Recently, considerable attention has been devoted to the potential of the femtocell as a solution to poor indoor network coverage. It has been exploited to provide higher capacity and intelligent coverage with an improvement in quality of service (QoS) for future indoor services. Though the advantages of femtocells cannot be overemphasized, they introduce some challenges, mostly with respect to the possible inherent interference due to a co‐channel deployment in two‐tier architecture with the Macrocell Base Station (MBS) and User Equipments (UEs). In this chapter, the femtocell is analysed as a solution for indoor network coverage problems and local‐convergence demands for indoor network applications. The chapter also focuses on the conventional methods/solutions used today to mitigate interference, which comprises spectrum allocation, power control and antenna approaches. These solutions can be combined with cognitive radio (CR) to introduce a higher degree of interference awareness with respect to the dynamic changes in the wireless environment.

2.1 Introduction

There will be a continuous increase in the demand for wireless spectrum in the foreseeable future with the introduction of Internet multimedia applications such as Facebook, YouTube and multimedia networks to name a few. As a result, there is even more of an increase in demand for the spectrum compared with technological development, which aims at increasing spectrum efficiency [1]. However, the main concern mostly in an indoor environment is the poor indoor network coverage as opposed to the increased demand of real‐time applications [2]. For these reasons, femtocells have gained attention to secure spectrum efficiency in near‐future networks. One of the important benefits of femtocells is the elimination of coverage‐area problems for indoor scenarios. Some other benefits of femtocells include increased average revenue per user, reduced capital expenditure (CAPEX) and operational expenditure (OPEX), deployment in operator‐owned spectrum and reduced bandwidth load and power requirements [3].

However, one of the main problems is due to the inefficient policies in spectrum management rather than the real‐time application on the available bandwidth [4]. Femtocells are typically designed to support simultaneous spectrum access of up to four mobile UEs in residential or small indoor environments. Predictions show that, in the near future, about 60% of voice traffic and about 90% of data traffic will originate from indoor environments such as home, office, airport and school [5]. Due to the limitations in spectrum availability, network operators prefer to deploy femtocells in a co‐channel access mode with the MBS, which means same resources are shared simultaneously [6]. However, since it is relatively expensive for network operators to perform careful cell planning and optimization in femtocells, they are user deployed by subscribers, which introduces an inherent interference with the MBS. This is because the femtocell uses the same spectrum as the MBS it is currently sharing with. Since the importance of femtocells cannot be overemphasized, there is a need to introduce interference mitigation schemes to enable femtocells to co‐exist alongside macrocells, as the former complements the latter in today’s heterogeneous networks.

This chapter concentrates on elaborating the importance of femtocells in a helpful or harmful perspective. The chapter highlights possible interference scenarios in a two‐tiered network with macrocells as well as probable solutions for mitigating interference.

2.1.1 Cross‐Tier Interference

Cellular networks require dedicated spectrum to support high data rates. However, the current radio spectrum is very crowded and only leaves very limited space for future evolutions, which results in a compact arrangement of frequency bands between 4G/5G releases and other wireless systems. For instance, the 2400–2483.5 MHz ISM band is utilized by both Wireless Fidelity (Wi‐Fi) and Bluetooth, while the operating band of the LTE‐A Band 40 ranges 2300–2400 MHz. Due to the imperfect transceiver components, the compact arrangement of adjacent frequency bands introduces severe cross‐tier interference, not only among wireless stations but also within a device with multiple radios such as femtocells, which causes immense interference to Macrocell User Equipments (MUEs) within its vicinity as depicted as I₃ in Figure 2.1. In [7], even usage of state‐of‐the‐art radio frequency filters fails to provide mitigation to interference especially in adjacent channels.

2.1.3 Downlink Interference Modelling

In downlink, where the users suffer from interference, it can be said that a certain user UEx, whose connected server (best server) is C_i, suffers from the interference of cell C_j, based on the following condition; If C_i and C_j are using the same sub‐channel for downlink transmission at the same OFDM symbol [10]. It is important to note that UE_x could be a Femtocell User Equipment (FUE) or MUE and C_i could be a FAP or MBS. Therefore, the total interference suffered in Downlink (DL) by UE_x at slot slot_i,k,t is the summation of the interferences coming from all neighbouring cells C_j.

(2.1)

where, x is the interfered UE, UE_x; k is the kth sub‐channel and t is the tth symbol; i is the best server, C_i,j is an interfering cell, C_j; P_j,k is the transmit power of cell C_j in a SC of the kth sub‐channel; Lp_j,x is the channel gain or path loss (PL) between C_j and UE_x; G_j and G_xs are the antenna gains in Cj and UE_x. Also, L_j and L_x stands for the equipment losses in cells, C_j and UE_x.

2.1.4 Uplink Interference Modelling

On the other hand, in UL, interference is suffered by the cells (MBS or FAPs in our scenario). The conditions are that if a certain cell C_i, serving user is UE_x, suffers from the interference of another UE UE_y, if UE_x and UE_y are using the same sub‐channel for UL transmission at same OFDM symbol. Therefore, the total interference suffered in UL by cell C_i at slot slot_i,k,t will be the summation of all the interferences emanating from all neighbouring UEs, UE_y.

(2.2)

where, i indicates cell suffering from interference, C_i; k is the kth sub‐channel and t is the tth symbol; x is the user being served, UE_x; y is the user causing interference, UE_y; P_y,k is the applied transmit power of UE_x in a subcarrier of the kth sub‐channel; Lp_y,i is the PL between user UE_y and cell C_i; G_y and G_i stand for the antenna gains for UE_y and C_i, respectively while L_y and L_i stand for the equipment losses in UE_y and C_i. Shadowing effects and multi‐path fading should be taken into account; computing L_p. L_p can be deduced as:

(2.3)

Where L_att is the attenuation, L_s is the shadow fading and L_ff is multi‐path fading. Therefore, the SINR of each slot, slot_i,k,t can be expressed as follows:

(2.4)

where, C is the received power of the carrier and I the interfering signals. σ denotes the background noise. The received signal power C can expressed as:

(2.5)

(2.6)

The background noise, σ, on the other hand, can be deduced by:

(2.7)

(2.8)

where, n_o is noise, and nf_eq for the noise figure of the UE. Also, F_sam represents the sampling frequency, while SC_used and SC_total are the number of used and total sub‐carriers, respectively.

Once the SINR of all slots allocated to a user are known, the effective SINR of the user is computed using the Mutual Information based Exponential SNR Mapping (MIESM) average [11].

2.2 Platform for Femtocell Deployment

In order to enable femtocells operate within a variety of networks a standard femtocell network architecture is required. This architecture enables a diversity of femtocells from different manufacturers to work in the networks of different operators. This section will cover the physical‐layer details of Long Term Evolution (LTE), which comprises time slot structures and available data rates. The current evolutionary step in 3GPP roadmap for future wireless cellular systems was introduced in 3GPP Release 8 in December 2008 with minor improvements in Release 9 and Release 10, respectively [12].

This release is commonly known as the LTE and it introduces enhancements to previous specifications to achieve higher throughput, spectral bandwidth and more flexible spectrum management. The requirements for high peak transmission rates are 100 Mbps for downlink and 50 Mbps for uplink. The LTE specifications introduce a wide range of support for femtocells. The data rates achieved by LTE are higher than those provided by most network interfaces, which increases the advantages of femtocells based on this release. An overview of the main transmission schemes of the LTE radio interface is provided in the subsequent section.

2.3 LTE Architecture Overview

The radio frame of LTE is defined as having a length of 10 ms. This is divided equally into 10 subframes (SF) of duration per SF. Each SF is further divided into slots of length . Each SF contains or OFDM symbols on the length of the selected cyclic prefix (CP). An extended CP of 16.7 µs is allowed in LTE, which might be suitable in accommodating delay. However, in femtocells a normal length CP (T_CP = 5.2 µs) might be enough due to its limited coverage area and short delay periods as compared with a MBS. More information about the frame structures can be found in [13].

2.4 LTE Femtocell Interference Analysis

In this section, we provide a brief analysis of the advantages and disadvantages of femtocells in a two‐tier interference scenario. Consider an OFDMA system where the femtocell and macrocell are deployed in a co‐channel fashion. A tri‐sector MBS is in the centre of the network and serves the randomly distributed 90 MUEs within its coverage area. Since there is more activity of MUEs in the downlink (DL) and less activity in the UL, the analysis is conducted on downlink interference. Sixty femtocells are randomly located within the coverage area of the MBS and accordingly there are number of MUEs within the coverage area of the femtocells. The simulation parameters are based on 3GPP LTE specifications [15], whereas four FUEs are attached to each FAP. The performance key indicator (PKI) used in the following analysis is SINR.

2.4.1 Scenario 1: Cross‐Tier Interference Analysis

1. Detriment of femtocells – Figure 2.2a demonstrates the detrimental effects of femtocells. In a co‐channel deployment, MUEs served by the MBS are interfered by the closely located FAPs, thereby resulting into reduced SINR (grey shaded curve). Whereas in the absence of femtocells, (i.e. FAPs), the SINR improves around 20 dB (black curve).
2. Importance of femtocells – In this analysis, the importance of femtocells in a network is demonstrated with the help of Figure 2.2b. This plot compares the output from a scenario where firstly all the UEs are located indoor and served by the respective MBS, thereby resulting in a seriously degraded SINR (grey curve). Later on, the same UEs become part of an FAP (i.e. FUEs) and subsequently are served by the respective femtocell access point, thereby improving the SINR (on average 11 dB, black curve).

2.4.2 Scenario 2: Effects of Femtocell Access Mode Deployment

The possible access modes of femtocells could be either an open or closed subscriber group (i.e. Open Subscriber Group ‐ Open Access (OSG) and Close Subscriber Group ‐ Close Access (CSG), respectively). Accordingly, the deployment mode can affect the SINR values of nearby MUEs as shown in Figure 2.3. In CSG, the MUEs SINR suffer more degradation, that is, of around 8 dB compared to OSG due to the fact that OSG allows admittance of MUEs (to become FUEs) as opposed to CSG, which inhibits admittance thereby resulting into reduced SINR value.

2.4.3 Scenario 3: Co‐Tier Interference Analysis

Figure 2.4 is a plot of two femtocell scenarios to include a standalone femtocell (STA FAP) and two collocated femtocells (COL FAP). SINR values show that the FUEs in standalone femtocells can reach average values of 29 dB, whereas in a collocated scenario FUEs can suffer an average SINR loss of 17 dB due to co‐tier interference component.

2.4.4 Scenario 4: Effects of Varying FAP Transmit Power Levels on MUEs

It is fair to determine that an adaptive power level for FAPs presents a more viable solution to interference management than a fixed power control scheme. Figure 2.5 compares the plots for three power levels (0, 10 and 20 dBm) in terms of their effect on close by MUEs in a CSG deployed femtocell. At 20 and 10 dBm, MUEs attain average SINR values of −2 and 3 dB, respectively; however, an improved average SINR of 5 dB is experienced by MUEs when FAP transmit power is 0 dBm.

2.5 Interference Mitigation: Current State of the Art

The interference scenarios mentioned previously require interference mitigation schemes to curb the mutual interference in a heterogeneous network. Next are some of the conventional methods implemented in research today to mitigate interference.

2.6 Cognitive Femtocells: A Smart Solution to a Complex Problem

CR is the ability of a radio frequency (RF) to sense its surroundings and modify its features such as frequency, modulation, power and other operating parameters automatically to dynamically reuse spectrum [16]. It has been considered to be a technology capable of spectrum sensing, management and mobility in an opportunistic manner. Through spectrum sensing and management, it can detect an available and unused spectrum also known as white space or spectrum hole, vacated by a user known as the licensed or primary user (PU) and allocate to an unlicensed secondary user (SU).

Cognitive Femtocells (CF) are the integration of CR in femtocells that signifies a femtocell with CR capabilities [17]. Hence, CR enabled femtocells with the spectrum sensing capability of a CR can avoid interference (co‐tier and cross‐tier) by sensing the spectrum and assigning resources on a different spectrum to avoid interference. Also, the self optimization capabilities of CFs allow the radio environment to be sensed in a dispersed manner so as not to interfere with operational parameters. This entails a SU opportunistically occupying them in the absence of a PU and also vacating them for another possibly available white space as soon as a PU reclaims it. An accurate and efficient spectrum sensing technique is mostly influenced by two key metrics, sensing speed and accuracy.

The effect of CR on conventional interference mitigation schemes offers the advantages of CR with regards to spectrum management in a coordinated and interference void manner. The CFs have the ability to operate as normal femtocell but can also use an opportunistic spectrum access when a user requires higher QoS for certain services. As mentioned earlier, one of the conventional ways to avoid interference in a two‐tier architecture will be in a dedicated approach where each UE is assigned a dedicated spectrum but that establishes a trade‐off between interference and available spectrum. In this regard, a co‐channel access is preferred at the detriment of interference. Unlike in conventional schemes that are restricted to licensed band only, CFs are able to allocate both licensed and unlicensed spectrum bands to UEs. As mentioned earlier, the random user deployment of FAPs is also a major problem as coordination between FAPs and MBSs is required to mitigate interference in most cases. Conventional schemes mostly employ a direct coordination between FAPs and MBS utilizing the backhaul that introduces overhead and delay as the cost.

In summary, to mitigate interference, the self optimization capabilities of CFs allow the radio environment to be sensed in a distributed manner to retrieve operating parameters. However, the operating parameters and features retrieved is usually specific to the radio technology but offers a wide range of options to include white spaces, SINR, Received Signal Strength Indicator (RSSI), noise, transmit power, channel statistics (channel gain and PL) and so on. By continuous sensing of the parameters, current as well as future interfering sources/signals can be deduced.

Although CR enabled schemes can be viable solution to effectively mitigate interference, there are various lessons that are to be learnt (Table 2.1) to identify the cost of using CR in femtocells.

2.7 Summary

This chapter outlined the importance of deploying femtocells as a solution to poor indoor network coverage and introduced 4G as a suitable platform for deployment. However, the advantages of femtocell can be overshadowed with interference when deployed in a co‐channel fashion with the MBS. The inherent interference scenarios in the uplink and downlink have been highlighted and a review into current avoidance or mitigation approaches mentioned. The question of whether femtocells are helpful or harmful in a co‐channel deployment heavily depends on the effectiveness of an interference mitigating scheme. This chapter highlighted CR as a promising and smart solution that can be applied to conventional schemes to produce interference‐aware schemes where present and future interfering sources can be detected and prevented. An ideal interference mitigation scheme for femtocells in a co‐channel deployment can involve a CR where hybrid schemes are incorporated to complement each other, while keeping it easier to address both cross and co‐tier interference.

References

1. 1 Z. Du, Q. Wu, P. Yang, Y. Xu, J. Wang and Y‐D. Yao, “Exploiting user demand diversity in heterogeneous wireless networks,” in Wireless Communications, IEEE Transactions on, vol. 14, no. 8, Aug. 2015, pp. 4142–4155.
2. 2 V. Chandrasekhar and J. Andrews, “Femtocell networks: A survey” IEEE Commun. Mag., vol. 46, no. 9, Sept. 2008, pp. 59–67.
3. 3 H. O. Kpojime and G. A. Safdar, “Interference mitigation in cognitive radio based femtocells”. IEEE Communications Surveys & Tutorials, vol. 17, no. 3, Aug. 2015, pp. 1511–1534.
4. 4 P. Demestichas, A. Saatsakis, and W. Koening, “An approach for realizing future Internet with cognitive technologies,” in Proc. 4th Int. Conf. Cognitive Radio‐Oriented Wireless Networks and Communications, Hannover, Germany, 2009, pp. 1–6.
5. 5 M. Chowdhury, Y. Jang, and Z. Haas, “Network evolution and QoS provisioning for integrated femtocell/macrocell networks,” Int. J. Wireless Mobile Networks, vol. 2, no. 3, 2010, pp. 1–16.
6. 6 T. Zahir, K. Arshad, A. Nakata and K. Moessner, “Interference management in femtocells,” Communications Surveys & Tutorials, IEEE, vol. 15, no. 1, February 2013, pp. 293–311.
7. 7 CMCC, “RP‐100671: Signalling and Procedure for In‐ Device Coexistence Interference Avoidance,” 3GPP TSG RAN Meeting 48, June 2010.
8. 8 S. Lien, K‐C. Chen, Y‐C. Liang and Y. Lin, “Cognitive radio resource management for future cellular networks,” in IEEE Wireless Communications, vol. 21, no. 1, Feb. 2014 pp. 70–79.
9. 9 M. Danneberg, R. Datta, A. Festag and G. Fettweis, “Experimental testbed for 5G cognitive radio access in 4G LTE cellular systems,” in Sensor Array and Multichannel Signal Processing Workshop (SAM), 2014 IEEE 8th, 22–25 June 2014, pp. 321–324.
10. 10 J. Kim, A. Ashikhmin, A. van Wijngaarden, E. Soljanin and N. Gopalakrishnan, “On efficient link error prediction based on convex metrics”, in Proc. IEEE 60th Vehicular Technology Conference (VTC2004‐Fall), Sept. 2004, pp. 4190–4194.
11. 11 L. Wan, S. Tsai and M. Almgren, “A fading‐insensitive performance metric for a unified link quality model”, in Proc. IEEE Wireless Communications and Networking Conference (WCNC2006. Apr. 2006, pp. 2110–2114.
12. 12 Technical Specification Group RAN, E‐UTRA; LTE physical layer – general description. 3GPP, Tech. Rep. TS 36.201 Version 8.3.0, March 2009.
13. 13 A. Valcarce, G. De La Roche, A. Juttner, D. Lopez‐Perez, and J. Zhang, “Applying FDTD to the coverage prediction of WiMAX femtocells”, EURASIP Journal on Wireless Communications and Networking, Feb. 2009.
14. 14 “Radio Resource Management”, Aricent White Paper, January 2008.
15. 15 3GPP, “Simulation assumptions and parameters for FDD HeNB RF requirements,” R4–092042, TSG‐RAN WG4, Meeting 51, 2009.
16. 16 J. Mitola III and G. Q. Maguire Jr., “Cognitive radio: making software radios more personal,” IEEE Personal Communications, vol. 6, no. 4, 1999, pp. 13–18.
17. 17 G. Gur, S. Bayhan and F. Alagoz, “Cognitive femtocell networks: an overlay architecture for localized dynamic spectrum access [dynamic spectrum management],” IEEE Wireless Communications, vol. 17, no. 4, August 2010, pp. 62–70.