Spectrum Monitoring Techniques for Spectrum Mobility in Connected Environments: A Technical Review (2024)

With the rapid and recent progresses in the field of technologies such as “Internet-of-Things (IoTs)” [1-8] “Machine-to-Machine communication (M2MC)” [9, 10], next generation (XenG) communication systems, the entire word becomes like a global village where everything (human, devices, machines etc.) is connected with each other and referred as connected environments which is contributing significantly in Industry 4.0 [9-18]. In the connected environments, always/ on demand connectivity is the prime desire which directly depends on the spectrum available however as per the various reports, Due to the fast growth of wirelessly connected gadgets, the spectrum will be scarce for new services in near future [5, 19]. But, according to the "Federal-Communication-Commission" (FCC) report [20], most of the spectrum bands that have been given out are underused at certain times and places. Therefore, the “Dynamic-Spectrum-Access” (DSA) has come out as the prospective contender in the wireless communication to deal with the problem of not having enough range which arises because of fixed spectrum allocation scheme [21-24]. The “cognitive radio (CR)” is a well-known framework to support the idea of DSA in which the “unlicensed/cognitive-user” (CU) is permitted to utilize the spectrum of the “licensed/primary-user” (PU) in such a way that the PU communication remains impermeable, and it uses the following features to do this.: 1) “spectrum sensing” [25-28], 2) “spectrum analysis and decision” [29-31], 3) “spectrum sharing/accessing” [32, 33] and 4) “spectrum mobility” [34-37] as depicted in the Figure 1. First, the CU does spectrum reading to find the channels that aren't being used. Then, after spectral analysis, it chooses the best channel. Also, the CU uses a selected channel for transmission and tells other users in the CRN about it so that they don't use the same channel. Also, the CU has to stop talking when the PU starts talking again on the same channel. If the CU wants to continue the conversation that was interrupted, it has to change the channel. Spectrum switching, hand-off, or mobility is the process of moving from one channel to another. Spectrum accessing methods like "interweave" [38-40], "underlay" [41-43], "overlay" [44, 45], and "hybrid spectrum access" [46-51] protect PU communication from interference caused by CU transmission. Interweave spectrum access is the most commonway to get to the spectrum. With this technique or approach, the CU uses the channels that aren't being used for communication. But in the base spectrum access, the CU uses the PUs' spectrum at the same time that the PUs is talking by putting limits on how much power can be sent. This is done to avoid interference at the PU. In the overlay spectrum accessing strategy, the CU and PU performs communication at same time with full-power however, for interference management, the advance encoding techniques such as “Gel’fand-Pinsker binning” [52] and “dirty paper coding” [53] are used. It is worth to mention that the prerequisite of interference management using advance encoding techniques increases the complexity of overlay spectrum accessing strategy consequently, less popular as compare to other strategies. Conversely, the interference management in underlay technique using power control require the channel state information (CSI) either perfectly or partially [54]. However, the formulation of perfect and partial CSI at the CU transmitter is a challenging task due to uncertainty of channel. So, it's clear that the interweave spectrum accessing strategy is a well-known and possible event that plays a big part in spectrum mobility. This is because the CU only uses the empty channels of the PU for communication, and when the PU shows up, the CU needs to switch the spectrum. In general, there are two ways to move the spectrum: 1) the reflexive, as shown in Figure 1, and 2) the proactive. In the first method, the CU changes its communication after the PU shows up. In the second method, the CU changes its communication before the PU shows up. The complete details about both the approaches are illustrated further below.

1.4 Motivation

In the spectrum prediction process [59], the CU predicts when PUs will appear based on the information about the channel that is already known. However, the SM is a possible technique in which the CU finds PUs during data transmission by using the statistics of the received packets, such as the number of receiver errors (REC), the number of iterations (IC), etc. Spectrum prediction needs to know the state of the channel ahead of time. Spectrum monitoring, on the other hand, doesn't need to know anything ahead of time because the decision is made based on the statistics of the current packet. Researchers have looked at different ways to predict the spectrum in the study by Xing et al. [59]: 1) "Hidden Markov model" (HMM) based, 2) "Multilayer perceptron neural network" (MLNN) based, 3) "Bayesian interference" (BI) based, 4) "Moving averages" (MA), 5) "Auto-regressive", and 6) "Static neighbour graph" (SNG) based approaches. In addition, some recent spectrum prediction techniques are explored by researchers for high frequency communication [60, 61]. Moreover, the SM techniques emphasizing on REC and energy-ratio are discussed briefly in a survey presented to explore the advance spectrum sensing techniques [62]. But, as far as the authors know, research into SM techniques for spectrum mobility is still in its early stages. Because of this, we have reviewed the SM techniques to "detect the emergence of the PU during CU data transmission" in this work.

The prominent inputs of the authors to this paper are summed up as follows:

· Techniques for predicting and keeping an eye on the spectrum are used to show how spectrum movement works.

· The SM with suitable hypothesis and its potential techniques for appropriate application scenarios, is described in detail.

· A novel and generalized SM technique on the bases of energy fluctuation of the received signal is proposed.

· The impact of SM implementation on performance metrics like "data-loss", "power-wastage", "interference-efficiency", and "energy-efficiency" are studied.

· A potential solution to manage the effects of imperfect SM on the performance of CUs is proposed and investigated.

· The potential issue and future research challenges regarding different SM techniques are presented.

The remaining part of this paper is structured as follows. In section II, the SM process with various SM techniques is illustrated. The prospective architecture of the CRN for spectrum monitoring process is presented in section III. The effects of implementation of SM with perfect and imperfect nature are described in section IV. The section V comprises the potential issues and research challenges. Finally, section VI concludes the work presented in this paper.

The SM is a possible method that lets the $\mathrm{CU}$ see the "emergence-of-PU simultaneously with the CU data transmission." This means that the $\mathrm{CU}$ has to "detect the presence of PU signals $\mathrm{s}(\mathrm{t})$ with the already existing $\mathrm{CU}$ signal $C(t)$ and noise $n(t)$." So, the considered binary hypotheses for the received signal $\mathrm{r}(\mathrm{t})$ are $\mathrm{H} _0$ and $\mathrm{H} _1$, which prove, respectively, that $\mathrm{PU}$ is not present and is present.

$r(t)= \begin{cases}h . s(t)+w(t)+C(t) & H_1, \\ w(t)+C(t) & H_0\end{cases}$ (1)

where, $h$ is the "channel-gain" and $w(t)$ designate the "additive white Gaussian noise" (AWGN). The complete process of spectrum mobility using SM is depicted in Figure 5 and illustrated as follows. At first, the CU feels the channel and starts sending data with SM on the idle/free channel it finds. If it doesn't find an idle/free channel, it switches the channel and does spectrum sensing again. In Figure 6, the SM is shown as a black box because there are different ways to show the SM. But at the black box's output, a condition check is done. If the "emergence-of-PU" is confirmed, the $\mathrm{CU}$ stops sending data, changes the channel, senses it, and so on. If it doesn't, the CU keeps sending data until the end of the time frame. Various SM techniques are as follows: A) Receiver error count (REC) based, B) REC plus cyclic redundancy check (CRC) based, C) Energy-based in OFDM, D) Energy-ratio-based in OFDM, E) Error vector magnitude (EVM) based, and F) Energy fluctuation based. These techniques are illustrated in detail as follows.

Figure 5. Illustration of system model for packet transmission using time frame

Figure 6. Flow diagram of spectrum mobility process using spectrum monitoring techniques

In the energy-based SM techniques presented in the study by Ko et al. [65], the CU communication uses OFDM and full-duplex mode [66-68]. But the signal sent by CU has periods of no energy in a certain subcarrier, which are used to find out if PU is present during that time. As shown in Figure 8, the CU plans its transmission with regular zero energy intervals and as shown in Figure 9, the energy of PU is detected and emergence of the PU is confirmed which continues till the completion of PU communication and CU discontinue its transmission. Moreover, the 2nd interval has zero energy which means the PU is absent, therefore the CU resume its communication. The major limitation of this technique is the resource wastage due to zero energy intervals. Therefore, in order to avoid these zero-energy intervals, a new energy-based detection algorithm has been proposed.

2.4 Energy-ratio-based spectrum monitoring technique

When the CU uses OFDM as the "physical layer transmission technique," a frequency-domain-based method called "energy ratio-based SM technique" [69] will work for SM by adding extra features to the normal OFDM signal. The main point of "energy-ratio-algorithm" is to look at how the energy of stored tones changes over time. The entire outline of the energy-ratio algorithm is illustrated in Figure 9. Firstly, the CU sensed its channel during sensing interval and performs data transmission if channel is free otherwise sense alternative channel. The CU employs energy-ratio (SM) algorithm parallel to the data transmission.

The CU emitter (CUT) sends out an OFDM signal, which is picked up by the CU receiver (CUR) and processed in the frequency domain. As part of the energy ratio method, the cyclic prefix $(\mathrm{CP})$ is removed. Also, the reserved tones from the different OFDM symbols are put together to make the complex sample sequence, which is then moved through in the time domain by two moving windows of equal size. Also, the energies of samples that fall within a certain window are measured, and the ratio of the two energies is used as the "decision-making-variable" $\left(D_k\right)$. In addition, the decisionmaking variable is compared to the set threshold $\left(\lambda_k\right)$ to "detect the emergence of PU". To help mathematicians understand the idea, the decision-making variable is described as:

$D_k=\frac{E_k}{F_k}=\frac{\sum_{i=N+k}^{2 N+k-1}\left|Z_i\right|^2}{\sum_{i=k}^{N+k-1}\left|Z_i\right|^2}$ (3)

where,

The mathematical representation is as:

The mathematical representation is as:

2.5 Error vector Magnitude based spectrum monitoring technique

The receiver statistics are the function of channel and system operating conditions. In real-world situations, the receiver error is affected by things like "carrier-frequencyoffset" (CFO), "phase noise," "nonlinear impairments," "carrier leakage," "sampling frequency offset" (SFO), and "IQ imbalance and clipping." The "error-vector-magnitude" (EVM) gives the most important information about these problems, so it's important to use EVM to describe receiver data [70, 71]. The EVM overcomes the limitation of prerequisites of both the REC and energy-based approaches i.e. this technique does not require the reserved subcarrier and zero interval during data sequence transmission. The block diagram of the EVM-SNR estimator is presented in Figure 10. The EVM is defined on the bases of measured pilots from received (Rx) signal when compared with transmitted (reference) pilot signals. The "result-in-error-vector" ( $\vec{e})$ is the difference between the measured $(\vec{w})$ and reference symbol vector $(\vec{v})$ which means $\vec{e}=\vec{w}-\vec{v}$. Further, the EVM is defined as [70, 71].

$E V M_{r m s}=\sqrt{\frac{\frac{1}{N_i} \sum_{n=1}^{N_i}\left|S_r(n)-S_t(n)\right|^2}{P_0}}$ (5)

$E V M_{r m s} \approx \sqrt{\frac{N_0}{P_0}}=\sqrt{\frac{1}{S N R}}$ (6)

where, SNR and $N_0$ represents the signal-to-noise ratio and noise power spectral density (PSD), respectively. Furthermore, the decision on the emergence of $\mathrm{PU}$ is yielded by comparing $E V M_{r m s}$ with the threshold value of $E V M\left(\lambda_{E V M}\right)$. If the value is greater than $\lambda_{E V M}$, the emergence of $\mathrm{PU}$ confirmed otherwise $\mathrm{PU}$ is assumed to be absent.

Mathematical representation of the decision process is as follow:

$\left\{ {{EVM}_{rms}}\begin{matrix}{{H}_{1}} \\ \ge \\< \\{{H}_{0}} \\\end{matrix} \right.{{\lambda }_{EVM}}~~~~~~~~~~~~~~~~~~~~~~~\left\{ \begin{matrix}Decide~{{H}_{1}} \\Decide~{{H}_{0}} \\\end{matrix} \right. $ (7)

Figure 10. The process of spectrum monitoring technique using error vector magnitude (EVM)

2.6 Energy fluctuation based SM

The major limitations of the presented approaches (having good immunity level the channel variations) are the nongeneralized nature i.e. only defined for OFDM signal however, the REC+CRC based technique has immunity to the environmental, channel or operating system variations. Thus, a technique with good immunity to environmental variations and of generalized nature is required. Therefore, we have proposed energy fluctuation-based SM technique. In this technique, the power of received signal $r(t)$ is measured continuously over certain time intervals immediately after the spectrum sensing interval. It is assumed that the PU emerges after certain time as the data transmission starts therefore, the entire frame time $(\mathrm{T})$ is divided into number of intervals i.e. $t_1$ to $t_2, t_2$ to $t_3$, and so on. Initially, the energy of received signal is computed as follows. $E_{r 1}=\int_{t_1}^{t_2}|r(t)|^2=$ $\int_{t_1}^{t_2}|w(t)+C(t)|^2 d t$. Further, let us assume that the $\mathrm{PU}$ emerges and it interferes with the CU communication constructively or destructively. In case of constructive interference, the effective energy increases, however, destructive interference reduces the effective energy. Now, the energy of $r(t)$ in next interval is defined as $E_{r 2}=$ $\int_{t_2}^{t_3}|r(t)|^2 d t=\int_{t_2}^{t_3}|h . s(t)+w(t)+C(t)|^2 d t$. If we exclude the effect of noise the hypothesis is formulated as follows:

$\begin{aligned}&E_{r2}=\begin{cases}\int_{t_2}^{t_2}|h . s(t)+w(t)+C(t)|^2 d t & { Contructive\ interfernce } \\ \int_{t_1}^{t_2}|w(t)|^2 d t & { Destructive\ interfernce }\end{cases} \end{aligned}$ (8)

Moreover, the destructive interference may provide zero energy if both the waveforms i.e. PU and CU are same. Therefore, the modulus of difference between energies (DE) $E_{r 1}$ and $E_{r 2}$ is the decision variable i.e. $D E=\left|E_{r 1}-E_{r 2}\right|$. If the $\mathrm{DE}$ is greater than predefined threshold value $\left(\lambda_P\right)$, the emergence of $\mathrm{PU}$ is confirmed otherwise it is identified as absent. Mathematical representation of this is as follow:

In addition, to this it is possible that the channel condition may vary the energy level, however the energy levels after the constructive and destructive interference are extreme therefore, this approach is more suitable as compare to that of the energy ratio algorithm which is designed only for OFDM signal.

On the bases of architecture, the spectrum monitoring techniques are divided into two parts namely centralized spectrum monitoring and distributed spectrum monitoring which are further explained as follows.

3.1 Centralized spectrum monitoring

The centralized architectures for CRN are the ones where the communication of all the nodes/CU is controlled and managed by a centralized entity called as centralized controlling unit (CCU). Here, every CU has to exchange the information with the CCU and final decision about any process such as spectrum sensing, channel allocation, accessing etc. is taken by the CCU. Similarly, for the centralized SM, the CUs need to be controlled by a CCU however, in the SM, the SM is performed during the data transmission on particular channel. Therefore, generally, it is not possible to perform centralized SM, therefore, we have presented a scenario where a wideband channel is divided into number of narrowband channels and the CUs are permitted to exploit these channels for communication. Here, all the CUs perform SM on different narrowband channels however, the PU status remains same for these all channels. Thus, the centralized architecture can be implemented for the spectrum monitoring process. The key challenge with the centralized spectrum monitoring is that huge information needs to be exchange with the CCU which increases the complexity of the system. Moreover, if CCU crashes, the whole CRN gets down which is unavoidable in the centralized architecture. The solution for this issue is provided by the distributed architecture.

3.2 Distributed spectrum monitoring

In a distributed design, each CU does all of SM's tasks on its own, and the CCU and CUs don't need to share information with each other. This takes some of the extra work off of the CUs. In addition to this, the distributed design solves the major problem of CRN crashing when CCU fails. But the distributed design can also be broken down into CRNs that work together and ones that don't. In the distributed cooperative SM, the CUs share information about the appearance of PUs with their neighbours. However, only the particular CU makes the final choice. In the non-cooperative SM, on the other hand, each CU can only find PUs by studying the information at its own CU. This means that CUs don't share information with each other.

We have studied different SM techniques and concludes that it is very difficult to achieve perfect SM which means immediate detection of $\mathrm{PU}$ on its emergence since all the techniques has imperfect nature due to random channel, system errors etc. Because of this, it's important to look at the "effect of imperfections in the SM," and these mistakes are measured by the probability of SM error ($P \_$me). The system model is set up so that $P \_$me has a straight proportional relationship with the detection delay. This means that the larger the value of $P \_m e$, the longer it takes for PU to appear and be found. Also, the appearance of $\mathrm{PU}$ is a function of the traffic intensity of $\mathrm{PU}$( ), and a high value of means that $\mathrm{PU}$ will appear early in the time frame, while a low value means that it will appear later. In this part, we looked at how SM (perfect nature) affects data loss, power waste, and interference at $\mathrm{PU}$ compared to when $\mathrm{SM}$ is not present. Further, how the imperfections in the SM worsen the system performance is also studied.

(a)

(b)

(c)

Figure 11. Effect of spectrum monitoring on the (a) normalized data-loss versus traffic intensity (b) variations of power-wastage with traffic intensity and (c) SNR at PU versus time $\left(P_{m e}=0.5\right)$ [72]

Figure 12. SNR of PU versus time for different values of $P_{m e}$ [73]

Figure 13. The interference efficiency versus spectrum monitoring error [73]

Figure 14. The variations of energy efficiency with spectrum monitoring error [73]

4.3 Combined effect over CRNS’ performance of imperfect spectrum monitoring and spectrum prediction

In order to improve the performance of spectrum mobility which relies on the minimized detection delay, we have analyzed the combined effect of the spectrum prediction and monitoring techniques to detect the emergence of PU [36]. The AND and OR fusion rules are exploited to combine the effects of both and it is reported that AND rule outperforms regarding the resource wastage which results the improved achieved throughput. However, the OR rule outperforms in terms of data-loss and interference introduction at PU. Therefore, the selection of AND rule is preferred when the PU has high interference tolerable limit and deliberate introduction of “interference-at-PU” results the increase in the achieved throughput. On the other hand, when the PU protection is constrained by very low interference tolerable limit, the OR rule is better option.

4.4 Effect of cooperation among CUs over CRNs’ performance

The cooperation among users always improves the performance of wireless systems by tolerating the randomness of the environment/channel. In the recently proposed technique to enhance the performance of CRN [74-76], we have exploited the cooperation among CUs where a suitable scenario for the cooperation among $\mathrm{CU}$ is proposed and further have investigated the effect of cooperation on the data-loss, achieved throughput, interference efficiency and energy efficiency. In the proposed system, a wideband PU channel with bandwidth $\mathrm{B}$ is assumed to be selected for the data transmission by the CUs after the spectrum sensing. The $\mathrm{N}$ number of CUs are allowed to access the bandwidth $\mathrm{B}$ on the frequency sharing bases which means every CU communicates using bandwidth $\mathrm{B} / \mathrm{N}$. When $\mathrm{PU}$ resumes its communication during the CUs communication, then all the $\mathrm{CU}$ detect the emergence of $\mathrm{PU}$ with certain probability of $\mathrm{SM}$ error $\left(P_{m e}\right)$. Further, every CU reports its results to the central unit where the data from all CUs fused by using hard combining technique ( 1 out of $\mathrm{N}$ rule). Furthermore, if the combined decision confirms the emergence of $\mathrm{PU}$, then the CUs switch their communication on another idle channel otherwise continue the same channel. Using cooperation, the emergence of $\mathrm{PU}$ is detected with a probability-ofcooperative-SM-error" $\left(Q_{m e}\right)$ which is computed as follow [77, 78].

$Q_{m e}=\sum_{j=l}^N\left({ }_j^N C\right)\left(P_{m e}\right)^j\left(1-P_{m e}\right)^{N-j}$ (10)

where, C denotes the mathematical combination and if l or more number of CUs confirms the emergence of PU, the final decision is that the PU has emerged. Here, the role of l is very prominent and significant approach to compute the optimal value of l is illustrated by Banavathu and Khan [77]. In this article, it is concluding that the cooperation among CUs reduces the value of “probability-of-SM-error” which results the early detection of the PU and lessens the detection delay. The variations of the various performance metrics with the “probability of spectrum monitoring error” is depicted in the Figure 15, Figure 16 and Figure 17.

The relationship between data loss (measured in packets) and P_me is seen in Figure 15, where data loss varies exponentially for cooperative SM but virtually linearly for non-cooperative SM. Additionally, the cooperative SM outperforms the non-cooperative SM in terms of performance. According to Figure 16, which shows the fluctuations in interference-efficiency with P_me, cooperative SM has a higher interference efficiency than non-cooperative SM. The Figure 17 is the witness of superior performance of the cooperative SM over the non-cooperative SM. Thus, from the afore-discussion, it is perceived that cooperative SM is a prominent solution to manage the effects of imperfections in SM process.

Figure 15. The variations of data-loss with probability of SM error [74]

Figure 16. The relation between interference efficiency (bits/joule/Hz) and probability of SM error [74]

Figure 17. The relation between energy efficiency (bits/Joule/Hz) and probability of SM error [74]

5.2.2 Non-Generalized nature (NGN)

Both the energy and energy-ratio-based SM techniques (EBSMT) are defined when the CU transmits OFDM signals however, in general, the CU needs to support all other modulation techniques. Due to which the monitoring technique should be defined for all the frameworks used in communication which means the design of a generalized energy-based SM techniques is an open research issue.

5.2.3 Collision of PUs’ and CUs’ zero energy intervals (COPCZEI)

If the PU transmits OFDM signal it is possible that the zero energy intervals of signals transmitted by PU and CU coincide and the energy detection algorithm unable to detect the emergence of PU. Therefore, in order to avoid this issue, more research efforts are required. One potential solution is to exploits the pre-knowledge about the PUs’ signal which means if PU uses OFDM signal then CU needs to avoid energy-based SM technique.

5.2.4 Spectrum monitoring using multiple antenna techniques (SMUMA)

The EBSMT is also explored for the multiple antenna systems such as “single-input multi-output” (SIMO) and “multi-input multi-output” (MIMO). However, the multi-antenna system enhances the complexity of the EBSMT, therefore, the complexity management using multiple antenna systems for EBSMT is a challenging issue.

5.2.5 Threshold selection

The energy of the 1st and 2nd window may vary due to variation in the channel conditions which results the increase in energy ratio. Therefore, the selection of threshold value to take the decision is critical issue that need to be investigated. The dynamic threshold exploitation to decide the emergence of PU can be a suitable option as compare to the static threshold.

5.2.6 Security

Similar to the security section in 5.1, the malicious nodes in the network can transmit with high power on the same channel and results in the high energy level which leads to false detection the PU in energy-based SM. Moreover, the malicious nodes can vary its power level and may cause the energy difference at the CU receiver that leads to false-detection in case of energy-ratio-based SM. Therefore, secure detection mechanism in the energy and energy-ratio-based SM techniques need to explore.

5.3 EVM based spectrum monitoring

5.3.1 Non-Generalized nature (NGN)

The EVM technique is also defined for OFDM however due to same limitation mention in 5.2, it must be analyzed for all the modulation techniques.

5.3.2 Threshold selection (TS)

Due to the same reason as mentioned in section 5.1, the threshold selection is a challenging issue.

5.3.3 Security

Similar to the other SM techniques, malicious nodes and attackers alters the decision of SM phenomenon, therefore, sufficient security aspects need to be explored.