 Research
 Open Access
Physical layer metrics for vertical handover toward OFDMbased networks
 Mohamed Rabie Oularbi^{1}Email author,
 FrancoisXavier Socheleau^{1},
 Sebastien Houcke^{1} and
 Abdeldjalil AïssaElBey^{1}
https://doi.org/10.1186/16871499201193
© Oularbi et al; licensee Springer. 2011
 Received: 12 January 2011
 Accepted: 12 September 2011
 Published: 12 September 2011
Abstract
The emerging trend to provide users with ubiquitous seamless wireless access leads to the development of multimode terminals able to smartly switch between heterogeneous wireless networks. This switching process known as vertical handover requires the terminal to first measure various network metrics relevant to decide whether to trigger a vertical handover (VHO) or not. This paper focuses on current and nextgeneration networks that rely on an OFDM physical layer with either a CSMA/CA or an OFDMA multipleaccess technique. Synthesis of several signal feature estimators is presented in a unified way in order to propose a set of complementary metrics (SNR, channel occupancy rate, collision rate) relevant as inputs of vertical handover decision algorithms. All the proposed estimators are "nondata aided" and only rely on a physical layer processing so that they do not require multimode terminals to be first connected to the handover candidate networks. Results based on a detailed performance study are presented to demonstrate the efficiency of the proposed algorithms. In addition, some experimental results have been performed on a RF platform to validate one of the proposed approaches on real signals.
Keywords
 Orthogonal Frequency Division Multiplex
 Orthogonal Frequency Division Multiplex System
 Orthogonal Frequency Division Multiple Access
 Cyclic Prefix
 Orthogonal Frequency Division Multiplex Symbol
1 Introduction
Nowadays, we are facing a wide deployment of wireless networks such as 3G (LTE), WiMAX, Wifi, etc. These networks use different radio access technologies and communication protocols and belong to different administrative domains; their coexistence makes the radio environment heterogeneous.
In such environment, one possible approach to overcome the spectrum scarcity is to develop multimode terminals able to smartly switch from one wireless interface to another while maintaining IP or voice connectivity and required quality of service (QoS). This switching process is known as vertical handover or vertical handoff. This new concept will not only provide the user with a great flexibility for network access and connectivity but also generate the challenging problem of mobility support among different networks. Users will expect to continue their connections without any disruption when they move from one network to another.
The vertical handover process can be divided into three main steps [1, 2], namely system discovery, handoff decision, and handoff execution. During the system discovery step, the mobile terminals equipped with multiple interfaces have to determine which networks can be used and the services available in each network. These wireless networks may also advertise the supported data rates for different services. During the handoff decision step, the mobile device determines which network it should connect to. The decision may depend on various parameters or handoff metrics including the available bandwidth, delay, jitter, access cost, transmit power, current battery status of the mobile device, and even the user's preferences. Finally, during the handoff execution step, the connections need to be rerouted from the existing network to the new network in a seamless manner [3].
Cognitive radio appears as a highly promising solution to this combined problems. Cognitive radio systems can sense their RF environment and react, either proactively or reactively, to external stimuli [4–7]. By the term react, it is implied that the systems have the ability to reconfigure the algorithms and its communication parameters to better adapt to environment conditions. Thus, in principle, the operation of a cognitive radio system includes two stages: sense and decide [8].
This paper focuses on the sensing task. Indeed, we deal with the passive estimation of metrics that help to trigger a vertical handover toward OFDM based systems such as WiFi, WiMAX, or 3G(LTE). It should be noted that the decision step and the handoff execution are not treated in this paper. These tasks may need interaction with the higher layers to guarantee a seamless and proactive vertical handover, which is beyond the scope of this paper. In the context of vertical handover, only the passive estimation is relevant since the terminal seeks to know a priori whether a network satisfies its QoS needs without wasting time and power to get connected to this network. The main contribution of this work relies on the fact that all the proposed metrics are estimated from the physical layer signal and require no connection to the system, no signal demodulation, and no frame decoding. To the best of our knowledge, various VHO decision algorithms based on a MAClayer sensing have been proposed [1, 2, 9–12], but none have been investigated on the PHY layer.
Three relevant and complementary metrics are presented. First, we propose a method to estimate the downlink signaltonoise ratio (SNR). The SNR is an indicator commonly used to evaluate the quality of a communication link. The proposed method exploits the correlation as well as the cyclostationarity induced by the OFDM cyclic prefix (CP) to estimate the noise as well as the signal power of OFDM signals transmitted through unknown multipath fading channel. In addition to the downlink signal quality, some knowledge on the traffic activity can be very informative since it is a good indicator of the network load. Measures of traffic activity strongly depend on the medium access technique of the sensed network. Today, OFDM wireless networks rely either on CSMA/CA (carrier sense multipleaccess/collision avoidance), see Wifi networks for instance, or on OFDMA (orthogonal frequency division multiple access), see WiMAX and 3G(LTE). Concerning the CSMA/CA protocol, we propose to estimate the channel occupancy rate (combined uplink and downlink) and the uplink collision rate, which are two relevant metrics of network load. These metrics can be estimated at the signal level providing that the terminal is equipped of several receiving antennas. For the OFDMA access techniques, the network traffic is estimated through the downlink timefrequency activity rate of the channel. Since OFDMA networks use either synchronous time division duplexing or frequency division duplexing, no collision occurs so that the collision rate metric is irrelevant^{a}.
The rest of the paper is organized as follows: First, we deal with metrics dedicated to CSMA/CAbased networks. In Section 2.1, we present a SNR estimator dedicated to OFDMbased physical layers. Section 2.2 describes the proposed algorithms to estimate the channel occupancy rate of a CSMA/CAbased network. A first algorithm is presented in Section 2.2.3. Then, due to some limitations of the latter, in Section 2.2.5, we propose a second algorithm based on a Parzen estimator, which shown its robustness thanks to simulations. As a complementary metric, in the congested networks, we propose to estimate the channel occupancy rate. The algorithm is derived in Section 2.3, for channels with different lengths on the antennas. Section 3 deals with OFDMAbased systems. In Section 3.1, we show how the proposed SNR estimator can also be applied for OFDMAbased systems, and in Section 3.2, we describe the proposed algorithm for the estimation of the timefrequency activity rate of OFDMA signals. A proposed architecture of the receiver, based on softwaredefined radio is described in Section 4. All the proposed algorithms are evaluated thanks to computer simulations in Section 5. In addition, some experimental results for the channel occupancy rate are also presented in this Section 5.1.4. These results are presented for the first time; many scenarios have been driven to show how the channel occupancy rate is informative about the QoS available in a sensed networks. Furthermore, thanks to these experimentations, we are now able to say that for the case of congested networks, the channel occupancy rate itself is not sufficient enough to decide whether to trigger the handover or not and that the collision rate is a necessary complementary metric. Finally, we outline some conclusions in Section 6.
2 Metrics for CSMA/CA based networks
CSMA/CA is a protocol for carrier transmission in some wireless networks. Unlike CSMA/CD (carrier sense multipleaccess/collision detect), which deals with transmissions after a collision has occurred, CSMA/CA acts to prevent collisions before they happen.
In CSMA/CA, as soon as a node receives a packet to be sent, it checks whether the channel is idle (no other node is transmitting at the time). If the channel is sensed "idle", then the node is permitted to begin the transmission process. If the channel is sensed as "busy", the node defers its transmission for a random period of time called backoff. If the channel is idle when the backoff counter reaches zero, the node transmits the packet. If the channel is occupied when the backoff counter reaches zero, the backoff factor is set again, and the process is repeated.
In this section, we deal with CSMA/CA networks whose physical layer is based on the OFDM modulation scheme. First, we present an algorithm for SNR estimation, then we propose a method for estimating the channel occupancy rate and finally a collision rate estimator is detailed.
2.1 OFDM signals SNR estimation
where ${\mathcal{M}}_{s}$ denotes the number of OFDM symbols in the observation window, E_{ s } is the average available power, and a_{ k, n } are the transmitted data symbols at the n th subcarrier of the k th OFDM block. These data symbols are assumed to be independent identically distributed (i.i.d), D is the cyclic prefix (CP) length, and m ↦ g(m) is the pulse shaping filter.
where $E\left[.\right]$ stands for the expectation operator. To get the SNR, first we have to estimate the noise power ${\sigma}_{w}^{2}$, and then, the power of the received signal S.
2.1.1 Noise power estimation
The estimator with the smallest variance is found for u = L. The difficulty is then to estimate L. In [13], we proposed an estimator of L inspired from maximum likelihood estimation. This estimator has the major advantage of being independent of any threshold level and shows good performance compared to the thresholdbased technique proposed in [14]. Here presented method has a computational complexity (C.C) of $\mathcal{O}\left({\mathcal{M}}_{s}.{D}^{2}\right)$.
2.1.2 Signal power estimation
N_{ c } represents the number of considered cycle frequencies to estimate the signal power. The choice of N_{ c } is a tradeoff between the estimator bias and variance. In [13], we show that we must choose qα_{0} within the coherence bandwidth of the channel B_{ c }. As the channel impulse response is unknown at reception, B_{ c } is approximated as ${\widehat{B}}_{c}=1\u2215\left(\rho \widehat{L}\right)$ where ρ is a coefficient expressing the desired correlation rate within B_{ c }. Consequently, we choose ${N}_{c}=min\left(\frac{{\mathcal{N}}_{sc}+D}{\rho \widehat{L}},\frac{{\mathcal{N}}_{sc}}{2D}\right)$. As shown in [13], ρ's choice has only a very little influence on the estimator performance. The signal power C.C is estimated to be $\mathcal{O}\left({N}_{c}{\mathcal{M}}_{s}\left({\mathcal{N}}_{sc}+D\right)\right)$.
OFDM synchronization can be performed in a nondataaided context by the mean of algorithms such as [16] and [17] for instance. The complexity of these algorithms is $\mathcal{O}\left({\mathcal{M}}_{s}.\left({\mathcal{N}}_{sc}+D\right).D\right)$ for [16] and $\mathcal{O}\left({\mathcal{M}}_{s}.\left({\mathcal{N}}_{sc}+D\right).{D}^{2}\right)$ for [17]. Misssynchronization only impacts the noise variance estimator and has the following effects. If the symbol synchronization is not well performed, signal samples may be included in the noise variance estimator, leading to an overestimation of the noise variance. If the carrier frequency offset is not well mitigated, the phase of $y\left(k\left({\mathcal{N}}_{sc}+D\right)+m\right)$ and $y\left(k\left({\mathcal{N}}_{sc}+D\right)+{\mathcal{N}}_{sc}+m\right)$ will be different so that the redundancy induced by the CP will not be well exploited, leading once again to an overestimation of the noise variance. To put it in a nutshell, both events will lead to an underestimation of the signaltonoise ratio, which is not so dramatic for the vertical handover process. Indeed, underestimating the SNR and not connecting to the access point are much better than overestimating it, and then we find that the QoS does not satisfy our needs and wasting time again finding other potential candidates. We point out that the method presented in [14], as our method, also requires a perfect timefrequency synchronization.
2.2 Channel occupancy rate estimation
In [12, 18], it has been highlighted that the usage of the channel bandwidth in a CSMA/CA system such as WiFi can be approximated as the ratio between the time in which the channel status is busy according to the NAV (network allocation vector) settings and the considered time interval. Indeed, prior to transmitting a frame, a station computes the amount of time necessary to send the frame based on the frame's length and data rate. This value is placed in the duration field in the header of the frame. By reading this file, we have access to the traffic load. The higher the traffic, the larger the NAV busy occupation, and vice versa. Then, once we read a NAV value during a certain time window, the available bandwidth and access delay can be estimated given a certain packet length [19]. The main drawback with this method is that it requires to be connected to the access point in order to have access to the NAV duration from the header. This may increase the decision time if many standards or access points (AP) are detected.
In this section, we propose a method that requires no connection to the AP and no NAV duration reading. This method [20] is based on a physical layer sensing: Considering that the medium is free when only noise is observed and occupied when signal plus noise samples are observed (data frame), we use a likelihood function that can distinguish the signal plus noise samples from the one corresponding to noise only. Once we get the number of signal plus noise samples, a simple ratio processing provides the network occupancy rate.
2.2.1 Model structure
For clarity reason, we assume in this section that we have only one data frame in the observation duration (N_{ s } samples), and Section 2.2.2 explains the proposed algorithm to locate it.
where the x(m) is an OFDM source signal expressed as in (1), h_{ i }(l) is the channel response from source signal to the i th antenna, and L_{ i } is the order of the channel h_{ i }. The process w_{ i }(m) is a complex additive white Gaussian noise with zero mean and variance ${\sigma}_{w}^{2}$. The variance ${\sigma}_{w}^{2}$ is assumed to be known or at least estimated by a subspacebased algorithm [21], where multiple antennas at reception are required.
2.2.2 Frame localization
As presented in the previous section, the vector y_{ i } can be divided into three parts: noise, signal plus noise, and noise. Starting from the set of observation y_{ i }, we would like to find which samples correspond to noise and which ones correspond to signal plus noise. This problem is a classical signal detection problem. Signal detection theory is a wellknown problem in signal processing. This problem deals with the detectability of signals from noise. Many works have been done in this field, and a large literature exists ([22–24], ...). A maximum a posteriori testing, a Bayes criterion, a Neyman Pearson, or an energy detector [25] can be used. Here, we use another approach, since the samples are supposed to be independent in the noise areas and correlated in the signal plus noise area due to the channel effect and their OFDM structure. We propose to use a likelihood function that provides an information about the independence of the processed sample, and we are seeing later that this approach is close to a constant false alarm rate detector, when its main advantage relies on the fact that it does not need to set a threshold value to the detector.
However, for u varying from m_{1} to m_{2}, the number of signal plus noise samples decreases; therefore, the ratio of noise samples to signal plus noise samples increases and by the way $\mathcal{J}\left(u\right)$ increases. It reaches its maximum value if and only if Y_{ i }(u) contains only noise samples, i.e., when u = m_{2}.
Finally, for m_{2} < u < N_{ s }, $\mathcal{J}\left(u\right)$ decreases again for the same reason that the one explained for 1 < u < m_{1}.
2.2.3 Estimation of the channel occupancy rate
2.2.4 Criterion validation limits
In this section, we propose to investigate the limits of the proposed criterion $\mathcal{J}\left(u\right)$. The aim is to find the dynamic where $\mathcal{J}\left(u\right)$ well behaves, i.e., where its slope is positive for signal plus noise samples and negative for noise samples.

For 1 ≤ u ≤ m_{1}: $\mathcal{J}\left(u\right)$ decreases only if $\frac{\partial E\left[\mathcal{J}\left(u\right)\right]}{\partial u}<0$, and therefore if$E\left[\mathcal{J}\left(u\right)\right]=\left({N}_{s}u\right)log\left(\pi {\sigma}_{w}^{2}\right)\frac{1}{{\sigma}_{w}^{2}}\left[\left({m}_{1}u\right){\sigma}_{w}^{2}+\left({m}_{2}{m}_{1}\right)\left({\sigma}_{w}^{2}+S\right)+\left({N}_{s}{m}_{2}\right){\sigma}_{w}^{2}\right]$

For m_{1}≤ u ≤ m_{2}: $\mathcal{J}\left(u\right)$ is an increasing function only if $\frac{\partial E\left[\mathcal{J}\left(u\right)\right]}{\partial u}>0$, then if$E\left[\mathcal{J}\left(u\right)\right]=\left({N}_{s}u\right)log\left(\pi {\sigma}_{w}^{2}\right)\frac{1}{{\sigma}_{w}^{2}}\left[\left({m}_{2}u\right)\left({\sigma}_{w}^{2}+S\right)+\left({N}_{s}{m}_{2}\right){\sigma}_{w}^{2}\right]$
where $\gamma =\frac{S}{{\sigma}_{w}^{2}}$ is the signaltonoise ratio.

For m_{2}≤ u ≤ N_{ s }: we get the same result as in (16).
The right part of the inequality is easy to satisfy, but unfortunately the left part requires the knowledge of the signaltonoise ratio, which is not available in our case. Another approach is to introduce a new criterion that overcomes this drawback; this criterion is the distance between $\mathcal{J}\left(u\right)$, a Parzen estimatorbased criterion introduced in the next section.
2.2.5 Parzen estimatorbased criterion
The proposed solution consists in processing a new criterion that aims to minimize the distance between the true probability density function of the noise and a Parzenestimated probability density function of the observed samples [26, 27]. The main advantage of this new criterion is that it does not rely on Equation (19). We see in Section 5.1 that its performance remains constant for any value of ${\sigma}_{w}^{2}$.
Substituting $\mathcal{J}\left(u\right)$ by $\mathcal{K}\left(u\right)$ in Equation 14, the function Φ(u) is processed to be then used to find the channel occupancy rate Equation (15).
2.2.6 Fluctuations problem
The choice of the length of the smoothing window W is very important. We choose W equal to the length of a SIFS (for Short IFS), which is the smallest interframe interval. Thus, theoretically, we can not get a set of successive noise samples of a length less than a SIFS. Then, if we met a set of noiseonly samples of length less than an SIFS, it means that the algorithm took the wrong decision and Φ(u) will be forced to 1 for those samples.
2.2.7 Relation with the CFAR method
We can demonstrate that there is a direct relation between our method and the CFAR (Constant False Alarm Rate [28]) method. The main difference of the proposed technique is that it does not rely on a false alarm probability P_{ fa }. Indeed, the proposed approach only depends on the noise variance value.
We obtain the same criteria with the CFAR if we choose a ${P}_{fa}=\pi {\sigma}_{w}^{2}$, providing that Equation (19) is satisfied. The main advantage of the proposed approach relies on the fact that the choice of the P_{ fa } is automatic and achieves good performance when Equation (19) is satisfied.
To reduce the computational cost, we propose to compute the criterion in the backward sense, i.e., from its last element and then deducing the other elements recursively. In this case, the CC is reduced to $\mathcal{O}\left(N{N}_{s}\right)$. The whole algorithm is described in Algorithm 1.
Algorithm 1 Channel Occupancy Rate Estimation
Observe N_{ s } samples on the desired channel;
$\mathcal{J}\left({N}_{s}\right)=\frac{1}{N{\sigma}_{w}^{2}}{\sum}_{i=1}^{N}{y}_{i}\left({N}_{s}\right){}^{2}$;
for u = N_{ s }  1: 1: 1 do
$\mathcal{J}(u)=\mathcal{J}(u+1)\left(\mathrm{log}(\pi {\sigma}_{w}^{2})+\frac{1}{N{\sigma}_{w}^{2}}{\displaystyle {\sum}_{i=1}^{N}}{y}_{i}(u{)}^{2})\right)$
end for
Compute the functions Φ(u) values using (14);
Smooth Φ(u) thanks to the described procedure in 2.2.6;
Deduce the C_{ or } thanks to (15).
As the number of users increases, the load increases and the collision probability too. To maintain a good QoS and to avoid the collisions, the backoff intervals are increased in an exponential manner. This leads to injecting a large amount of white spaces in the communication exchange For congested networks, i.e., where all the nodes have a frame ready to be sent in their buffers, we remark that the channel occupancy rate decreases. In order to avoid a VHO in that particular case, it is relevant to have access to another relevant metric in such situation, which is the collision rate.
2.3 Frame collision detection
The contentionbased access mechanism in WiFi implies that all the stations have to listen to the channel before competing for the access in order to avoid collision between the frames. Unfortunately, as the number of competing stations increases, the collision probability increases and the throughput decreases affecting the QoS. Then, the collision rate is a good metric for both horizontal handover where many access points are available and also vertical handover if we wish to hand off from any standard to an OFDM access point.
A proposed method [29, 30] for collision detection in a WiFi system suggests that the AP of a basic service set (BSS) measures RF energy duration on the channel and broadcasts this result. Then, stations can detect collisions by checking the duration against their previous transmission schedules, if they are different it means that a collision occurs. This method assumes that the mobile is able to measure this time duration and requires to be connected and synchronized with the access point.
Within this framework, we propose a method for collision detection that requires no connection to the AP. Once the data frames are detected thanks to the algorithm presented in Section 2.2.2, we use an information theoretic criterion to get the rank of the autocorrelation matrix of the observed frame.
where the x_{ j }(m) for j = 1,..., M are OFDM source signals expressed as in (1), h_{ ij }(l) is the channel impulse response from source signal j to the i th antenna, and L_{ ij } is the order of the channel h_{ ij }.
Note that the dimension of ${\mathcal{H}}_{j}$ is Nd × (L_{ j } + d).
where I_{ Nd } is the identity matrix of order Nd and (.)^{ H } is the transpose conjugate operator.
Therefore, according to Equation (44), the number of sources M is estimated as the nearest integer to $\frac{rL}{d}$. Unfortunately, the channel length L is unknown, and we should have it to estimate M.
To avoid this step, we propose to exploit the properties of the OFDM signals. We know that the length of the cyclic prefix is always chosen to be greater than L_{ ij }. So, if the smoothing factor d is defined as equal to the cyclic prefix, we are sure that L_{ ij } < d.
We can generalize that to estimate a number of sources greater than one. In fact, if r = Md + L then L = r  Md. Since $L={\sum}_{j=1}^{M}\underset{i}{max}\left({L}_{ij}\right)$, we are sure that L < Md and by the way r  Md < Md. Thus, r/M < 2d, and therefore $M>\frac{r}{2d}$. We conclude that $\widehat{M}$ is the nearest integer greater than $\frac{r}{2d}$. If this value equals 1, it means that there is indeed one source, otherwise more than one source is present and a collision occurs. The algorithm is described in Algorithm 2. For each frame, we have to compute the eigenvalue decomposition (EVD) and then perform AIC or MDL. As the C.C of these two algorithms is negligible compared to the EVD, the computational cost is proportional to an EVD.
Algorithm 2 Collision detection algorithm
nb_collision = 0;
Run algorithm described in Section 2.2.2;
for each detected data frame do
Process the autocorrelation matrix R_{ y };
Compute r thanks to (45) or (46);
if ceil(r/ 2d) > 1 then
nb_collision = nb_collision+ 1;
end if
end for
$\mathsf{\text{collisionrate}}=\frac{\mathsf{\text{nb\_collision}}}{\mathsf{\text{thenumberofdetectedframes}}}$
3 Metrics for OFDMAbased networks
Orthogonal frequency division multiple access (OFDMA) is a multiaccess technique based on orthogonal frequency division multiplexing (OFDM) digital modulation scheme. Multiple access is achieved in OFDMA by assigning subsets of subcarriers to individual users in a given time slot. This technique allows to support differentiated quality of service (QoS), i.e., to control the data rate and error probability individually for each user.
First, we propose to apply the algorithm presented in Section 2.1 to get an estimate of the downlink SNR in an OFDMAbased network. Then, we propose an alternative approach to estimate the time frequency activity rate, which is a similar metric of the channel occupancy rate for CSMA/CAbased systems. Concerning the collision rate, as said previously, since OFDMAbased systems are full duplex, no collision occurs and it has no meaning as a metric.
3.1 SNR estimation for OFDMA based systems
The whole algorithm presented in Section 2.1 stays valid for OFDMA signals.
3.2 Timefrequency activity rate estimation for OFDMA system
In OFDMAbased systems, when the number of active subcarriers is small, the data traffic should also be. Therefore, providing a satisfying downlink signal strength, it is better for a multimode terminal to connect on such a base station rather than on one where the data traffic is high (high number of active subcarrier).
In this section, we focus on the passive estimation of the allocation rate of OFDMA physical channels' timefrequency slots. The allocation rate is defined as the number of active slots (allocated symbols) divided by the total number of slots per frame.
In some networks such as WiMAX, the physical channels' allocation rate is regularly broadcasted by the base station so that it can be known by any terminal. However, this requires a multimode terminal that listens to the surrounding networks to intercept every frame preamble. If the multimode terminal has to decode every intercepted preamble to get this information, the vertical handover can be a very time and powerconsuming process.
An alternative approach developed in this section is to get the OFDMA physical channels' allocation rate by blindly estimating the timefrequency activity rate of OFDMA physical signals. Such approach focuses on the signal properties and therefore does not require any message decoding (assuming this message is made available by the base station, which may not be the case in all OFDMA networks). To the best of our knowledge, there is no algorithm published to date that addresses the blind estimation of the timefrequency activity rate of OFDMA signals. We propose a method [32] with a low computational cost to estimate the time frequency activity rate of a WiMAX networks. This method is based on the estimation of the first and secondorder moments of the received signal.
The received signal is expressed as in Equation (2). We assume that the receiver is synchronized with the transmitter in time and in frequency. This synchronization can be realized thanks to the frame preamble or thanks to blind techniques presented in [16] and [33]. We also assume that the noise power ${\sigma}_{w}^{2}$ is known or at least estimated thanks to blind methods such as those detailed in Section 2.1 or in [13, 34].
3.2.1 Estimation algorithm
where H_{ k, n } and W_{ k, n } are, respectively, the channel frequency response at subcarrier n and the noise at subcarrier n of the k th received symbol. The limitation of such approach is that the performance is strongly impacted by the choice of a threshold. In order to avoid this constraint, we hereafter propose complementary alternative method. The proposed technique relies on the absolute value of the first and secondorder moments of the observed samples. These moments are indeed dependent of the activity rate τ.
where $E\left[.\u2215.\right]$ defines the conditional expectation.
where the ${C}_{{M}_{j}}$ constellations are M_{ j }QAM such that for j = 1,..., 4, M_{ j } is equal to 2,4,16,64.
where φ is a function that associate with each ${\sum}_{l}{\sigma}_{h\left(l\right)}^{2}{E}_{s}$ the expectation $E\left[\left{Y}_{k,n}\right\u2215{\epsilon}_{k,n}=1\right]$, when ${\sigma}_{w}^{2}$ is assumed to be known.
This equation has no analytical solution. We propose to solve it by a binary search algorithm. The whole corresponding technique is presented in Algorithm 3. The computational cost of the proposed algorithm is negligible compared to the FFT, and thus the C.C is $\mathcal{O}\left({\mathcal{N}}_{sc}log{\mathcal{N}}_{sc}\right)$.
Algorithm 3 Moments method
Observe ${\mathcal{M}}_{s}$ OFDM symbols;
Estimate ${\sigma}_{w}^{2}$;
Compute Y_{ k, n };
Compute ${\widehat{\mu}}_{1}$ and ${\widehat{\mu}}_{2}$ thanks to (61) and (62);
Deduce $\widehat{\tau}$ solving (63) thanks to the binary search algorithm.
4 Architecture of the proposed detector
The current design of cognitive receivers is based on software defined radio (SDR) technology that enables through software, dynamic reconfiguration of all protocols stacks including the physical layer. In other words, frequency band, airinterface protocol, and functionality can be upgraded with software download and update instead of a complete hardware replacement. SDR provides an efficient and secure solution to the problem of building multimode, multiband, and multifunctional wireless communication devices [7]. A cognitive radio (CR) is an SDR that additionally senses its environment, tracks changes, and reacts upon its findings.
5 Simulation and experimental results
5.1 Metrics for CSMA/CA based networks
In this section, we present computer simulations results that show the algorithms performance.
5.1.1 SNR estimation
In this section, the performance of the proposed estimator is assessed on WiFi signals. WiFi signals are OFDM signals with 64 subcarriers and a guard interval of length equal to 16. The propagation channel {h(l)}_{l = 0,..., L  1}has an exponential decay profile for its nonnull component (i.e., $E\left[h\left(l\right){}^{2}\right]=G{e}^{l\u2215\mu}$ for l = 0,..., L  1), G is chosen such that ${\sum}_{l=0}^{L}E\left[{h}_{k}\left(l\right){}^{2}\right]=1$. The channel is assumed to be time variant with a Doppler frequency equal to 10 Hz for WiFi signals and a rootmeansquare delay spread of 25% of D.
5.1.2 Channel occupancy rate
The proposed method is compared with the CFAR (constant false alarm rate) method with a probability of false alarm P_{ fa } = 10^{4} and with the energy detector proposed by Urkowitz [25], with a P_{ fa } = 10^{4}. The cognitive terminal is supposed to have N = 2 antennas. We can clearly see that the proposed approach outperforms the other methods.
5.1.3 Collision detection
5.1.4 Experimental results
Configurations of the experiments
Equipment  Function  Quantity 

NETGERAR RangeMax WNR3500L  Router and access point, DHCP server  1 
Dell Laptop Mobil Stations  Clients  6 
Dell Laptop PHY Scanner  PHY Scanning and processing  1 
USRP2  Scanning PHY open hardware card  1 
NETGEAR RangeMax WNDA3100  Wireless USB adapter  3 
Intel(R) WiFi Link 5300 AGN  Integral wireless card  3 
JPerf Software  Traffic generator  6 
As explained previously, the aim of the algorithm is to trigger a vertical handoff toward the access point where the traffic is lower. According to the figures, we clearly see that the channel occupancy rate is lower in the configurations where a lower bit rate is required by users and increases as the required bit rate and number of users increases.
5.2 Metrics for OFDMAbased networks
5.2.1 OFDMA SNR estimation
In this section, the performance of the proposed estimators is assessed on WiMAX signals. The configuration tested is a partial usage of subchannels configuration with 512 subcarriers (Section 8.4, Table 310.b, [35]), and D is set to 64. The propagation channel {h(l)_{l = 0,..., L}} has an exponential decay profile for its nonnull component (i.e., $E\left[h\left(l\right){}^{2}\right]=G{e}^{l\u2215\mu}$ for l = 0,..., L), and G is chosen such that ${\sum}_{l=0}^{L}E\left[{h}_{k}\left(l\right){}^{2}\right]=1$. The channel is assumed to be time variant with a Doppler frequency equal to 100 Hz for WiMAX signals and a rootmeansquare delay spread of 25% of D.
5.2.2 OFDMA timefrequency activity rate estimation
In this section, OFDMA signals with 512 subcarriers are considered. D is set to 128, M = 24, and the timefrequency slots' allocation is supposed i.i.d. Each a_{ k, n } is randomly chosen within BPSK, QPSK, 16QAM, and 64QAM constellations according to a uniform law. These constellations are the main constellations used by the WiMAX adaptive modulation and coding (AMC) scheme [35].
6 Conclusion
When the QoS offered to a mobile station does not satisfy the upper layer application, the latter needs to migrate between heterogeneous networks looking for better performance. As a previous step to the vertical handover, a sensing step of the QoS of the present networks is needed. Since these networks rely on different medium access mechanisms, methods to estimate the link quality have to be adapted to each of them.
New metrics for vertical handover toward OFDM systems have been proposed in this article. First, we proposed a method to get the SNR for OFDMbased systems. SNR is the most relevant indicator of the link quality but not always sufficient. Therefore, we focused on the CSMA/CAbased systems and propose to estimate two metrics: The first one is related to the channel occupancy rate and the second one to the collision rate. These two metrics inform us on the MAClayer QoS of the network, such as available bandwidth and access delay, which are relevant to trigger a vertical handover if combined with the SNR. Computer simulation and experimentation are run on WiFi signal (most famous CSMA/CAbased system). Good performances are obtained for the WiFi SNR operating range.
Then, a new blind estimation method of OFDMA timefrequency activity rate has been presented. The method is computationally cheap and exhibits accurate estimation. This approach does not rely on a choice of a threshold and shows good performance compared with the classical CFAR approach even when the noise variance σ_{ w } is estimated.
All the proposed algorithms are blind and rely only on a physical layer sensing, which makes them low computational and avoid time and power waist to get connected^{d}.
End notes
^{a}Note that the intercell interference is neglected here. ^{b}SIMO model is considered here, where multiple antennas at reception are required to estimate the noise variance with no frame synchronization. The proposed technique is also valid in the SISO case, but the noise variance must be known. ^{c}Thanks to S. HADIN, the research engineer who realized the experiments. ^{d} The authors declare that they have no competing interests.
Declarations
Authors’ Affiliations
References
 McNair J, Zhu F: Vertical handoffs in fourthgeneration multinetwork environement. IEEE Trans Wirel Commun 2004, 11: 815.View ArticleGoogle Scholar
 Chen WT, Liu JC, Huang HK: An adaptive scheme for vertical handoff in wireless overlay networks. Parallel and Distributed Systems, International Conference on 2004, 0: 541.Google Scholar
 PinedaRico U, StevensNavarro E, AcostaElias J: Vertical handover in beyond third generation (B3G) wireless networks. Int J Futur Gener Commun Netw 2008, 1: 5158.Google Scholar
 Haykin Simon: Cognitive radio: brainempowered wireless communications. IEEE J Sel Areas Commun 2005, 23(2):201220.View ArticleGoogle Scholar
 Mitola J, Maguire GQ: Cognitive radio: making software radios more personal. IEEE Pers Commun 1999, 6: 1318. 10.1109/98.788210View ArticleGoogle Scholar
 Akyildiz IF, Lee WY, Vuran MC, Mohanty S: Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput Netw Elsevier 2006, 50(13):21272159. 10.1016/j.comnet.2006.05.001View ArticleMATHGoogle Scholar
 Jondral FK: Softwaredefined radio basics and evolution to cognitive radio. EURASIP J Wirel Commun Netw 2005, 3: 275283.MATHGoogle Scholar
 Adamopoulou E, Demestichas K, Theologou M: Enhanced estimation of configuration capabilities in cognitive radio. IEEE Commun Mag 2008, 46(4):5663.View ArticleGoogle Scholar
 Zhu F, McNair J: Optimizations for vertical handoff decision algorithms. Wireless Communications and Networking Conference, 2004. WCNC. 2004 IEEE 2004, 2: 867872.Google Scholar
 Zhang W: Handover decision using fuzzy MADM in heterogeneous networks. Wireless Communications and Networking Conference, 2004. WCNC. 2004 IEEE 2004, 2: 653658.Google Scholar
 Song Q, Jamalipour A: A network selection mechanism for next generation networks. Communications, 2005. ICC 2005. 2005 IEEE International Conference on 2005, 2: 14181422.View ArticleGoogle Scholar
 Dai Z, Fracchia R, Gosteau J, Pellati P, Vivier G: Vertical handover criteria and algorithm in IEEE 802.11 and 802.16 hybrid networks. IEEE International Conference on Communications 2008, 24802484.Google Scholar
 Socheleau FX, AissaElBey A, Houcke S: Non dataaided SNR estimation of OFDM signals. IEEE Commun Lett 2008, 12(11):813815.View ArticleGoogle Scholar
 Cui T, Tellambura C: Power delay profile and noise variance estimation for OFDM. IEEE Commun Lett 2006, 10(1):2527. 10.1109/LCOMM.2006.1576558View ArticleGoogle Scholar
 Jallon P: An algorithm for detection of DVBT signals based on their secondorder statistics. EURASIP J Wirel Commun Netw 2008, 2008: 28:128:9.View ArticleGoogle Scholar
 van de Beek J, Sandell M, Borjesson P: ML estimation of time and frequency offset in OFDM systems. IEEE Trans Acoust Speech Signal Process 1997, 45: 18001805.View ArticleMATHGoogle Scholar
 Ma S, Pan X, Yang G, Ng T: Blind symbol synchronization based on cyclic prefix for OFDM systems. Vehicular Technol IEEE Trans 2009, 58(4):17461751.View ArticleGoogle Scholar
 Guo C, Guo Z, Zhang Q, Zhu W: A seamless and proactive endtoend mobility solution for roaming across heterogeneous wireless networks. IEEE J Sel Areas Commun 2004, 22: 834848. 10.1109/JSAC.2004.826921View ArticleGoogle Scholar
 Zhang Q, Guo C, Guo Z, Zhu W: Efficient mobility management for vertical handoff between WWAN and WLAN. Commun Mag IEEE 2003, 41: 102108.View ArticleGoogle Scholar
 Oularbi MR, AissaElBey A, Houcke S: Physical Layer IEEE 802.11 Channel Occupancy Rate Estimation. ISIVC 2010: International Symposium on Images/Video Communications over Fixed and Mobile Networks, October 2010 2010.Google Scholar
 Xu X, Jing Y, Yu X: Subspacebased noise variance and SNR estimation for OFDM systems. Wireless Communications and Networking Conference, 2005 IEEE 2005, 1: 2426.Google Scholar
 Kay SM: Fundamentals of statistical signal processing, Volume II: Detection Theory. Prentice Hall, Englewood Cliffs; 1998.Google Scholar
 Van Tress HL: Detection, Estimation, and Modulation Theory. Volume IIII. Wiley, London; 1968.Google Scholar
 Van Tress HL: Elements of Signal Detection and Estimation. Prentice Hall, Englewood Cliffs; 1995.Google Scholar
 Urkowitz H: Energy detection of unknown deterministic signals. Proceeding of the IEEE 1967, 55(4):523531.View ArticleGoogle Scholar
 Scott DW: Wiley series in probability and mathematical statistics: applied probability and statistic section. In Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley, London; 1992.View ArticleGoogle Scholar
 Silverman BW: Density Estimation for Statistics and Data Analysis. Chapman & Hall/CRC Monographs on Statistics & Applied Probability, London; 1992.MATHGoogle Scholar
 Scharf L: Statistical Signal Processing: Detection, Estimation, and Time Series Analysis. Addison Wesley, Reading;Google Scholar
 Yun JH, Seo SW: Collision Detection based on RE Energy Duration in IEEE 802.11 Wireless LAN. Communication System Software and Middleware, 2006. Comsware 2006. First International Conference on 2006., 0:Google Scholar
 Yun J, Seo S: Novel collision detection scheme and its applications for IEEE 802.11 wireless LANs. Comput Commun Elsevier 2007, 30(6):13501366.View ArticleGoogle Scholar
 Wax M, Kailath T: Detection of signals by information theoretic criteria. IEEE Trans Acoust Speech Signal Process ASSP33 1985, 387392.Google Scholar
 Oularbi MR, Socheleau FX, AissaElBey A, Houcke S: Blind estimation of the timefrequency activity rate of OFDMA signals. ICUMT 2010: International Conference on Ultra Modern Telecommunications, October 2010Google Scholar
 Park B, Ko E, Cheon H, Kang C, Hong D: A blind OFDM synchronization algorithm based on cyclic correlation. IEEE Globecom Conf 2001, 5: 31163119.Google Scholar
 Socheleau FX, Pastor D, AissaElBey A, Houcke S: Blind noise variance estimation for OFDMA signals. ICASSP 2009, 25812584.Google Scholar
 IEEE Std 802.16, Part 16: air interface for broadband wireless access systems, Amendment 2: Physical and Medium Access Control layers for Combined Fixed and Mobile Operation in License Bands and Corrigendum 1 (2005)Google Scholar
 Jaynes ET: Probability Theory: The Logic of Science. Addison Wesley, NY; 2000.Google Scholar
 cabric D, Mishra SM, Brodersen RW: Implementation issues in spectrum sensing for cognitive radios. Proc. 38th Asimolar Conf. Sig., Sys. and Comp 2004, 772776.Google Scholar
 Socheleau FX, Houcke S, Ciblat P, AissaElBey A: Cognitive OFDM system detection using pilot tones second and thirdorder cyclostationarity. Elsevier Signal Process 2010, 91(2):252268.View ArticleMATHGoogle Scholar
 Li H, BarNess Y, Abdi A, Somekh OS, Su W: OFDM modulation classification and parameters extraction. 1st International Conference on Cognitive Radio Oriented Wireless Networks and Communications 2006, 16.Google Scholar
 Sutton PD, Nolan KE, Doyle LE: Cyclostationary signatures in practical cognitive radio applications. IEEE J Sel Areas Commun 2008, 26(1):1324.View ArticleGoogle Scholar
 Bouzegzi A, Ciblat P, Jallon P: New algorithms for blind recognition of OFDM based systems. Elsevier Signal Process 2010, 90(3):900913.View ArticleMATHGoogle Scholar
 USRP2, Ettus Research LLC website[http://www.ettus.com/]
 Erceg V, et al.: Channel Models for Fixed Wireless Applications, IEEE 802.16 Broadband Wireless Access Working Group. 2001.Google Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.