is proposed in . In , a novel sensing method is developed based on the statistical covariance of the received signal, which seems also immune to noise uncertainty.
Radio Emissions Strategy. After identifying idle spectrums, UWB sensors need to take real-time adjustment on their emitting parameters to best match current spectral environment. Firstly, the transmit pulse should avoid primary bands to effectively mitigate mutual interference between UWB sensors and other networks. Also, the emitted waveforms should entirely utilize the idle spectrum bands in order to ensure communication reliability of UWB sensors. In , a novel UWB pulse shaping method based on prolate spheroidal wave functions (PSWFs) is presented. In , Gaussian Hermit functions (HGFs) are introduced to soft-spectrum waveform design, but the spectral efficiency is still unfavorable. The UWB shaping filter based on the second-order cone programming (SOCP) is proposed in [23, 24]; however, it is relatively hard to generate sufficient spectral notches to avoid other legal networks. Recently, a new spectrum forming technique based on transform domain communication system (TDCS) is presented in , which can design wideband waveforms according to identified idle spectrums. However, windowing process is indispensable in order to shorten the time domain waveform with an infinitely long tail that may introduce serious inter symbol interference (ISI). As a result, spectrum efficiency of TDCS will be considerably reduced and the out-of-band leakage becomes remarkable.
In this paper, we address the issue of interference mitigation in UWB sensor networks. Unlike the simple assumption that the working states between two adjacent sensing periods are independent, we employ a finite state machine to characterize PU's state transition. On this basis, we further model spectrum sensing as demodulation of a coded sequence with memory. After comprehensively balancing the cost between missing idle spectrums and causing interference to PU, in different applications, false alarm probability and missed detection probability are combined as the overall cost. From the aspect of minimizing the detection probability of state symbols, we then employ the maximum a posteriori criterion (MAP) to perform spectrum sensing. Compared with traditional sensing methods, our scheme can effectively explore potential gain carried by PU's working states and hence significantly reinforce sensing performance. We also reveal the rough interrelation between sensing performance and the potential information carried by PU and provide a new attractive pattern for future spectrum sensing which can be built into other algorithms to further enhance their detection probability.
Subsequently, based on radial basis function (RBF) neural network, we present a novel UWB waveforms generator with a versatile spectrum forming capability, which can produce the emitting signal effectively and flexibly. This scheme requires no frequency hopping between multiple isolated bands; thus it can considerably shorten switch time and reduce hardware complexity. The spectral attenuation of emitted signals can even reach 95 dB in corresponding primary bands, which can ensure the highly reliable communications of other legal networks. Also, our designed UWB waveforms can entirely utilize uncontaminated idle spectrum and the whole spectral efficiency is up to 95%. Therefore, seamless data transmission for UWB users can be basically guaranteed. Meanwhile, by carefully designing the phase response of emitted signals, orthogonal pulses can be obtained which can greatly reduce mutual interference of UWB sensors and enhance the transmission performance of UWB networks, even when there is synchronization derivation. With the efficient self-adjusting algorithm and well-designed reconfigurability, our proposed spectrum sculpting technique totally meets the real-time and highly dynamic demands in cognitive UWB networks.
The remainder of this paper is outlined as follows. In Section 2, we discuss the system model of monitoring the other nearby networks. The working state evolution characteristics of PU is further introduced and an optimal sensing algorithm is proposed, in the sense that minimizing the detection probability, accompanying the robustness analysis of this sensing algorithm. A spectrum sculpting RBF is then proposed to design cognitive UWB waveforms with arbitrary spectrum shaping. Section 3 is dedicated to evaluate the sensing performance through numerical simulations. The performance of the designed UWB waveforms is also presented in this part. Finally, we conclude the whole paper in Section 4.
2. Cognitive-Based Interference Mitigation
In order to effectively mitigate interference between UWB sensors and other legal networks, from the signal processing aspect, two jobs can be suggested in consideration of the underlay nature of UWB. Firstly, UWB sensors accurately identify available spectrums by monitoring the nearby perspective networks. Then, based on the discovered spectral environment, they adjust the RF emissions to advantageously perform their functions, without interfering other networks . These two functions will be elaborated in Sections 2.1 and 2.2, respectively.
2.1. Spectrum Sensing in UWB Sensors
In cognitive UWB sensors, spectrum sensing is mainly adopted to obtain the current states of other networks. Most traditional sensing schemes assume that whether the authorized users exist maintains independent between two adjacent detection periods, and the probabilities of active state and idle state remain the same. This processing strategy can greatly simplify the sensing algorithms; however, it also results in a suboptimal sensing performance.
Practically, the behavior of PU is close associated to its corresponding wireless service, leading to specific probability features on its working states to some extent. This potential information can be properly explored to improve sensing accuracy. There have existed a few literatures that seek to employ partial probability characteristics to enhance sensing performance [27, 28], including the optimization of spectrum detection scheduling to improve multiple channels utilization. Nevertheless, to the best of our knowledge, extensive investigation on spectrum detection by totally utilizing the state transition information of PU has not been addressed in the literature. The interrelation between sensing gain and the prior information also remains not discussed.
Our main contributions in spectrum detections may lie in that, for the first time, we model the working states of PU as a binary sequence characterized by finite state machine. Then, by fully exploring the potential information carried by PU, we employ MAP to perform optimal spectrum sensing and greatly enhance the detection performance. This processing strategy provides a novel insight into spectrum sensing, and our original revealment of the rough interrelation between the achieved sensing gain and PU's state transition characteristics may substantially benefit future researches in cognitive networks.
2.1.1. General Sensing Strategy
As UWB sensors cannot cause much interference to the authorized networks when using spectrum, they should search unused spectrum before establishing their data links. When specific unoccupied authorized band has been detected, the UWB sensor will send its data during the following time slot. However, since PUs may reclaim their spectrum at any time, UWB users should periodically sense the spectrum to avoid interfering nearby networks. So, we adopt the cycle spectrum sensing mode in this paper. The fixed frame duration F is assumed in which the sensing duration is T and the remaining duration F-T is used for data transmission . It is noteworthy that the transmission here means either the data communications or some other dedicated functions, such astarget detection and positioning operations.
Generally, cooperative sensing can alleviate the problem that one single sensor cannot detect the spectrum correctly when there is serious shadow fading . If UWB sensors are taken into consideration, however, single node spectrum sensing is still a reasonable choice. Firstly, in a distributed UWB sensor network with highly dynamic characteristics caused by movement or birth-and-death process of sensor nodes, effective collaboration in spectrum sensing seems hard to be realized. Moreover, the required overhead may create heavy load for UWB network. The cooperative sensing even becomes impractical when the control channels are not available . Additionally, the whole sensing time may become intolerantly long in a cooperative fashion. So, in this paper, we mainly focus on the single node spectrum sensing.
2.1.2. Energy Detection
2.1.3. State Transition of PU
Usually, these transition probabilities vary with time. It is found that the state of authorized networks in each sensing duration corresponds with certain state symbol Open image in new window which constantly changes along the trellis diagram. Specifically, when the state transition occurs at Open image in new window , the following primary state keeps different from that in Open image in new window . As the state transition further extended, Open image in new window can be viewed as a BPSK coding sequence with memory, which is also characterized by a Markov chain. Therefore, the main objective of spectrum detection lies in correctly demodulating this coded sequence Open image in new window . Denoting the two states of PU as binary symbol "0" and "1", the optimum spectrum detection in (6) is equivalent to minimizing the symbol error probability (SER).
The solution for (10) recursively begins with the first symbol Open image in new window ; then the following state symbols Open image in new window are sequentially obtained. The amplitude levels Open image in new window are only 2; so the computational complexity is basically acceptable. But, it is noteworthy that there exist fixed D delays in MAP algorithm. Consider that the idle spectrum may be reclaimed again during a short period, this presented detection algorithm withD accessing delays may miss most chances of using idle spectrum. Therefore, this original MAP-based sensing algorithm may not be appropriate to UWB sensors.
Accordingly, the probability of the primary state entering into busy in the k th detection period, after staying idle for Open image in new window periods, can be expressed as Open image in new window .
2.1.4. Spectrum Detection
Here, Open image in new window is the weighting coefficient ranging in Open image in new window , which can be carefully used to adjust relative preference between missed probability and detection probability. In practice, Open image in new window means the cost of spectral efficiency decline because a missed detection is relatively larger than that of interfering the PU, which implies that the nearby networks possess a strong anti-interference ability. In this situation, we may aggressively improve the utilization efficiency of idle spectrums to facilitate data transmissions of urgent UWB applications. On the other hand, Open image in new window implies that we show much favor to the unperturbed communication link of PU compared to the spectrum efficiency. Hence, strict protection to the authorized networks is necessary.
It is obvious that maximizing Open image in new window gives the minimum sensing expense Open image in new window . Actually, from the information theory aspect, spectrum sensing is equal to detecting a binary-coded sequence that obeys an unsteady state transition, given the reward of detection probability and missed probability. Like in most traditional spectrum sensing algorithms, if two sequential states of authorized users are simply assumed to be independent, the detection probability would be rather limited. However, our MAP-based optimum spectrum sensing is supposed to be much superior to ED, in consideration of entire exploration of the implicit state evolution and corresponding potential coding gain. Compared to matched algorithm and cyclostationary detection, our method only requires a statistic traffic model rather than the specific signal parameters of different networks, which can be conveniently obtained by experiential data or by learning.
2.1.5. Robustness Analysis
When the last state transitions of PU are exactly estimated, the simplified algorithm is equal to MAP algorithm, and it has optimal detection performance. However, from (16), spectrum detection errors in state transition moment will cause error extension in the upcoming periods. To avoid this unfavorable situation, following measures are suggested when PU has entered one state and lasted beyond its mean duration. ( Open image in new window ) We may change the relative ratio between sensing period and transmission period. In an extreme case, the whole cycle is allocated for spectrum detection. ( Open image in new window ) Other advanced sensing algorithms can be employed to assist estimating the exact state transition. ( Open image in new window ) After Open image in new window sensing-transmission periods, usually Open image in new window being among 5–10, we may employ thetruncated sequential detection and retake a long duration to detect the initial state and then repeat this process.
The bad effect on detection probability caused by state transition estimation errors will be discussed in this part, which is instructive if these suggested measures cannot be realized. In the situation with low detection probability, the state transition point can be correctly estimated by adjusting sensing duration. Compared to ED, however, detection gain in this case is quite limited but with a high complexity. So, we mainly analyze the performance decline caused by the state transition estimation errors under high detection probability. Assuming that the average detection probability is Open image in new window , in the worst case, the detection error occurs successively, whose length is about Open image in new window .
The analysis above provides the detection performance in the case that the false state estimations cause error diffusion during following detections. However, it is also noteworthy that the successive estimation errors of Open image in new window are almost impossible to happen; so our analysis only provides a loose low bound for sensing performance with state estimation errors under high detection probability.
2.2. Spectrum Sculpting in UWB Sensors
Detecting the presence of other nearby networks in a given primary band is just the first step in operation of a cognitive UWB sensor. In order to best adapt to current spectral environment and minimize mutual interference, UWB sensors should dynamically adjust the RF emissions after probing the current spectrum. Usually, this process covers the physical layer design as well as the upper layer joint optimization. Cross layer optimization is recommended for selecting transmission parameters according to the upper layer quality of service (QoS). Unfortunately, this process is basically computational and also has intolerable delay in practice. In contrast, the UWB waveform designing rarely considers QoS information from the upper layer, but this flexible strategy allows the simple implementation and fast accessing to idle spectrums and hence is much more suitable for distributed UWB sensors.
2.2.1. RF Requirements in CR
Avoid the licensed frequency band flexibly and effectively. It is possible to avoid authorized frequencies based on frequency hopping technique ; but in this mechanism, UWB sensors can only use one single free band, resulting in rather low spectrum utilization. In addition, oscillator operating at multiple frequencies is required, which also complicates the hardware implementation. Moreover, the switching time for typical PLL can even reach 1 ms, which may prevent UWB sensors from the timely utilization of idle spectrum. On the other hand, spectrum avoidance-based schemes have a limited spectral attenuation, which can hardly eliminate the accumulated interference from multiple UWB sensors to other networks [21, 22].
Use the idle spectrum entirely. Generally, more than one free spectrum hole exist, which always isolates a long spectral distance from each other. The traditional methods can use only one free band. Considering the high uncertainty of authorized band, data transmission of UWB nodes is easy to be interrupted by the reclaim of primary band. In order to ensure seamless communications, UWB sensors should utilize multiple idle bands simultaneously in case one PU reoccupies its primary band. TDCS can take advantage of multiple frequency bands. But, the designed waveform has an infinite long tail which inevitably causes ISI and hence undermines its transmission performance. Yet, truncation by windowing will in turn lead to an obvious degradation on spectral efficiency and remarkable out-band leakage .
Simplify the upper layer design. Most of traditional spectrum access strategies are based on competitive mechanism or centralized scheduling. On one hand, it has to occupy remarkable bandwidth resource to pass the global control signaling. On the other hand, it also has to take a long time to coordinate transmission of each UWB node, which also inevitably misses most spectrum holes.
In this part, we suggest a novel UWB waveform based on the RBF neural network. The designed signal is highly reconfigurable which can entirely match target spectrum shape after an extremely short switching time. Also, efficient spectral attenuation can be produced to eliminate mutual interference between PU and UWB sensors. After the spectral holes have been identified based on our presented sensing algorithm, UWB sensors can immediately access in the following transmission slot by means of orthogonal waveforms, without waiting for a coordination control. Thus, the upper layer control can be considerably simplified, and the UWB sensors capacity can be enhanced at the same time.
2.2.2. System Architecture
where Open image in new window and Open image in new window are both adjustable parameters of the transmission functions Open image in new window , which can be employed to modify their center and width, respectively; Open image in new window is the sampling interval in frequency domain.
Each part of signal is discussed in detail as follows.
(1) Transform Function Open image in new window
where Open image in new window represents the network offset. In the block diagram above, the transmission function Open image in new window has to be implemented by the group of filters in practice, and the time bandwidth product BT of these Gaussian filters is determined by Open image in new window in (25). Note that the number of transform functions is always less than N in order to simplify implementations .
(2) Target Output Open image in new window
where Open image in new window represents current working state of the i th PU.
(3) Parameters Updating
In UWB sensors applications, the duration of the learning algorithm directly determines the accessing delay to idle spectrums. Therefore, we need to optimize the transmission parameters beforehand to further shorten the switching time, also simplifying the implementation of UWB nodes.
In fact, when Open image in new window is big enough, we may let each Open image in new window evenly distributed in frequency axis and employ one single Gaussian filter to generate the basis function Open image in new window . Then, the other basis functions Open image in new window can be obtained by Open image in new window samples cycle shifting operation on Open image in new window , where l represents the shifting factor ( Open image in new window ). So we can further optimize the single parameter Open image in new window . A reasonable parameter Open image in new window should actually be neither too large to avoid serious ripples in the pass band nor too small in case that the designed waveform has an obvious out-band leakage which may interfere PU in adjacent band .
Based on the above simplification efforts, the structure of UWB pulse generator can be obtained as is shown in Figure 2. Firstly, an impulse sequences with a period of N is produced, which is then fed into a Gaussian shaping filter whose key parameter, the time-bandwidth product BT, is determined by the already optimized Open image in new window . Then the network basis function Open image in new window can be formed. The sampling frequency of Open image in new window is set to Open image in new window , where Open image in new window is the maximum frequency of UWB signals. After that, the network input sequences Open image in new window can be constructed after Open image in new window ( Open image in new window ) samples delay has been performed on Open image in new window . Notice that, here, sample delay is equivalent to cycle shift considering the periodic input impulse sequence. Finally, in the updating stage, the UWB waveform shaper makes adjustment to its weighting vector Open image in new window according to target output Open image in new window that relies on nearby spectrum environment.
where Open image in new window is the pruning step. Open image in new window represents the UWB emission spectrum obtained from this shaping network, and Open image in new window is the regulatory spectral constraint. This iterative spectrum pruning process will be continued until all frequency bands have met the given spectral constraints.
2.2.3. Orthogonal Pulse Design
where purelin( Open image in new window ) is the output function of RBF network , Open image in new window denotes the conjugate symmetric spectrum constructed from z which is the representative spectrum of the equivalent lowpass form . The Open image in new window dimensional vector Θ represents the user defined phase response, and the operator ⊗ denotes the vector multiplication between the corresponding two vectors.
As is well known, orthogonal waveforms allow multiple UWB sensors to access the same idle band at the same time and in the same location, without causing serious collision. In a UWB sensor network, therefore, the orthogonal waveform division multiple accessing (WDMA) can also greatly simplify the upper layer protocol design and reduce accessing delay, hence significantly reducing scheduling complexity and improving spectrum efficiency.
Here, we specify Open image in new window to be a binary sequence, for example, Open image in new window . Then, the designed UWB waveforms will keep orthogonal with each other so long as to ensure orthogonality of discrete sequence Open image in new window . If an appropriate pseudorandom sequence, such as Open image in new window -sequenc e, is selected based on the length of sampling lengthN, orthogonal UWB waveforms can be easily derived.
It is noted that from (34) the orthogonality design requires UWB signal remain constant in the whole frequency axis. If the regulatory UWB emission mask is taken into account, such as the FCC emission limits, however, this algorithm has to be further modified. Practically, we may represent the whole spectral line by a combination of constant spectral lines along the frequency axis. Thus, this orthogonality design algorithm is still applicable to each constant spectrum.
3. Numerical Simulations and Evaluations
In this part, we evaluate the performance of our presented algorithms through numerical simulations, both for the spectrum sensing and the UWB waveform sculpting.
3.1. Numerical Results for Spectrum Sensing
In our analysis, the number of sampling points M is set to 80. With respect to the service traffic parameters Open image in new window , corresponding to busy state and idle state of PU, we select five sets of parameters combination to comprehensively study the influence from model parameters on detection performance, which are as follows: ( Open image in new window ) Open image in new window , Open image in new window ; ( Open image in new window ) Open image in new window , Open image in new window ; ( Open image in new window ) Open image in new window , Open image in new window ; ( Open image in new window ) Open image in new window , Open image in new window ; ( Open image in new window ) Open image in new window , Open image in new window . Another group parameters combination is also used for systematical analysis.
3.1.1. Sensing Performance
Actually, as we discussed before, the performance improvement is mainly attributed to the potential coding gain caused by the hidden state transition of PU. Theoretically, the achievable gain is related to theminimum code distance of its corresponding finite state machine . Consider that the state transition here is inhomogeneous, which means that the state transition probabilities change over time, and we can hardly employ a transition matrix to describe it, which also brings in great difficulty in analyzing the minimum code distance. Alternatively, we seek to reveal the interrelationship between the detection gain and the service traffic parameters from a quantitative angle by numerical simulations. From simulations in Figure 3(a), we note the following. ( Open image in new window ) When the state parameters are ( Open image in new window , Open image in new window ) and ( Open image in new window , Open image in new window ), the detection performance can be increased by 2.2 dB and 1.2 dB, respectively. The same results are observed in Figure 3(b). Therefore, it can be concluded that the performance improvement in spectrum sensing is related to the ratio between states duration periods Open image in new window . The larger this duration ratio is, the better the sensing gain is. In an extreme case that the ration tends to be infinite, which means that only one state exists, totally correct detection can be achieved going with the common sense. ( Open image in new window ) In comparison with ED, when the state parameters are ( Open image in new window , Open image in new window ) and ( Open image in new window , Open image in new window ), the detection performance can be increased by 0.7 dB and 0.95 dB, respectively. So, we may say that the detection performance is improved with the increase of cycle duration. However, what to be emphasized is that the sensing gain caused by the increase of state duration is much less than that of by improving the ratio Open image in new window . The reason is that the increase of state duration is equivalent to repeat coding; however, the improvement of Open image in new window implies the minimum coding distance being aggregated. Hence, the achieved coding gain in the former case is limited relatively. Observation from Figure 3 shows that given the service parameters, slight enhancement in spectrum sensing can be obtained by only increasing the observed states duration, which is realized by shortening sensing-transmission period or increasing sampling rates.
3.1.2. Different Preferences
3.1.3. Performance with Detection Errors
3.2. Numerical Results for Spectrum Sculpting
In UWB waveform generation simulations, the length of the basis function N is 180, the equivalent filter order Open image in new window is 32, and the shift factor Open image in new window is 8. We note from the simulations that the UWB pulse can probably reach its convergence after 50 iterations. The out-band attenuation can be further optimized after total 100 iterations. Therefore, our proposed network has a fast convergence. Consequently, the switching time can be considerably shortened. Hence, with little accessing delay, the utilization of idle spectrum can be enhanced.
3.2.1. UWB Waveforms without Emission Limits
3.2.2. UWB Waveforms with Emission Limits
On the other hand, the UWB signal in Figure 6(b) is emitted under a strictly regulatory emission mask in order to avoid interfering other legal wireless services in indoor applications. Here, we adopt the FCC emission mask . At the same time, recent investigations indicated that there may still exist unbearable interference to some specific legal services even if the emitted pulse has adopted the regulatory emission limit . Therefore, the transmit UWB pulses should perform sufficient spectrum avoidance to further eliminate its potential interference to these specific wireless systems. It can be found that, even in this situation, UWB signal can still take full advantage of the regulatory spectrum to improve its communication reliability. As is shown by Figure 6(b), spectrum efficiency can be as much as 97.6% in unoccupied bands. We also assume that there is one active legal network being detected in [5 5.5] GHz. With little effort, the corresponding subweight vector, denoted by Open image in new window , can be determined from (31) with Open image in new window and Open image in new window replaced by the vulnerable band. Then by directly setting Open image in new window to 0, the UWB waveform with spectrum notch can be generated as shown in Figure 6(b). Spectral notches with attenuation larger than 90 dB can be produced, which effectively eliminates the interference from UWB sensors to other networks. Besides, other networks' signals are usually equivalent to narrow-band interference for UWB sensors; so this spectrum notch can be also employed to mitigate the narrow-band interference. In comparison, the obtained spectrum efficiency of the Hermite-Gaussian function based UWB waveform is only 65%, and the spectral attenuation in primary band is 25 dB . The designed shaping filter in  can generate UWB waveforms with a spectrum efficiency of 83.7%. However, given a specific primary user over its working band, the spectral attenuation may be only 30 dB at the expense of the obvious spectrum efficiency decline in nonprimary bands. When the aggregate emission energy from multiple UWB nodes is considered, these exited schemes can hardly mitigate mutual interference between UWB sensors and other legal networks.
3.2.3. Orthogonal UWB Waveforms
In Figure 7(b), we evaluate the transmission performance of existing different waveforms in a UWB network which is based on WDMA. In this experiment, we still adopt FCC emission mask and the maximum UWB frequency is 12 GHz. The uncoded binary pulse amplitude modulation (PAM) is adopted in the transmitter, and the coherent correlator is employed to perform optimal receiving in the presence of AWGN. It is demonstrated from this simulation that the BER performance of our proposed pulses, operating in 4-user WDMA network, can surpass SOCP technique about 2 dB , if accurate timing has been acquired in UWB receivers. When there is timing inaccuracy in UWB receivers, the designed pulses can obtain about 9 dB gain compared to SOCP-based orthogonal pulses, when the maximum timing deviation is about 0.2 nanoseconds in 2-user WDMA network and the BER drops below 10-4. Therefore, from the aspect of the whole UWB network performance, our scheme can indeed enhance transmission performance and reduce stringent requirement on networks synchronization, thus simplifying UWB receiver complexity. Notice that, here, we only show the performance in AWGN channel. As is discussed in , on the other hand, noncoherent receiver is much more suitable for a simple UWB implementation, and in this case, BER performance is closed related to spectral energy carried by single UWB waveforms. Hence, our UWB signal is still supposed to outperform other methods given the UWB emission limits.
We address the coexistence issues between UWB sensors and others networks in this paper. A cognitive-based dynamic spectrum accessing scheme is suggested to mitigate mutual interference between geographic adjacent networks, in which UWB sensors utilize available idle spectrums by monitoring the nearby spectral environment and identifying the unused spectrum. By introducing state transition process to describe the working state of PU, we transform spectrum sensing into the demodulation of an equivalent state sequence. In fact, our presented algorithm provides a new insight in general spectrum sensing which may benefit other specific sensing algorithms. To react to the highly emission adaptation in UWB sensors, a signal generator with great reconfigurable capability is proposed based on RBF network. The designed UWB waveforms can entirely utilize multiple spectral sections to improve the transmission reliability of UWB sensors. Also, our algorithm can produce signals with sufficient spectrum avoidance and totally eliminate mutual interference between nearby networks and UWB sensors. The orthogonal cognitive UWB waveform is also investigated finally. It can be found that, in WDMA-based UWB sensor networks with timing deviation, our orthogonal waveforms considerably outperform other existed UWB orthogonal signals. Future work may include profound analysis on sensing performance in the presence of state estimation errors. Also, the accurate relation between sensing gain and the PU state transition characteristic remains an attractive area in following investigations.
This research was partly supported by the Ministry of Knowledge Economy, South Korea, under the ITRC support program supervised by the Institute for Information Technology Advancement (IITA-2009-C1090-0902-0019). This work was supported by NSFC (60772021, 60972079, 60902046), the Research Fund for the Doctoral Program of Higher Education (20060013008, 20070013029), and the National High-tech Research and Development Program (863 Program) (2009AA01Z262).
- 3.Ground Penetrating Radar http://www.geo-sense.com/GPR.htm
- 4.Liang J, Liang Q, Samn SW: Foliage clutter modeling using the UWB radar. Proceedings of the IEEE International Conference on Communications, 2008, Beijing, China 1937-1941.Google Scholar
- 5.Liang J, Liang Q, Samn SW: A propagation environment modeling in foliage. EURASIP Journal on Wireless Communications and Networking 2010, 2010:-12.Google Scholar
- 6.Liang Q, Samn SW, Cheng X: UWB radar sensor networks for sense-through-foliage target detection. Proceedings of the IEEE International Conference on Communications, May 2008, Beijing, China 2228-2232.Google Scholar
- 7.Liang J, Liang Q, Samn SW, Narayanan R: A differential based approach for sense-through-foliage target detection using UWB radar sensor networks. Proceedings of the IEEE International Conference on Communications, May 2008, Beijing, China 1952-1956.Google Scholar
- 8.Liang J, Liang Q: UWB radar sensor networks detection of targets in foliage using short-time Fourier transform. Proceedings of the IEEE International Conference on Communications, June 2009, Dresden, Germany 1-5.Google Scholar
- 9.Liang J, Liang Q, Samn SW: Sense-through-wall channel modeling using UWB noise radar. Proceedings of IEEE International Workshop on Multi-Gigabit MM-Wave and Tera-Hz Wireless Systems (GlOBECOM '09), November 2009, Honolulu, Hawaii, USAGoogle Scholar
- 11.Ren Q, Liang Q: Throughput and energy-efficiency aware ultra wideband communication in wireless sensor networks: a cross-layer approach. IEEE Transactions on Mobile Computing 2008, 7(7):805-816.Google Scholar
- 12.Federal Communications Commission (FCC) : Revision of part 15 of the commission's rules regarding ultra-wide band transmission systems. In First Report and Order. FCC 02–48; 2002.Google Scholar
- 15.Digham FF, Alouini M-S, Simon MK: On the energy detection of unknown signals over fading channels. Proceedings of the IEEE International Conference on Communications, May 2003, Anchorage, Alaska, USA 5: 3575-3579.Google Scholar
- 16.Cabric D, Mishra SM, Brodersen RW: Implementation issues in spectrum sensing for cognitive radios. Proceedings of the Asilomar Conference on Signals, Systems and Computers, November 2004, Pacific Grove, Calif, USA 1: 772-776.Google Scholar
- 18.Tandra R, Sahai A: Fundamental limits on detection in low SNR under noise uncertainty. Proceedings of the 25th International Conference on Wireless Networks, Communications and Mobile Computing, June 2005 1: 464-469.Google Scholar
- 19.Tian Z, Giannakis GB: A wavelet approach to wideband spectrum sensing for cognitive radios. Proceedings of the 1st International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM '06), June 2006, Island, Greece 1-5.Google Scholar
- 25.Chakravarthy VD, Shaw AK, Temple MA, Stephens JP: Cognitive radio—an adaptive waveform with spectral sharing capability. Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC '05), March 2005, New Orleans, La, USA 2: 724-729.Google Scholar
- 27.Ma J, Li Y: A probability-based spectrum sensing scheme for cognitive radio. Proceedings of the IEEE International Conference on Communications, May 2008, Beijing, China, Proceedings of SPIE 3416-3420.Google Scholar
- 31.Proakis JG: Digital Communications. 4th edition. McGraw-Hill, New York, NY, USA; 2001.Google Scholar
- 32.Vujitic B, Cackov N, Vujitic S: Modeling and characterization of traffic in public safety wireless network. Proceedings of the International Symposium on Performance Evaluation of Computer and Telecommunication Systems, July 2005, Edinburgh, UK 213-223.Google Scholar
- 33.Papoulis A: Probability, Random Variables, and Stochastic Process. 4th edition. McGraw-Hill, New York, NY, USA; 2002.Google Scholar
- 35.Li B, Zhou Z, Zou W: A novel spectrum adaptive UWB pulse: application in cognitive radio. Proceedings of IEEE Vehicular Technology Conference (VTC '09), September 2009, Anchorage, Alaska, USA 1-5.Google Scholar
- 36.Li B, Zou W, Zhou Z: A novel adaptive spectrum forming filter: application in cognitive ultra wideband radio. Science China Information Science. Accepted for publicationGoogle Scholar
- 37.Haykin S: Neural Networks: A Comprehensive Foundation. 2nd edition. Prentice-Hall, Upper Saddle River, NJ, USA; 1998.Google Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.