Title: cognitive radio systems
Efficient use of radio spectrum is a necessity for future wireless systems. While the vast majority of the frequency spectrum is licensed to different organizations, observations provide evidence that usage of the licensed spectrum is by far not complete
neither in the time domain nor the spatial domain. This material discusses basics of Cognitive radio (CR), classifications of
cognitive radio systems, cognitive system architecture, antenna design for cognitive radio system.
The idea of cognitive radio was first presented officially in an article by Joseph Mitola III and Gerald Q. Maguire, Jr in 1999. cognitive radio (CR) refers to wireless architectures in which a communication system does not operate in a fixed assigned band, but rather searches and finds an appropriate band in which to operate Cognitive Radio has been proposed as a promising technology to solve todayâ„¢s spectrum scarcity problem. Cognitive Radio is able to sense the spectrum to find the free spectrum,which can be optimally used by Cognitive Radio without causing interference to the licensed user. Radio spectrum is one of the most scarce and valuable resources for wireless communications. Given this fact, the new insights into the use of the spectrum have challenged the traditional approaches to the spectrum management. Actual measurements have shown that most of the allocated spectrum is largely underutilized and similar views about the underutilization of the allocated spectrum have been reported by Spectrum-Policy Task Force appointed by Federal Communications Commission (FCC). Spectrum efficiency can be increased significantly by giving opportunistic access of the frequency bands to a group of potential users to whom the band has not been licensed. Cognitive Radio has been proposed as a mean to improve spectrum efficiency by exploiting the
unused spectrum in dynamically changing environments. The cognitive radio design is, therefore, an innovative radio design philosophy which involves smartly sensing the swaths of
spectrum and then determining the transmission characteristics (e.g., symbol rate, power, bandwidth, latency) of a group of potential users based on the primary usersâ„¢ behavior. Specifically, cognitive radio is likely to be built
on software defined radio, which allows it to dynamically adjust its transmitter characteristics based on the interaction with the environment in which it operates. Although cognitive radiowas initially thought of as a software-defined
radio extension (Full Cognitive Radio), most of the research work is currently focusing on Spectrum Sensing Cognitive Radio While essentially the entire frequency spectrumis allocated to different applications,
observations provide evidence that usage of the spectrum is actually quite limited, particularly in bands above 3 GHz. Cognitive radio technology is envisaged to solve the problems in wirelessnetworks, resulting from the limited available spectrum and the inefficiency in the spectrum usage by exploiting the existing wireless spectrum opportunistically. CR networks, equipped with the intrinsic capabilities of the cognitive radio, will provide an ultimatespectrum aware communication paradigm in wireless communications. CR networks,however, impose unique challenges due to the high fluctuation in the available spectrum aswell as diverse quality-of-service (QoS)
Depending on the set of parameters taken into account in deciding on transmission andreception changes, and for historical reasons, we can distinguish certain types of cognitive radio. The main two are:
Ã‚Â· Full Cognitive Radio (Mitola radio):
in which every possible parameter observable by a wireless node ornetwork is taken into account.
Ã‚Â· Spectrum Sensing Cognitive Radio:
In which only the radio frequency spectrum is considered. Also, depending on the parts of the spectrum
available for cognitive radio, we can distinguish:
Ã‚Â· Licensed Band Cognitive Radio:
In which cognitive radio is capable of using bands assigned to licensed users, apart from unlicensed bands, such as UNII band or ISM band. The IEEE 802.22 working group is developing a standard for wireless regional area network
(WRAN) which will operate in unused television channels.
Ã‚Â· Unlicensed Band Cognitive Radio:
which can only utilize unlicensed parts of radio frequency spectrum. One suchsystem is described in the IEEE 802.15
Task group 2 specification, which focuses on the coexistence of IEEE 802.11 and Bluetooth.
3. Cognitive radio system requirements:
Cognitive Radio based emergency networks have different requirements compared toordinary networks. Some requirements for the AAF Cognitive Radio have been presented in. Here we mention some physical layer related
1) The multiple services should be supported including real-time voice, data message, still pictures and video. To support multiple services, different constraints on QoS have to be met.
2) The radio needs to be robust to combat bad physical channel conditions.
3) energy-efficiency is a concern because the battery life of radio devices can be a limitation for successful operations.
4) The radio should be operational in presence of intentional jamming. All these requirements have to be supported by a flexible and reconfigurable radio architecture.
4 .Efficient spectrum allocation and management for C.R systems: Spectrum hole concept:
Since most of the spectrum is already assigned, the most important challenge is to share the licensed spectrum without interfering with the transmission of other licensed users as illustrated in Fig. 1. The cognitive radio enables the usage of temporarily unused spectrum, which is referred to asspectrum hole or white space. If this band is
further utilized by a licensed user, the cognitive radio moves to another spectrum hole or stays in the same band, altering its transmission power level or modulation scheme to avoid interference as shown in Fig. 1.
Fig. 1. Spectrum hole concept.
According to the network architecture, cognitive radio (CR) networks can be classified as the infrastructure-based CR network and the CRAHNs (CRAHNs: Cognitive Radio Ad hoc Networks),The infrastructure-based CR network has a central network entity such as a basestation in cellular networks or an access point in wireless local area networks (LANs). On the other hand, the CRAHN does not have any infrastructure backbone. Thus, a CR user can communicate with other CR users through ad hoc connection on both licensed and unlicensed spectrum bands. In the infrastructure-based CRnetworks, the observations and analysis performed by each CR user feeds the central CR base-station, so that it can make decisions on how to avoid interfering with primary networks. According to this decision, each CR user reconfigures its communication parameters, as
shown in Fig. 2a. On the contrary, in CRAHNs, each user needs to have all CR capabilities and is responsible for determining its actions based on the local observation, as shown in Fig. 2b.Since the CR user cannot predict the influence of its actions on the entire network.
Fig. 2. Comparison between CR capabilities for:
(a) infrastructure-based CR networks, and
The components of the cognitive radio ad hoc network (CRAHN) architecture, as shown in Fig. 4a, can be classified in two groups as the primary network and the CR network components. The primary network is referred to as an existing network, where the primary users (PUs) have a license to operate in a certain spectrum band. If primary networks have an infrastructure support, the operations of the Pus are controlled through primary base stations.
Due to their priority in spectrum access, the Pus should not be affected by unlicensed users. The CR network (or secondary network) does not have a license to operate in a desired band. Hence, additional functionality is required for CR users (or secondary user) to share the licensed spectrum band. Also, CR users are
mobile and can communicate with each other in a multi-hop manner on both licensed and unlicensed spectrum bands. Usually, CR networks are assumed to function as stand-alone networks, which do not have direct
communication channels with the primary networks. Thus, every action in CR networks depends on their local observations. In order to adapt to dynamic spectrum environment, the CRAHN necessitates the spectrum-aware
operations, which form a cognitive cycle.
Fig. 3. The CRAHN architecture and the cognitive
radio cycle are shown in (a) and (b), respectively. As shown in Fig. 3b, the steps of the cognitive cycle consist of four spectrum management functions: spectrum sensing, spectrum decision,spectrum sharing, and spectrum mobility. To
implement CRAHNs, each function needs to be incorporated into the classical layering protocols, as shown in Fig. 4.
Fig. 4. Spectrum management framework for CRAHNs.The following are the main features of spectrum
A CR user can be allocated to only an unused portion of the spectrum.Therefore, a CR user should monitor the
available spectrum bands, and then detect spectrum holes. Spectrum sensing is a basic functionality in CR networks, and hence it is closely related to other spectrum management functions as well as layering protocols to
provide information on spectrum availability.
Once the available spectrums are identified, it is essential that the CR users select the most appropriate band
according to their QoS requirements. It is important to characterize the spectrum band interms of both radio environment and the statistical behaviors of the PUs. In order todesign a decision algorithm that incorporates
dynamic spectrum characteristics, we need to obtain a priori information regarding the PU activity. Furthermore, in CRAHNs, spectrum decision involves jointly undertaking spectrum selection and route formation.
Since there may be multiple CR users trying to access the spectrum, their transmissions should be coordinated to prevent collisions in overlapping portions of the spectrum. Spectrum sharing provides the capability to share the spectrum resource opportunistically with multiple CR users which includes resource allocation to avoid
interference caused to the primary network. For this, game theoretical approaches have also beenused to analyze the behavior of selfish CR users. Furthermore, this function necessitates a CR medium access control (MAC) protocol, which facilitates the sensing control to distribute the sensing task among the coordinating nodes as
well as spectrum access to determine the timing for transmission.
Spectrum mobility: If a PU is detected in the specific portion of the spectrum in use, CR usersshould vacate the spectrum immediately and continue their communications in another vacant portion of the spectrum. For this, either a new spectrum must be chosen or the affected links may be circumvented entirely. Thus, spectrum
mobility necessitates a spectrum handoff scheme to detect the link failure and to switch thecurrent transmission to a new route or a new spectrum band with minimum qualitydegradation. This requires collaborating with
spectrum sensing, neighbor discovery in a link layer, and routing protocols. Furthermore, this functionality needs a connection management scheme to sustain the performance of upper layer protocols by mitigating the influence of
spectrum switching. To overcome the drawback caused by the limited knowledge of the network, all of spectrum management functions are based on cooperative operations where CR usersdetermine their actions based on the observed information exchanged with their neighbors.
5. Spectrum sensing for cognitive radio :
A cognitive radio is designed to be aware of and sensitive to the changes in its surrounding,which makes spectrum sensing an important requirement for the realization of CR networks. Spectrum sensing enables CR users to exploit the unused spectrum portion adaptively to the radio environment. This capability is required in the following cases:
(1) CR users find available spectrum holes over a wide frequency range for their transmission (out-of-band sensing), and
(2) CR users monitor the spectrum band during the transmission and detect the presence of primary networks so as to avoid interference (in band sensing).
Fig. 5. Spectrum sensing structure for adhoc CR networks. As shown in Fig. 5, the CRAHN necessitates the following functionalities for spectrum sensing:
The CR user observes and analyzes its local radio environment. Based on these location observations of itself and its
neighbors, CR users determine the presence of PU transmissions, and accordingly identify the current spectrum availability.
The observed information in each CR user is exchanged with its neighbors so as to improve sensing accuracy.
This function enables each CR user to perform its sensing operationsadaptively to the dynamic radio environment. In addition, it coordinates the sensing operations of the CR users and its neighbors in a distributedmanner, which prevents false alarms in cooperative sensing. In order to achieve highspectrum utilization while avoiding interference,
spectrum sensing needs to provide high detection accuracy. However, due to the lack of a central network entity, CR ad hoc users perform sensing operations independently of each other, leading to an adverse influence on
sensing performance. In the following subsection, the investigation of these basic functionalities required for spectrum sensing to address this challenge in CRAHNs is done.
5.1. Primary user detection:
Since CR users are generally assumed not to have any real-time interaction with the PU transmitters and receivers, they do not know the exact information of the ongoing transmissions within the primary networks. Thus, PU detection
depends on the only local radio observations of CR users. Generally, PU detection techniques for CRAHNs can be classified into three groups: primary transmitter detection, primary receiver detection, and interference temperature
management (see Fig. 6). As shown in Fig. 7a, transmitter detection is based on the detection of the weak signal from a primary transmitter through the local observations of CR users. The primary receiver detection aims at finding the PUs that are receiving data within the communication range of a CR user . As depicted
in Fig. 7b, the local oscillator (LO) leakage power emitted by the radio frequency (RF) front-end of the primary receiver is usually exploited, which is typically weak. Thus, although it provides the most effective way to
find spectrum holes, currently this method is only feasible in the detection of the TV receivers. Interference temperature management accounts for the cumulative RF energy from multiple transmissions, and sets a maximum cap on their aggregate level that the primary receiver could tolerate, called an interference temperature limit . As long as CR users do not exceed this limit by their transmissions, they can use this spectrum band. However, the difficulty of this model lies in accurately measuring the interference temperature since CR users cannot
distinguish between actual signals from the PU and noise/interference. For these reasons, most of current research on spectrum sensing in CRAHNs has mainly focused on primary transmitter detection.
Fig. 6. Classification of spectrum sensing.
Fig. 7. Spectrum sensing techniques: (a) transmitter and (b) receiver detection. In transmitter detection, in order to distinguish between used and unused spectrum bands, CR
users should have the capability to detect their own signal from a PU transmitter. The local RF observation used in PU detection sensing is based on the following hypothesis model:
Where r(t) is the signal received by the CR user, s(t) is the transmitted signal of the PU, n(t) is a zero-mean additive white Gaussian noise (AWGN) and h is the amplitude gain of the channel.
H0 is a null hypothesis, which states that there is no licensed user signal in a certain spectrum band. On the other hand, H1 is an alternative hypothesis, which indicates that there existssome PU signal. Three schemes can be used for the transmitter detection in spectrum sensing: matched filter detection, energy detection,
and feature detection
5.1.1. Matched filter detection:
The matched filter is the linear optimal filter used for coherent signal detection to maximize the signal-to-noise ratio (SNR) in the presence of additive stochastic noise. As shown in Fig. 8, it is obtained by correlating a known original PU signal s(t) with a received signal r(t) where T is the symbol duration of PU signals. Then the
output of the matched filter is sampled at the synchronized timing. If the sampled value Y is greater than the threshold k, the spectrum is
determined to be occupied by the PU transmission. This detection method is known asan optimal detector in stationary Gaussian noise.
It shows a fast sensing time, which requires O (1/SNR) samples to achieve a given target detection probability .However, the matched
filter necessitates not only a priori knowledge of the characteristics of the PU signal but also the synchronization between the PU transmitter and the CR user. If this information is not accurate,then the matched filter performs poorly. Furthermore, CR users need to have different
multiple matched filters dedicated to each type of the PU signal, which increases the implementation cost and complexity. For more
practical implementation, a pilot signal of PU systems is used for the matched filter detection. In this method, PU transmitters send the pilot
signal simultaneously with data, and CR users have its perfect knowledge, which may not stillfeasible in CRAHNs. For this reason, energy
detection and feature detection are the most commonly used for spectrum sensing inCRAHNs.
5.1.2. Energy detection
The energy detector is optimal to detect the unknown signal if the noise power is known. Inthe energy detection, CR users sense the
presence/absence of the PUs based on the energy of the received signals. As shown in Fig.9, the measured signal r(t) is squared and
integrated over the observation interval T. Finally, the output of the integrator is compared with a threshold k to decide if a PU is present.
While the energy detector is easy to implement, it has several shortcomings. The energy detector requires O(1/
SNR2) samples for a given detection probability Thus, if CR users need to detect weak PU signals (SNR: _10 dB to _40 dB), the energy
detection suffers from longer detection time compared to the matched filter detection. Furthermore, since the energy detection depends
only on the SNR of the received signal, its performance is susceptible to uncertainty in noise power. If the noise power is uncertain, the
energy detector will not be able to detect the signal reliably as the SNR is less than a certain threshold, called an SNR wall .In addition, while
the energy detector can only determine the presence of the signal but cannot differentiate signal types. Thus, the energy detector often
results in false detection triggered by the unintended CR signals. For these reasons, in order to use energy detection, CRAHNs need to
provide the synchronization over the sensing operations of all neighbors, i.e., each CR user should be synchronized with the same sensing
and transmission schedules. Otherwise, CR users Cannot distinguish the received signals from primary and CR users, and hence the sensing
operations of the CR user will be interfered by the transmissions of its neighbors.
5.1.3. Feature detection:
Feature detection determines the presence of PU signals by extracting their specific features such as pilot signals, cyclic pre.xes, symbol rate,
spreading codes, or modulation types from its local observation. These features introduce built-in periodicity in the modulated signals,
which can be detected by analyzing a spectral correlation function as shown in Fig. 10. The feature detection leveraging this periodicity is
also called cyclostationary detection. Here, the spectrum correlation of the received signal r(t) is averaged over the interval T, and compared ith
the test statistic to determine the presence of PU signals, similar to energy detection . The main advantage of the feature detection is its
robustness to the uncertainty in noise power. Furthermore, it can distinguish the signals from different networks. This method allows the CR
user to perform sensing operations independently of those of its neighbors without synchronization. Although feature detection is
most effective for the nature of CRAHNs, it is computationally complex and requires significantly long sensing time. In the enhanced
feature detection scheme combining cyclic spectral analysis with pattern recognition based on neural networks is proposed. The distinct
features of the received signal are extracted using cyclic spectral analysis and represented by both spectral coherent function and spectral
correlation density function. The neural network, then, classi.es signals into different modulation types. In it is shown that the feature detection
enables the detection of the presence of the Gaussian minimum shift keying (GMSK)modulated GSM signal (PU signal) in the
channel under severe interference from the orthogonal frequency division multiplexing (OFDM) based wireless LAN signal (CR signal)
by exploiting different cyclic signatures of both signals. A covariance-based detection scheme based on the statistical covariance or autocorrelations of the received signal is proposed in.The statistical covariance matrices or autocorrelations of signal and noise are
generally different. The statistical covariance matrix of noise is determined by the receiving filter. Based on this characteristic, it
differentiates the presence of PU users and noise. The method can be used for various signal detection applications without knowledge of the
signal, the channel and noise power.
5.2. Sensing control:
The main objective of spectrum sensing is to find more spectrum access opportunities without interfering with primary networks. To this end,
the sensing operations of CR users are controlled and coordinated by a sensing controller, which considers two main issues on:
(1) how long and frequently CR users should sense the spectrum to achieve sufficient sensing accuracy in in-band sensing, and (2) how
quickly CR user can find the available spectrum band in out-of-band sensing, which are summarized in Fig. 11.
5.2.1. In-band sensing control:
The first issue is related to the maximum spectrum opportunity as well as interference avoidance. The in-band sensing generally adopts
the periodic sensing structure where CR users are allowed to access the spectrum only during the transmission period followed by sensing
(observation) period. In the periodic sensing, longer sensing time leads to higher sensing accuracy, and hence to less interference. But as
the sensing time becomes longer, the transmission time of CR users will be decreased. Conversely, while longer transmission time
increases the access opportunities, it causes higher interference due to the lack of sensing information. Thus, how to select the proper
sensing and transmission times is an important issue in spectrum sensing. Sensing time optimization is investigated in and in the sensing time is determined to maximize the channel efficiency while maintaining the required detection probability, which does not consider
the influence of a false alarm probability. In the sensing time is optimized for a multiple spectrum environment so as to maximize the throughput of CR users.
5.2.2. Out-of-band sensing control
When a CR user needs to find new available spectrum band (out-of-band sensing), aspectrum discovery time is another crucial factor to determine the performance of CRAHNs. Thus, this spectrum sensing should have a coordination scheme not only to discover as
many spectrum opportunities as possible but also to minimize the delay in finding them. This is also an important issue in spectrum mobility
to reduce the switching time. First, the proper selection of spectrum sensing order can help to reduce the spectrum discovery time in out-of band sensing. Moreover, if the CR user senses more spectrum bands, it is highly probable to detect a better spectrum band while resulting in
longer spectrum searching time. To exploit this tradeoff efficiently, a well-de.ned stopping rule of spectrum searching is essential in out-of-band
In CRAHNs, each CR user needs to determine spectrum availability by itself depending only on its local observations. However the
observation range of the CR user is small and typically less than its transmission range. Thus, even though CR users find the unused spectrum
portion, their transmission may cause interference at the primary receivers inside their transmission range, the so-called receiver
uncertainty problem . Furthermore, if the CR user receives a weak signal with a low signal-to noise ratio (SNR) due to multi-path fading, or it
is located in a shadowing area, it cannot detect the signal of the PUs. Thus, in CRAHNs,spectrum sensing necessitates an efficient
cooperation scheme in order to prevent interference to PUs outside the observation range of each CR user .A common cooperative
scheme is forming clusters to share the sensing information locally. For cooperation, when a CR user detects the PU activities, it should
notify its observations promptly to its neighbors to evacuate the busy spectrum. To this end, a reliable control channel is needed for
discovering neighbors of a CR user as well as exchanging sensing information. In addition to this, asynchronous sensing and transmission
schedules make it difficult to exchange sensing information between neighbors. Thus, robust neighbor discovery and reliable information
exchange are critical issues in implementing cooperative sensing in CRAHNs. This cooperation issue will be also leveraged by other
spectrum management functions: spectrum decision, spectrum sharing, and spectrum mobility. Cooperative detection is theoretically
more accurate since the uncertainty in a single userâ„¢s detection can be minimized through collaboration .Moreover, multipath fading and
shadowing effects can be mitigated so that the detection probability is improved in a heavily shadowed environment. However, cooperative
approaches cause adverse effects on resource constrained networks due to the overhead traffic.
6. Cognitive radio transmitter and receiver:
success of the cognitive radio depends on spectrum pooling. References show that orthogonal frequency division multiplexing
(OFDM) is the ideal candidate for spectrum pooling-based wireless transmission systems since subcarriers in the vicinity of
the licensed users can be deactivated in order to minimize interference to the licensed users. Substantial research has
been conducted over the past few years to prove that OFDM is indeed a suitable candidate in the scenario discussed above.
An important design issue associated with the use of OFDM for spectrum pooling based wireless transceiver systems is its
large out-of-band (OOB) side lobe power levels that can potentially interfere with existing neighboring transmissions. There is
a need to substantially suppress these side lobes in order to enable spectrum sharing with primary users. Some of the techniques
been proposed to suppress the side lobe power levels are: windowing the transmitted signal in time domain and/or insertion of
guard bands, singular value decomposition optimization approaches for either inserting cancellation subcarriers or weighting the
subcarriers in order to reduce the side lobe power levels over a neighboring RF spectrum optimization region, transmitting a
modified symbol sequence based on the original sequence but with lower side lobe levels, and a reserved tones based convex
optimization technique for suppressing side lobe power levels as well as peak-to average power ratio (PAPR). The
suppression achieved by applying windowing and insertion of guard carriers is not commensurate with the loss of other
system resources, i.e., the symbol duration is prolonged in the case of windowing the transmitted signal in time domain whereas
insertion of guard bands results in a waste of the available bandwidth. Both insertion of cancellation carriers and subcarrier
weighting involve complex optimization techniques making their real-time implementation expensive when the number
of subcarriers is large and when higher order modulation schemes are involved. The technique mentioned here necessitates
the transmission of considerable amount of side information to the receiver for error free demodulation of the modified symbol
sequence, and hence results in reduced overall throughput. In this material, method used is a two-step technique to reduce the
side lobe power of an OFDM signal without degrading the throughput or the error performances. The described technique first
maps the modulated symbols to an expanded constellation set and chooses the sequence that results in the lowest-possible
side lobe power level . Fundamentally, the idea is to exploit the fact that different symbol sequences have different side lobe
power levels. Side lobe power levels are further reduced by employing a suboptimal cancellation subcarriers (CCs) based
technique which performs a straightforward algebraic operation to compute the amplitude and phase of the cancellation
carriers. It should be noted that, the described technique addresses the problem of side lobe power levels occurring due to the use of OFDM. The spectral leakage occurs due to the use of non-linear power amplifiers.
Fig. 12. Schematic of an OFDM-based cognitive radio transceiver employing the proposed CE+CC-based side lobe suppression technique
6.1 ofdm transceiver:
A general schematic of an OFDM transceiver with dynamic spectrum sensing is shown in Fig. 12. A high speed data stream d(n) is modulated
using M-ary phase shift keying (MPSK).The modulated data stream is then split into N slower data streams using a serial-to-parallel (S/P)
converter. In a DSA environment, it may not be feasible to obtain large contiguous bands for single carrier secondary user transmissions. As a
result, the secondary transmission is performed over several unoccupied frequency bands corresponding to the locations of the white
spaces detected by spectrum sensing mechanisms. This is achieved with a single OFDM transceiver by deactivating subcarriers in
the vicinity of occupied spectrum, thus allowing transmission across the non-contiguous set of frequency bands. We refer to this type of OFDM
as non-contiguous OFDM (NC-OFDM) . Of the remaining active subcarriers, a small fraction are reserved for inserting cancellation subcarriers
(CCs) in order to minimize the out of-band (OOB) interference due to the OFDM side lobes. For a given OFDM input sequence, an expanded symbol sequence that yields a low side lobe power level is determined first by the CE mapper followed by the insertion of the CCs. The actual operation of the CE Mapper and the Insert CCs blocks in Fig. 12 is explained in the following Section. The inverse fast Fourier
transform (IFFT) is then applied to the new sequence. The cyclic prefix (CP) block adds a guard interval to each OFDM symbol, with a
length greater than the channel delay to mitigate the effects of inter symbol interference (ISI). Following the parallel-to-serial (P/S) conversion,
the baseband OFDM signal s(n) is then passed through the transmitterâ„¢s radio frequency (RF)chain, to amplify the signal and up convert it to
the desired frequency. The receiver performs the reverse operation of the transmitter, mixing the RF signal to baseband and yielding r(n). After
converting the serial-to-parallel streams using S/P converter, the cyclic prefix is discarded andthe fast Fourier transform is applied to transform
the time domain data into frequency domain. The symbols over the cancellation subcarriers are then discarded since they do not carry any
information. After compensating for distortion introduced by the channel using equalization, a demapping operation is performed to obtain
transmitted symbols. Then, the data in the subcarriers is multiplexed using a P/S converter, and demodulated into a reconstructed version of
the original high-speed input, Ã‹â€ d(n).
6.2.Side lobe suppression techniques:
The flow diagram shown in Fig. 13 gives a brief idea about the implementation of the used combined CE and CC technique. First, the maximum side lobe power over N/2 subcarrier locations on either side of the OFDM spectrum occupying N subcarriers is calculated. This value
is compared with a threshold before performing either of the side lobe suppression techniques. However, in order to demonstrate the best
achievable suppression by combining the two techniques, we set the threshold level to a large value. It can also be noted that, we insert the
CCs after performing CE operation in order to reduce the side lobe re-growth that occurs because of the original MPSK modulation as well as the mapping operation.
Fig. 13. The constellation expansion algorithm for symbol selection employed in the described two-step algorithm.
6.2.1. Operation of the CE Mapper block:
In the described constellation expansion (CE) technique, the symbols obtained by modulating the input bit sequence to a 2^k constellation
space are mapped to an expanded constellation space consisting of 2^k+1 constellation points. In other words, for every constellation point in the original symbol sequence, there are two points to choose from in the expanded constellation space. Selecting one of the points
on a random basis, each symbol in a sequence of N symbols is mapped to N symbols from the expanded symbol set. An underlying assumption with the proposed CE technique is, the transmitter and the receiver have prior knowledge of the points of the expanded constellation that are associated with the points in the original constellation. Hence, after the demodulation process, the symbols can
be re-mapped to the points of the original constellation. With this knowledge, no side information is needed to be shared between the
transmitter and the receiver. As an example, an approach for mapping QPSK symbols to an expanded constellation space is shown in Fig.
12. The rationale behind this association of points is to take advantage of the randomness involved in selecting one of the two points, and
hence the combination of different in-phase and Quadrature-phase components from all the subcarriers would result in a sequence with
lower side lobes. Instead of the mapping shown, if points a1 and d2 or points a1 and c2 from the expanded constellation are associated with the original point a, a random selection would lead to selecting points which have the same sign over either the real component or the imaginary component. So, there might not be any difference in the sidelobe pattern over one degree of freedom except for a change in scale.
The algorithm that selects the sequence randomly is shown in Fig. 14. For each symbol, the maximum interference power level is
calculated and j iterations are performed. After each iteration1, if the calculated interference power level of the new sequence is less than a
predefined threshold or if the limit on the number of iterations is reached, the sequence with the lowest possible interference level out of
all the randomly selected sequences is assigned to the original sequence. This process is repeated over all the symbols. It can be noticed from the algorithm that the complexity of the algorithm is directly dependant on the value of iterations threshold, i.e., the maximum number of
iterations allowed. If the value of this variable is large, there is a greater number of sequences from which the desired sequence with the lowest maximum side lobe power is chosen. Thus, there is a greater possibility of encountering asequence which has lower side lobe power
levels. Therefore, the average suppression over a large number of symbols can be expected to belower in the case where the number of allowed iterations is large.
6.2.2. Operation of the Insert CCs block:
In this subsection, the low complexity procedure employed for computing the symbols over the cancellation subcarriers (CCs) is described. Suppose we define the total number of subcarriers that can be transmitted by a secondary user in a spectral white space as L =
LA + LCC, where LA is the number of active subcarriers used for signal transmission, and LCC is the total number of subcarriers reserved
for inserting cancellation subcarriers. As a result, the frequency response of each subcarrier is given by:
Where y represents the frequency-shifted to the center frequency of the OFDM system f0 and normalized to the subcarrier bandwidth 1/T0,
i.e., y = (f -f0)T0. Since an OFDM signal consists of the superposition of individual subcarrier spectra, this yields the OFDM pulse shape:
Since the frequency response of an OFDM subcarrier can be represented by the sinc function, the side lobe power levels of
the composite signal, at any frequency location which consists of superposed and frequency translated subcarriers, can be algebraically
computed as the sum of the sidelobe powers of each of sinc function at that location given the input sequence. Therefore, if Ik represents the
sidelobe amplitude level at the kth frequency index (in the OOB region) normalized to the subcarrier bandwidth, then we can express this
amplitude level as:
Suppose now that the amplitude level of the cancellation subcarrier inserted at j = LA/2 + 1
to nullify the sidelobe at the k = L/2 + 1 frequency index is selected in such a way that it possesses a side lobe at the kth frequency index,
which is equal in amplitude but opposite in sign to Ik. In other words, we select Cj such that,Ck=-Ik. In the case of multiple cancellation
subcarriers, the symbols over cancellation subcarriers are computed iteratively for minimizing the side lobe power. The symbol
over the first cancellation subcarrier is computed such that it nullifies the first OOB sidelobe amplitude. The symbol over the second
cancellation subcarrier is computed for minimizing the second OOB sidelobe amplitude.The procedure can be continued for a given
number of cancellation subcarriers until the desired sidelobe power levels is achieved. However, significant side lobe power
suppression can be achieved with a small number of CCs, such as one or two subcarriers on each side of a contiguous group of
subcarriers, resulting in a reasonable trade-off between bandwidth reduction and achievable interference suppression. The technique
described above is illustrated in the form of a flow diagram as shown in Fig. 14. Steps 1 and 2 initialize the variables i and p that count the
number of OFDM symbols and the number of cancellation carriers respectively. Step 3performs LA multiplications and LA additions
and determines the number of computations involved in the algorithm. Thus, as the number of active subcarriers increases, the number of
multiplications and additions also increases. Step 4 merely performs a scaling operation of the cancellation carrier in order to nullify the
effect of the side lobe amplitude level. Step 5 performs Steps 2 through 4 on the right hand side of the OFDM symbol spectrum. Step 6
terminates the procedure when the defined numbers of cancellation subcarriers are inserted on either side of the OFDM symbol spectrum,
after which Step 8 is executed to repeat the operation over the next OFDM symbol. Step 7 is executed to keep count of the number of
cancellation subcarriers inserted. It can be observed that this procedure does not involve any optimization and hence is a low-complexity
approach towards sidelobe suppression. The number of additions and multiplications involved in performing Step 3 for an OFDM
transceiver employing QPSK or higher order modulation scheme with the total number of subcarriers in the spectral whitespace being L =
LA+LCC are 2Ãƒâ€” LAÃƒâ€” LCC each. It can be noted that for a fixed number of cancellation subcarriers, the computational complexity grows
linearly as the number of subcarriers increase. Additionally, the number of computations involved in performing Step 3 are further
reduced by a significant amount when only a fraction of the total number of active subcarriers i.e., those present near the edges of the OFDM
signal spectrum are considered to have more impact on the side lobe power levels than those that are interior in the transmission spectrum. In
contrast, all the existing procedures with the involve complex optimization procedures, which add significant cost to the complexity of the
7. Antennas for C.R.systems:
The key distinction is that the CR would perform intelligent decision making. To inform these decisions the CR would
continually scan the available frequency spectrum. This information would be used in order to detect and classify any legacy
users who may begin transmitting on the CRâ„¢s frequency. In this event protocol would probably dictate that the CR should vacate
the band immediately. During each frequency scan the CR would also identify unused areas of spectrum together with
radio born interference on its operating frequency. This information would be used when reconfiguring the radio in order to
select an area of free spectrum which best suited the userâ„¢s needs. Factors involved in this decision would include the content of
message (e.g. urgent or non-urgent), the bandwidth required, and the spot price for renting spectrum. This flexible pooling of the
radio spectrum would provide the user with an improved and more reliable service. It would also liberalize the conditions for
spectrum trading and make far more intensive use of the entire radio spectrum whilst avoiding spectrum traffic jams.
I t is clear that the CR will require some rather specialized antennas and front-end transceiver circuitry in order to handle the
demands of frequency scanning and communication. To illustrate typical challenges in antennas for CR the existing FCC UWB band
(3.1 GHz to 10.6 GHz) is chosen, although a fuller implementation may involve frequencies from 400 MHz (or lower) to 10 GHz. Although
the architecture of CR has not yet been standardized, some experts suggest that an ultra- wideband (UWB) antenna should be used for
performing the sensing function . A narrowband antenna with a reconfigurable frequency would then handle communications. There is an
extensive body of literature on planar UWB antennas. Many of these antennas have evolved from broadband 3-D structures which include
the biconical antenna. There are however no examples of very closely integrated wide and narrowband antennas in the literature. This kind
of integration would be essential within a portable CR handset where the available space would be very limited. The antennas reported
here were specifically developed to address this need. Narrowband Shorted Patch Antenna and Integrated Wine-Glass Shaped UWB Monopole are the two antennas described here.
Fig. 15 shows the geometry of the described antenna. The antenna is printed on a Taconic TLC substrate with a relative permittivity of er=3+/-0.05 and a thickness ofh=0.79 mm.
8.C.R system: applications:
8.1. Government and Regulatory Interest
With the promises of an intelligent and aware device, a wide range of applications have emerged, from Dynamic Spectrum Access
(DSA) to interoperability solutions to the idea of a universal portable communicator; all of these target markets that range from military to
commercial. DSA is currently being considered as the prime candidate for the first practical application of cognitive radio technology. The
impact and possible importance of this application is felt throughout the United States agencies responsible for spectrum management.
The Federal Communications Commission (FCC), the National Telecommunications and Information Administration (NTIA) and the
Department of State have expressed interest in what CR technology has to offer and how it would affect their current regulatory scheme. In
particular, the FCC has launched a set of initiatives to facilitate the development and deployment of this technology. One of their
most recent actions will allow the use of cognitive radios/cognitive applications to be incorporated into certain TV bands . The CR
impact also extends beyond our geographical border as other countries and international agencies such as the International
Telecommunications Union (ITU) are looking to adopt a similar cognitive radio approach to increase spectrum utilization. We acknowledge
that although dynamic spectrum access looks to be very promising, the complexity required to achieve it could be overwhelmingly difficult.
The military community has recognized the benefits that this new radio technology offers. With frequency agility and/or flexibility, the
ability to enhance interoperability between different radio standards, and the capability to sense the presence of interferers, CR has become
a must-have technology. This technology offers advantages in the protection of communication transmissions, recognition of enemy
communications, and the discovery of paths of opportunity. By recognizing other communication devices, the cognitive radio can
address interoperability issues by adjusting itself to communicate with legacy systems. The U.S.Department of Defense (DoD) has devoted a
great amount of effort to advanced wireless topics in recent years and has established programs such as Speakeasy radio system,
Joint Tactical Radio System (JTRS), and next Generation (XG) to further explore the possibilities of the creation of an intelligent
8.3. Public Safety:
Public safety and emergency response is another area in which cognitive radio has gained a lot of attention. For years public safety agencies have desperately needed additional spectrum allocation to ease frequency congestion and
enhance interoperability. With spectrum sharing capabilities, cognitive radios can prove their effectiveness by utilizing some of the existing
spectrum that is not widely used while help in maintaining call priority and response time. In addition, CRs can play an important part in
improving interoperability by enabling devices to bridge communications between jurisdictions using different frequencies and modulation
formats. The National Institute of Justice issued a call for proposals in which they seek to find a technology that can not only provide them with
a solution to the interoperability issue they currently face but with an ubiquitous system able to handle communication needs yet to
8.4. Broader Impacts and Commercial Use:
With the proliferation of wireless technologies in the ISM band, especially after the success of wireless local area networks (WLAN) like
802.11, interference is becoming increasingly problematic. In urban environments, the ISM band is already showing the symptoms of
spectrum scarcity as the demand for its use continues to increase and performance degradation becomes the norm. Although some
technologies are currently using some adaptive techniques (802.11g uses channel identification, dynamic frequency selection, and
adaptive modulation) to obtain higher data throughput, they are still governed by a standard that limits their full potential. By taking the CR
approach into a challenging RF environment like the ISM band, where inherently the devices need to accept any interference while reducing the
possibility of interfering with others, it provides a framework for us to quantify current system performance and look at the improvements that
the CRs can provide. It is within bands that are heavily utilized that we see the greater need for spectrum efficiency improvement and where the
promises offered by CR technology could render its greater benefits. Leveraging on the success of wireless technologies such as 802.11 and new advances in emerging ones like 802.16 and .22 could help translate current CR research directly into commercial benefits. This could enhance the possibility of the provision for commercial off-the-shelf products for both military and public safety use.
Cognitive radio techniques offer a promising approach to dynamic spectrum allocation. A simple example shows a 20 dB SINR
improvement for wireless LAN using cognitive techniques in an interference environment over that provided by the current IEEE 802.11a
service PHY standard. Spectrum cognition is essential to dynamic spectrum resource management at both node and network level.