Best Signal Quality in Cellular Networks: Asymptotic Properties and Applications to Mobility Management in Small Cell Networks
© Van Minh Nguyen et al. 2010
Received: 15 October 2009
Accepted: 21 March 2010
Published: 3 May 2010
The quickly increasing data traffic and the user demand for a full coverage of mobile services anywhere and anytime are leading mobile networking into a future of small cell networks. However, due to the high-density and randomness of small cell networks, there are several technical challenges. In this paper, we investigate two critical issues: best signal quality and mobility management. Under the assumptions that base stations are uniformly distributed in a ring-shaped region and that shadowings are lognormal, independent, and identically distributed, we prove that when the number of sites in the ring tends to infinity, then (i) the maximum signal strength received at the center of the ring tends in distribution to a Gumbel distribution when properly renormalized, and (ii) it is asymptotically independent of the interference. Using these properties, we derive the distribution of the best signal quality. Furthermore, an optimized random cell scanning scheme is proposed, based on the evaluation of the optimal number of sites to be scanned for maximizing the user data throughput.
Mobile cellular networks were initially designed for voice service. Nowadays, broadband multimedia services (e.g., video streaming) and data communications have been introduced into mobile wireless networks. These new applications have led to increasing traffic demand. To enhance network capacity and satisfy user demand of broadband services, it is known that reducing the cell size is one of the most effective approaches [1–4] to improve the spatial reuse of radio resources.
Besides, from the viewpoint of end users, full coverage is particularly desirable. Although today's macro- and micro-cellular systems have provided high service coverage, 100%-coverage is not yet reached because operators often have many constraints when installing large base stations and antennas. This generally results in potential coverage holes and dead zones. A promising architecture to cope with this problem is that of small cell networks [4, 5]. A small cell only needs lightweight antennas. It helps to replace bulky roof top base stations by small boxes set on building facade, on public furniture or indoor. Small cells can even be installed by end users (e.g., femtocells). All these greatly enhance network capacity and facilitate network deployment. Pervasive small cell networks have a great potential. For example, Willcom has deployed small cell systems in Japan , and Vodafone has recently launched home 3G femtocell networks in the UK .
In principle, high-density and randomness are the two basic characteristics of small cell networks. First, reducing cell size to increase the spatial reuse for supporting dense traffic will induce a large number of cells in the same geographical area. Secondly, end users can set up small cells by their own means . This makes small cell locations and coverage areas more random and unpredictable than traditional mobile cellular networks. The above characteristics have introduced technical challenges that require new studies beyond those for macro- and micro-cellular networks. The main issues concern spectrum sharing and interference mitigation, mobility management, capacity analysis, and network self-organization [3, 4]. Among these, the signal quality, for example, in terms of signal-to-interference-plus-noise ratio (SINR), and mobility management are two critical issues.
In this paper, we first conduct a detailed study on the properties of best signal quality in mobile cellular networks. Here, the best signal quality refers to the maximum SINR received from a number of sites. Connecting the mobile to the best base station is one of the key problems. The best base station here means the base station from which the mobile receives the maximum SINR. As the radio propagation experiences random phenomena such as fading and shadowing, the best signal quality is a random quantity. Investigating its stochastic properties is of primary importance for many studies such as capacity analysis, outage analysis, neighbor cell scanning, and base station association. However, to the best of our knowledge, there is no prior art in this area.
In exploring the properties of best signal quality, we focus on cellular networks in which the propagation attenuation of the radio signal is due to the combination of a distance-dependent path-loss and of lognormal shadowing. Consider a ring of radii and such that . The randomness of site locations is modeled by a uniform distribution of homogeneous density in . Using extreme value theory (c.f., [8, 9]), we prove that the maximum signal strength received at the center of from sites in converges in distribution to a Gumbel distribution when properly renormalized and it is asymptotically independent of the total interference, as . The distribution of the best signal quality can thus be derived.
The second part of this paper focuses on applying the above results to mobility support in dense small cell networks. Mobility support allows one to maintain service continuity even when users are moving around while keeping efficient use of radio resources. Today's cellular network standards highlight mobile-assisted handover in which the mobile measures the pilot signal quality of neighbor cells and reports the measurement result to the network. If the signal quality from a neighbor cell is better than that of the serving cell by a handover margin, the network will initiate a handover to that cell. The neighbor measurement by mobiles is called neighbor cell scanning. Following mobile cellular technologies, it is known that small cell networking will also use mobile-assisted handover for mobility management.
To conduct cell scanning [10–12], today's cellular networks use a neighbor cell list. This list contains information about the pilot signal of selected handover candidates and is sent to mobiles. The mobiles then only need to measure the pilot signal quality of sites included in the neighbor cell list of its serving cell. It is known that the neighbor cell list has a significant impact on the performance of mobility management, and this has been a concern for many years in practical operations [13, 14] as well as in scientific research [15–18]. Using neighbor cell list is not effective for the scanning in small cell networks due to the aforementioned characteristics of high density and randomness.
The present paper proposes an optimized random cell scanning for small cell networks. This random cell scanning will simplify the network configuration and operation by avoiding maintaining the conventional neighbor cell list while improving user's quality-of-service (QoS). It is also implementable in wideband technologies such as WiMAX and LTE.
In the following, Section 2 describes the system model. Section 3 derives the asymptotic properties and the distribution of the best signal quality. Section 4 presents the optimized random cell scanning and numerical results. Finally, Section 5 contains some concluding remarks.
2. System Model
The underlying network is composed of cells covered by base stations with omnidirectional antennas. Each base station is also called a site. The set of sites is denoted by . We now construct a model for studying the maximum signal strength, interference, and the best signal quality, after specifying essential parameters of the radio propagation and the spatial distribution of sites in the network.
As mentioned in the introduction, the location of a small cell site is often not exactly known even to the operator. The spatial distribution of sites seen by a mobile station will hence be treated as completely random  and will be modeled by an homogeneous Poisson point process  with intensity .
where is the location of site represents the base station's transmission power and the characteristics of propagation, is the path loss exponent (here, we consider ), and the random variables , which represent the lognormal shadowing, are defined from , an independent and identically distributed (i.i.d.) sequence of Gaussian random variables with zero mean and standard deviation . Typically, is approximately 8 dB [21, 22]. Here, we consider that fast fading is averaged out as it varies much faster than the handover decision process.
In the following, we will use (3) instead of (2).
3. Best Signal Quality
which is the interference from cells in set . In the following, for notational simplicity, the location variable appearing in , and will be omitted in case of no ambiguity. We will simply write , and . Note that since .
In view of Corollary 1, we need to study the properties of the maximum signal strength as well as the joint distribution of and . As described in the introduction, in dense small cell networks, there could be a large number of neighbor cells and a mobile may thus receive from many sites with strong enough signal strength. This justifies the use of extreme value theory within this context.
The distribution of the best signal quality is derived (c.f., Theorem 2 in Section 3.3).
3.1. Asymptotic Properties
See Appendix .
Since is the maximum of i.i.d. random variables, we can also study its asymptotic properties by extreme value theory. Fisher and Tippett [9, Theorem 3.2.3] proved that under appropriate normalization, if the normalized maximum of i.i.d. random variables tends in distribution to a nondegenerate distribution , then must have one of the three known forms: Fréchet, Weibull, or Gumbel distribution. In the following, we prove that belongs to the MDA of a Gumbel distribution. First of all, we establish the following result that is required to identify the limiting distribution of .
See Appendix .
Equation (19b) shows that the tail distribution of the signal strength is close to that of , although it decreases more rapidly. The fact that determines the tail behavior of is in fact reasonable, since is the distribution of the signal strength received from the closest possible neighboring site (with and ). The main result is given below.
See Appendix .
Note that the total interference can be written as where denotes the complement of in . Under the assumptions that the locations of sites are independent and that shadowings are also independent, and are independent. The asymptotic independence between and thus induces the asymptotic independence between and . This observation is stated in the following corollary.
This asymptotic independence facilitates a wide range of studies involving the total interference and the maximum signal strength. This result will be used in the coming sub-section to derive the distribution of the best signal quality.
The asymptotic properties given by Theorem 1 and Corollaries 3 and 4 hold when the number of sites in a bounded area tends to infinity. This corresponds to a network densification process in which more sites are deployed in a given geographical area in order to satisfy the need for capacity, which is precisely the small cell setting.
3.2. Convergence Speed of Asymptotic Limits
Theorem 1 and Corollaries 3 and 4 provide asymptotic properties when . In practice, is the number of cells to be scanned, and so it can only take moderate values. Thus, it is important to evaluate the convergence speed of (20) and (23). We will do this based on simulations and will measure the discrepancy using a symmetrized version of the Kullback-Leibler divergence (the so-called Jensen-Shannon divergence (JSdiv)).
3.3. Distribution of the Best Signal Quality
From the above results, we have the distribution of and the asymptotic independence between and . In order to derive the distribution of the best signal quality, we also need the distribution of the total interference.
See Appendix .
See Appendix .
The approximation proposed in Theorem 2 will be used in Section 4 below. It will be validated by simulation in the context considered there.
4. Random Cell Scanning for Data Services
In this section, the theoretical results developed in Section 3 are applied to random cell scanning.
4.1. Random Cell Scanning
Wideband technologies such as WiMAX, WCDMA, and LTE use a predefined set of codes for the identification of cells at the air interface. For example, 114 pseudonoise sequences are used in WiMAX , while 504 physical cell identifiers are used in LTE . When the mobile knows the identification code of a cell, it can synchronize over the air interface and then measure the pilot signal quality of the cell. Therefore, by using a predefined set of codes, these wideband technologies can have more autonomous cell measurement conducted by the mobile. In this paper, this identification code is referred to as cell synchronization identifier (CSID).
When a mobile gets admitted to the network, its (first) serving cell provides him/her the whole set of CSIDs used in the network. The mobile then keeps this information in its memory.
To find a handover target, the mobile randomly selects a set of CSIDs from its memory and conducts the standardized scanning procedure of the underlying cellular technology, for example, scanning specified in IEEE 802.16 , or neighbor measurement procedure specified in 3G  and LTE .
The mobile finally selects the cell with the best received signal quality as the handover target.
In the following, we determine the number of cells to be scanned which maximizes the data throughput.
4.2. Problem Formulation
The optimization problem has to take into account the two contrary effects due to the number of cells to be scanned. On one hand, the larger the set of scanned cells, the better the signal quality of the chosen site, and hence the larger the data throughput obtained by the mobile. On the other hand, scanning can have a linear cost in the number of scanned cells, which is detrimental to the throughput obtained by the mobile.
Let us quantify this using the tools of the previous sections.
Note that is the expected throughput from the best cell. Since is the maximum signal quality of the cells, increases with and so does . Hence, the mobile should scan as many cells as possible. However, on the other hand, if scanning many cells, the mobile will consume much time in scanning and thus have less time for data transmission with the serving cell. A typical situation is that where the scanning time increases proportionally with the number of cells scanned and where the data transmission is suspended. This for instance happens if the underlying cellular technology uses a compressed mode scanning, like for example, in IEEE 802.16  and also inter-frequency cell measurements defined by 3GPP [12, 27]. In this mode, scanning intervals, where the mobile temporarily suspends data transmission for scanning neighbor cells, are interleaved with intervals where data transmission with the serving cell is resumed.
Another scenario is that of parallel scanning-transmission: here scanning can be performed in parallel to data transmission so that no transmission gap occurs; this is the case in, for example, intrafrequency cell measurements in WCDMA  and LTE .
Finally, let be the average throughput received from the serving cell when no scanning at all is performed (this would be the case if the mobile would pick as serving site one of the sites of set at random).
4.3. Numerical Result
where is the distance from the base station in meters, the number of penetrated floors in the propagation path. For indoor office environments, is the default value ; however, here, the small cell network is assumed to be deployed in a general domain including outdoor urban areas where there are less penetrated walls and floors. So, we use in our numerical study.
It is assumed that the mobile is capable of scanning eight identified cells within 200 ms . So, the average time needed to scan one cell is given by .
In order to check the accuracy of the approximations used in the analysis, a simulation was built with the above parameter setting. The interference field was generated according to a Poisson point process of intensity in a region between and . For a number , the maximum of SINR received from base stations which are randomly selected from the disk between radii and was computed. After that the expectation of the maximal capacity received from the selected BSs was evaluated.
Figure 5(b) gives an example of acceleration for second and . In the plot, is normalized by its maximum. Here, an agreement between model and simulation is also obtained. We see that first increases rapidly with , attains its maximum at by simulation and by model, and then decays.
It is clear that this factor also depends on the ratio . Figure 5(c) plots the optimal for different values of . Larger will drive the optimal towards larger values. Since can be roughly estimated as the mobile residence time in a cell, which is proportional to the cell diameter divided by the user speed, this can be rephrased by stating that the faster the mobile, the smaller and thus the fewer cells the mobile should scan.
Finally, Figure 5(d) plots the growth factor with different . In Figure 5(d), the "limiting case" corresponds to the case when or . We see that is quite stable w.r.t. the variation of . Besides, flattens out at about 30 cells for a wide range of . Therefore, in practice this value can be taken as a recommended number of cells to be scanned in the system.
5. Concluding Remarks
In this paper, we firstly develop asymptotic properties of the signal strength in cellular networks. We have shown that the signal strength received at the center of a ring shaped domain from a base station located in belongs to the maximum domain of attraction of a Gumbel distribution. Moreover, the maximum signal strength and the interference received from cells in are asymptotically independent as . The above properties are proved under the assumption that sites are uniformly distributed in and that shadowing is lognormal. Secondly, the distribution of the best signal quality is derived. These results are then used to optimize scanning in small cell networks. We determine the number of cells to be scanned for maximizing the mean user throughput within this setting.
A. Proof of Lemma 2
B. Proof of Lemma 3
C. Proof of Theorem 1
We will use Lemma 3 and the following two lemmas to prove Theorem 1.
Lemma 5 (Embrechts et al. ).
Lemma 6 (Takahashi ).
D. Proof of Lemma 4
E. Proof of Theorem 2
Under the assumption that sites are distributed as a homogeneous Poisson point process of intensity in , the expected number of cells in is . We assume that is much larger than , which ensures that there are cells in with high probability, so that is well defined.
Substitute the above into (E.7) and then into (E.6), we have (27).
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