https://orcid.org/0000-0002-1016-2105
Figures
Abstract
Coverage and capacity are optimized in fifth generation (5G) networks by small base station (SBS) distribution in the coverage realm of macro base station (MBS). However, system performance is significantly reduced by inter-cell interference (ICI) because of the orthogonal frequency division multiple access assumption. In addition to ICI, this work considers intentional jammers’ interference (IJI) due to the presence of jammers. These Jammers try to inject undesirable energies into the legitimate communication band, which significantly degrade uplink (UL) signal-to-interference ratio (SIR). To reduce ICI and IJI, in this work, we employ SBS muting, where the SBSs near MBS are switched off. To further mitigate ICI and IJI, we use one of the effective interference management schemes a.k.a reverse frequency allocation (RFA). We presume that due to mitigation in ICI and IJI, the UL coverage performance of the proposed network model can be further improved.
Citation: Ghonaim SM, Khan S, Althobiani F, Alghaffari S, Khan S, Irfan M, et al. (2023) Interference mitigation in intentional jammers aided non-uniform heterogeneous cellular networks. PLoS ONE 18(6): e0287709. https://doi.org/10.1371/journal.pone.0287709
Editor: Khalid Taher Mohammed Al-Hussaini, Thamar University: Dhamar University, YEMEN
Received: April 6, 2023; Accepted: June 9, 2023; Published: June 28, 2023
Copyright: © 2023 Ghonaim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper.
Funding: This research work was funded by Institutional Fund Projects under grant no. (G:138-980-1443). Therefore, authors gratefully acknowledge the technical and financial support from the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia.
Competing interests: The authors have declared that no competing interests exist.
1 Introduction
1.1 Motivation
Heterogeneous cellular networks (HetNets) is a promising candidate technology for the future fifth generation (5G) networks [1–3]. The world wireless research forum predicts high speed connectivity for trillion of devices in the near future [4]. 5G networks can achieve capacity of 100 Gbps with improved battery life, higher coverage, and enhanced user accommodation [5, 6]. HetNets are made up of tiny, small base stations (SBS) coupled with high-power macro base stations (MBS) from a homogeneous cellular network [2, 7]. The deployment of such base stations (BSs) enhances network scalability [8, 9].
Intra-cell interference(ICI) is still the key limiting factor in HetNets despite the adoption of orthogonal frequency division multiple access (OFDMA), which results in minimal intra-cell interference [10, 11].
The functioning of the HetNet network can be negatively impacted by severe intentional jammers’ interference (IJI) caused by jammers’ presence [12–14]. The location of base stations transmit power, and other network parameters are all presumptively known to such jammers [13]. Therefore, by introducing undesired energy in the appropriate communication range, they can significantly degrade the uplink (UL) signal-to-interference ratio (SIR) [13]. Due to (i) decreased MBS-edge user (M-EU) power transmission in UL, (ii) greater M-EU user distances, and (iii) a higher path-loss exponent, IJI is effective in UL [14, 15].
SBS muting is taken into consideration in HetNets because I a user receives more coverage close to the MBS [5] and (ii) a higher MBS transmit power causes significant co-tier interference [16]. Due to less SBS deployment, SBS muting results in lower ICI and IJI, which enhances network coverage [17].
We refer to SBS muting by non-uniform HetNets (NUHs) and without SBS muting by uniform HetNets (UHs) in the remaining sections of the work. Different interference mitigation strategies, including reverse frequency allocation (RFA) [18], cell range extension (CRE)(CRE) [19], and fractional frequency reuse (FFR) [20], are used in the state-of-the-art. RFA is regarded as one of these plans’ proactive and effective interference mitigation strategies [18, 21].
Different key 5G technologies with their applications are presented in [22, 23]. Latest work on HetNets along with emerging technologies, such as (i) non-orthogonal multiple access (NOMA), (ii) massive multiple input multiple outputs (massive MIMO), and (iii) millimeter wave can be found in [24–26].
The works in [27, 28] evaluate both intra-cell interference and ICI in 5G networks. The authors used inter-cell interference coordination (ICIC) technique to mitigate the interference. Through results, it is shown that ICIC leads to improved network performance results. Similarly, in [29], the authors investigate the inter-block interference (IBI) and ICI in HetNets. They propose a novel precoding scheme to reduce ICI and IJI in HetNets.
Their proposed model leads to significant performance superiority due to lower IJI and ICI. The above-mentioned work, however, lacks to investigate both SBS muting and RFA scheme.
The work in [30] study the security aspects of 5G networks focusing on various types of attacks and security services. Moreover, security concerns are evaluated for different 5G technologies, such as software-defined networks, device-to-device communications, heterogeneous networks, massive MIMO, and the Internet of things. Moreover, attacks on 5G networks including traffic analysis, eavesdropping, denial of service, distributed denial of service, and jamming are investigated. The study in [31], provides an in-depth analysis of different jamming and anti-jamming techniques in 5G networks. Similarly, the works in [32] investigate the spoofing and jamming of the physical downlink and UL control channels and signals in 5G networks. Moreover, they employ various jamming methods to evaluate network immunity against jamming. They conclude that effective measures are needed to mitigate jamming in 5G networks. In contrast to our work, [30–32] lacks the employment of RFA and SBS muting to reduce ICI and IJI.
In [33], the authors employ NOMA enabled NUH, where SBSs are distributed with different densities in various regions. Through results, they demonstrate that NOMA-enabled NUH outperforms all other scenarios in terms of energy efficiency. Similarly, the works in [34, 35] explore the employment of NUHs. Their results indicate significant performance improvement due to lower interference achieved by SBS muting in HetNets. However, the latest work of [33–35] lacks to analyze IJI in HetNets.
The latest work on RFA employment can be found in [17, 21, 36], where RFA scheme leads to better coverage and rate due to effective mitigation of interference. However, they lack to investigate both NUH and intentional jammers in HetNets.
In this work, we look into HetNets’ performance in terms of coverage when there is IJI and ICI. To alleviate the impact of ICI and IJI we use SBS muting as well as RFA as a preventive measure for interference mitigation.
1.2 Approach and contributions
The paper uses two layers of BS, known as MBSs and SBSs, to illustrate a model based on HetNets. IJI attacks frequently result in additional UL intersections in addition to the typical ICI. Thus, the system as a whole is considered degenerative. In Fig 1A and 1B. the network models are displayed. The MBS service area is divided into two sections: the inner zone, designated A1, and the edge region, designated A2, with radii Δs1 and Δs2, accordingly [37, 38].
A: Uniform HetNets. B: Non-uniform HetNets. C: Legends.
The significance of this work from the state-of-the-art can be summarized as follow.
- The work in [27–29] evaluates both intra-cell interference and ICI in 5G networks. However, they lack to investigate IJI.
- In contrast to our work, [30–32] lacks the employment of RFA and SBS muting to reduce ICI and IJI.
- The latest work of [33–35] evaluates NUHs but lacks to analyse IJI in HetNets.
- The latest work on RFA employment can be found in [17, 21, 36]. However, they lack to investigate both NUH and IJI in HetNets.
The following are this paper’s significant contributions.
- Analysis of the UL coverage for the typical user which is defined as Slivnyak theorem states that the statistical characteristics of an independent homogeneous Poission Point Process (IHPPP) are preserved and simplified by a typical user at origin [39, 40].,
, in A2 when IJI and ICI are present.
- This study examines how proactive interference control strategies can reduce IJI and ICI. RFA and the use of NUHs, a smart network design technique.
- For (i) UHs with RFA employment (see (10)) and (ii) NUHs with RFA employment (see (11)), we develop coverage probability expressions.
- The outcomes are presented for various network characteristics, including SIR threshold, jammers’ density and transmitted power, users’ transmit power and density of SBS.
1.3 Paper organization
The remainder of the paper is laid out as follows. The system model is presented in Section 2. The suggested model’s coverage probabilities are calculated in Section 3. Section 4 contains the results and commentary. The paper is finished in Section 5. Table 1 showcases a qualitative tabulation of various references that carried work based on our proposed work that needs improvements while, Table 2 contains an index of the notations made in the article. And finally, Table 3 has the system parameters defined.
2 System model
This section presents the suggested network design as shown in Fig 1A and 1B. Due to multi-tier BSs deployment and the existence of intentional jammers the network performance degrades severely due to ICI and IJI. UL communication of M-EUs in HetNets are susceptible to IJI and ICI because of lower UL transmit power and longer transmission distances between MBS and M-EUs. Moreover, we incorporate RFA in NUH with non uniform BSs deployment to mitigate both ICI and IJI and thus, enhance UL performance of M-EUs. Preliminary mathematical results obtained in this section are used for coverage probability assessment in Section 3.
2.1 Network layoutS
In this paper, we consider two-tier HetNet comprising of co-deployed SBS’s with MBS’s. We suppose that there exist intentional jammers throughout the network which degrade the desired communication link. MBS’s, SBS’s, users, and jammers are distributed via IHPPPs ϕM, ϕS, ϕu, and ϕJ, respectively [17]. The density of MBSs, SBSs, users, and jammers is ζM, ζS, ζu, and ζJ, respectively. The proposed network models are presented in Fig 1A and 1B [17, 18, 33]. We assume that the UL communication of M-EUs are stressed by IJI and ICI. This work assumes NUHs with RFA in contrast to UHs with RFA to reduce ICI and IJI. Moreover, we investigate the UL coverage performance of located in A2. The path loss exponent is denoted by α [41, 42]. The Rayleigh fading gain, i.e., |h|2 ∼ exp(1) [17, 40] is represented by |h|. For RFA and NUH employment, we divide the MBS coverage region in to A1 and A2 with radii Δs1 and Δs2, respectively [37, 43].
2.2 Jamming mechanism
Jammers are considered to transmits unwanted energy across the entire spectrum of the communication system to reduce network performance [37, 38, 44]. This work assumes that the jammers are located uniformly in the coverage vicinity of MBS which are distributed according to IHPPP [35, 45]. The UL communications of M-EUs in HetNet is significantly degraded by ICI and IJI [39]. Due to power constraints, jammers in lower density or located at far distance merely cause any harm to the communication system [15]. Therefore, such low power jammers to be effective, they must be well tuned and need to be located near the target [45, 46]. Moreover, in worst case scenario, jammers block the UL communication in HetNets and, thus, cause the distributed denial of service (DDoS) attacks [39, 45].
2.3 Reverse frequency allocation
Due to efficient interference mitigation, RFA-based resource partitioning significantly improves coverage [39]. By using RFA, the entire spectrum is left open for an SBS to use in the opposite direction and in non-overlapping regions [18, 39]. Various sub-bands are used interchangeably among SBSs and MBSs while following RFA as ∀ g ∈ (1, 2) and l ∈ (M, S) used alternatively. Fig 2 showcases this scenario. M stands for MBS, while S stands for SBS.
In-accordance with RFA, total alloted frequency band, F, is further divided into sub-bands with different frequencies, i.e., F1 and F2, such that F = ⋃z∈(1,2) Fz, as shown in Fig 2. Whereas, these sub-bands F1 and F2 of MBS is used for UL and DL communication in outer area macro cell () and inner area of macro cell (
), respectively. For the UL and DL communication, these sub-bands are further split into UL and DL sub-carriers which are modeled as F1 = F1,UL+ F1,DL and F2 = F2,UL+ F2,DL, respectively. Similar to F1 and F2, as sub-band frequencies of MBS, the sub-bands for SBSs are
and
, respectively, which are reversely used in the corresponding regions reciprocally, i.e., outer region of SBS,
, and center region of SBS,
, respectively. These sub-bands of SBSs i.e.,
and
, are further cut-up into sub-carriers of UL and DL denoted respectively as
and
for notation clarity.
3 Coverage probability
This section focus on the assessment of coverage probability in the proposed network scenarios where ν is assumed to be located in A2 and in A1; (i) uplink coverage probability for uniform HetNets (UHs’) is presented in Subsection 3.1 while (ii) the uplink coverage probability in case of non-uniform HetNets (NUHs’) is derived in Subsection 3.2.
3.1 Uplink coverage probability for uniform HetNets (UHs)
The UL coverage probability when there are intentional jammers (IJs) and RFA, , while considering ν in A2 can be obtained as:
(1)
Following the architecture of RFA, the total interference in UL is the addition of the UL interference from MBSs in A2, i.e., , the DL interference from SBSs in A1, i.e.,
, and the interference from IJs, i.e., IJ,A. Therefore,
from (1) can be written as:
(2)
Eq (2) can be expanded as:
(3)
In (3), is the ν UL transmission power connected with MBS,
is the transmission power of SBS, and Pt,j is the emitting power of jammers. Moreover, substituting (2) into (1), we obtain
as:
(4)
Here, Step (1) follows from the coverage probability definition [17, 40]. Step (2) follows from Step (1) by using the void property of IHPPPs [40]. Similarly, Step (3) is obtained by replacing by s, where
. In addition, Stage (4) is obtained by the use of the exponential property of additions in products i.e., exp(a + b) = exp(a) × exp(b).
The Laplace transform (LT) of interference in UL from MBSs in A2, i.e., , is obtained as:
(5)
Here, Step (a) follows the definition of LT [40], Step (b) is achieved by substituting , into Step (a), Step (c) is achieved by replacing s, s.t.,
, into Step (b), Step (e) is followed by evaluating the LT of Step (d) with respect to hj, Step (f), is followed by considering probability generating functional (PGFL) of IHPPP [47], Step (g) is achieved by replacing
into Step (f), and Step (h) is achieved from Gauss-hypergeometric approximation of Step (g) [47].
Similarly, the LT of the total UL interference received from the MBSs in A1, i.e., , is obtained as:
(6)
In addition, the LT of the DL interference from SBSs in A1, i.e., , can be written in a similar way as far (5), and is given as:
(7)
(8) η2 is the ratio of
and
where
is the DL transmit power of SBSs.
The UL coverage probability, , in the presence of ICI, IJI, and RFA employment while considering ν in A2 can be written as [17]
(9)
(10)
(11)
By substituting (6), (7), and (8) into (9), is expressed as (10).
3.2 Uplink coverage probability for non-uniform HetNets (NUHs’)
Non-uniform heterogeneous network deployment is established where SBS in A1 is muted and user in that vicinity is in coverage with MBS. The UL coverage probability, , while assuming IJs, RFA, and ν in A2 can be written as
(12)
By substituting (6) and (8) into (12), is expressed as (11). In (10) and (11),
indicates the Gauss-hypergeometric function.
4 Results and discussion
This section describes results for the user’s UL coverage probability while taking into consideration: (i) UL coverage probability of UH and (ii) UL coverage probability of NUH. MATLAB 2015a has been used in drawing our results. MBS, SBS, jammers and users are dispersed in A = π(500m)2, s.t., A = A1UA2. Transmitted power by MBS, SBS, jammers, and users are supposed to be 40 dBm, 30 dBm, 20 dBm, and 20 dBm, respectively. Various network parameters such as ζM, ζS, ζj, ΓM, Pt,J and are assumed for analyzing UL coverage when the user is located in A2.
Fig 3 compares UL coverage probability for different values of ΓM in A2. This figure assumes ζj = 0 and 100, for both UH and NUH network scenarios. This figure indicates that the simulation results will coincide with the numerical results both for UH and NUH. The plots in the figure further demonstrate that NUHs with ζj = 0 lead to the highest coverage gain as compared with the rest of the scenarios. This is due to improved interference mitigation by NUHs as a result of lower SBS deployment.
In Fig 4, we demonstrate UL coverage probability against different values of ΓM for both UH and NUH in A2. This figure is obtained for ζj = 0 and 100 and ζS/ζM = 30. This result demonstrates that NUH with RFA outperforms the other scenarios due to significant interference mitigation. At ΓM = −10dB, the proposed NUH with RFA and ζj = 0 leads to 20% UL coverage gain.
In Fig 5A and 5B, we compare UL coverage probability against different values of ΓM for UH and NUH, respectively. The plots in both the figures are obtained for ζj = 0, 100, 200, 300, 400, 500. Moreover, the results indicate that a sufficient number of IJs in the network are needed to significantly degrade UL coverage because of the wideband nature and low transmission power of IJs. Furthermore, increasing the value ζj leads to lower UL coverage in both UH and NUH due to higher interference. The results indicate significant coverage performance improvements by RFA and NUH due to effective interference mitigation.
A: UH B: NUH.
In Fig 6A and 6B, we evaluate UL coverage probability for different values of ζj, while considering RFA, UH, and NUH. The plots are obtained for ΓM = 0 dB, −5 dB, −10 dB, −15 dB, −20 dB, −25 dB and ζS/ζM = 30. The plots indicate that higher values of ΓM lead to lower coverage due to lower user association. Furthermore, the plots in both figures indicate that NUH gives rise to higher coverage in contrast to UH. By employing RFA, the network performance improves in both cases but due to less interference the coverage in NUH is better than UH.
A: UH B: NUH.
Similarly, Fig 7A and 7B demonstrate UL coverage performance against IJs distribution area for different values of ζj and ζS/ζM = 10.
A: UH B: NUH.
Both of these figures indicate that increasing IJs distribution area leads to improved coverage as the IJs become less effective. The figures further depict that at an area of 1 km2, NUH with RFA leads to 19% UL coverage improvement due to significant interference reduction.
Finally, Fig 8A and 8B show UL coverage probability against IJs distribution area for different values of ΓM. These figures consider ζj = 100 and ζS/ζM = 10. The results indicate that increase in the values of ΓM gives rise to lower coverage due to lower user connection. The figures also indicate that NUH with RFA and ΓM = -50 dB give rise to the highest coverage gain in contrast to the rest of the scenarios.
A: UH B: NUH.
5 Conclusion
This work aims to reduce ICI and IJI by employing SBS muting and RFA in HetNets. Various network parameters such as jammer’s density, jammers transmit power and their distribution area, SIR threshold are investigated against user coverage. The results are obtained for both UHs and NUHs in addition to and without RFA. The results depict that NUHs employing RFA outperform other scenarios in terms of UL coverage. Moreover, the investigation indicates 20% UL coverage improvement at ΓM = −10 dB while using RFA and NUHs as compared with RFA and UHs. This work can be extended to evaluate drone-based jammers in HetNets.
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