A context-aware framework for the efficient integration of femtocells in IP and cellular infrastructures
© Makris et al; licensee Springer. 2013
Received: 29 June 2012
Accepted: 31 January 2013
Published: 5 March 2013
In today’s heterogeneous networking (HetNet) environments, where end users are provided with universal connectivity opportunities, femtocell deployments can become key players in the enhancement of critical performance indicators such as capacity, coverage, QoS, etc. In order to confront the up-to-date LTE femtocell challenges, we propose a context-aware framework that provides a controlled environment from the femtocell point of view, which is required for applicable functionality. More specifically, we aim to (a) control the local environment where the femtocell is placed within, by efficiently managing the total incoming traffic load and by continuously adjusting the distribution of the backhaul capacity among the coexisting networks and (b) control the macro–femto interference caused by macrocell users transmitting close to the femtocell by investigating the “femtocell as a relay” concept. Finally, the performance of the proposed framework is evaluated via simulation results showing that the overall performance of a HetNet environment can be leveraged in terms of QoS requirements, energy saving and data rate enhancement.
KeywordsContext-aware Femtocell Relay RRM HetNet
The Future Internet (FI) vision is expected to extend the “Always Best Connected” (ABC) notion and include use cases according to which the FI end user will attain any service on a single intelligent device (e.g., smartphone, tablet, etc.) using any available network within a heterogeneous network environment consisting of open, cognitive, and collaborative wireless and wireline networks. Towards this direction, heterogeneous networking (HetNet) deployments, proposed by 3GPP LTE-A standardization group, are expected to become a reality in the next few years, thus providing efficient ways to deal with the continuously growing traffic demand [1, 2]. In contrast with the widely known cellular networks, which are based on a dedicated terrestrial backbone, the emerging femtocell technology utilizes the IP backhaul network along with small-sized base stations (BSs) .
Hence, the advent of femtocells added a new perspective on the ABC concept as they are capable of extending cellular coverage with minimum cost by utilizing an already deployed IP infrastructure. However, despite their mature cellular heritage, several aspects of such technology still require detailed investigation . Indeed, merging the strict QoS requirements of femtocell users with the best effort IP architecture can be problematic. The main issue is the efficient sharing of the common backhaul capacity among the femtocell and other existing wireline or wireless networks . As IP networks do not employ a Service Admission Control (SAC) operation, the capacity allocated to the femtocell can significantly and unpredictably fluctuate over time. Moreover, the backhaul capacity itself can also fluctuate as a result of traffic load variations at the ISP core network. Consequently, the performance of the femtocell operation can considerably be degraded, thus leading to inability to maintain the required QoS . Current femtocell deployments address this issue by operating in closed mode, thus limiting the number of users that can have access to the femtocell. This approach represents a low-cost and low complexity solution, which, however, can be efficient only under low traffic conditions. On the other hand, future LTE femtocells operating in a heterogeneous and small cell network (HetSNet) environment may widely function in open or hybrid access modes and therefore need to handle significantly higher traffic loads .
Furthermore, the unstructured placement of femtocells within a structured cellular network causes interference issues. In the downlink, the femto base station (BS) transmitting towards its femto users will interfere with the reception of nearby macro users served by the macro BS. On the other hand, in the uplink, users transmitting towards the macro BS may cause interference to closely located femtocells . As the positioning of the femtocells cannot be controlled, the only source of interference that can actually be controlled is the transmission energy of the mobile terminals (MTs) that communicate with the macrocell BS (e.g., following femtocell as a relay concepts described in this article). Consequently, we focus on the uplink case aiming to reduce the transmission power of the macro user equipments (UEs) in order to protect the operation of nearby femtocells. Towards this end, one state-of-the-art proposal is that each femtocell should serve its own interfering macro UEs . Although this practice would definitely reduce interference, on the other hand it may raise privacy and QoS issues, as the femtocell owners will not always be willing to share their limited backhaul line with external users.
Based on the above considerations, we propose a context-aware framework for LTE femtocells’ efficient integration (CA-FEI) in IP and cellular infrastructures, which aims to provide a “shielded” environment for the femtocell operation. Our approach is twofold: (a) control the local environment, where the femtocell is placed within, by efficiently managing the total incoming traffic load and continuously adjusting the distribution of the backhaul capacity among the coexisting networks. The full exploitation of femtocells’ backhaul capacity is examined following the context-aware mobile and wireless networking rationale  aiming to achieve more accurate and timely radio resource management (RRM) decisions and (b) to confront the uplink interference that comes from macro UEs by exploiting the use of the “femtocell as a relay” concept. We identify when a femtocell is able to act as a relay in order to assist the reduction of local interference and thus improve the QoS in other femtocells of the same region.
The remainder of the article is organized as follows: in Section 2, we cite the related work in the areas that make up the subject of our study, in Section 3, a typical system model is presented together with an outline of the proposed modifications, in Sections 4, 5, 6, and 7, the functions that should be implemented by each CA-FEI module are presented in detail, while in Section 8, numerical results and details about the simulations are provided. Finally, concluding remarks are given in Section 9.
2. Related work
Several QoS considerations related to the femtocell network architecture have arisen and deal with the question how it is possible for the backhaul network to provide acceptable QoS . The existence of net heterogeneity poses a serious concern, especially in cases where the wireline backhaul provider is not in a tight strategic relationship with the cellular operator. Moreover, as femtocell access points (FAPs) are part of a continuously changing HetNet environment , femtocells experience difficulty transferring even low-bandwidth services.
2.1. Integration of femtocell and IP traffic
The limitations of xDSL backhaul capacity is a vital issue that needs to be addressed. Recent 3GPP standardization activities assert that it shall be possible for the network to set different criteria for access control in a hybrid cell for a closed subscriber group (CSG) and non-CSG members . These different criteria incur conflicting results and this problem can be tackled by specific admission control policies, as addressed in our related work in . To the best of the authors’ knowledge, there is no other work in the literature dealing with the efficient integration of femtocell and IP traffic. The proposed CA-FEI framework can be considered as complementary to , which presents a scheme for dynamic service-level agreement negotiation between the ISP provider and the femtocell operator, aiming to tackle the problem of xDSL line’s varying capacity. In general, our proposed scheme adopts a hybrid partitioning policy  and can be classified within the family of admission control schemes designed for the integration of heterogeneous networks, such as [14–17]. However, such schemes are not directly applicable in our case as either (a) they consider significantly different system models, which in most cases include networks that have their own separate links to the core network or (b) they support basic QoS features that are not compatible with the strict QoS requirements of a femtocell.
2.2. Femtocells as relays and interference mitigation
In the classic setup, a femtocell relays the traffic of the femto users utilizing the wired backhaul link. In more advanced cases, they could adopt an open access policy and cooperate with users served by the macro BS. In , the two different policies are compared concluding that in TDMA/OFDMA networks, the decision of whether a macro UE will be accepted in a hybrid access femtocell, depends on user density. Elkourdi and Simeone  suggest the use of femtocells as relays in the uplink stream of a cellular network by serving both indoor and outdoor users via the wireline backhaul. The decoder is positioned at the mobile operator network and soft information is provided by the femtocell/relay to facilitate decoding. Rath et al.  suggest utilizing users, in the range of the femtocell, as relays of information to the macrocell UEs and conclude that with this system architecture, the macrocell’s bandwidth shortage can be surpassed. Dizhi and Wei  elaborate on the relaying concept of , employing femtocells with the ability to broadcast relay service availability to the macrocell users on behalf of the femtocell users. Moreover, it is shown that the proposed architecture achieves better load balancing for the macrocell and improved throughput for the macrocell users. Furthermore, in , mitigation schemes using femtocells with relaying capabilities are presented. Finally, the proposed scheme of  introduces femtocells that are able to decode the control channel of the macrocell BS. In this way, the femto BSs can adjust their transmissions, since they have information about the scheduled users in the macrocell avoiding inter-cell interference with the macrocell. In addition, interference cancellation is implemented at the femtocells as an extra interference mitigation measure.
To the best of the authors’ knowledge, no work studies the concept of merging classical relaying and femtocell technologies aiming to reduce the burden of the wireline backhaul. Consequently, we introduce the concept of femtocells as relays that can communicate with a wireless backhaul with the macro BS, allocating as a result, all the available wireline backhaul to the femto UEs (see also paper’s contribution points in Section 1).
3. System architecture and proposed modifications
In the following, we will present a typical topology of a femtocell deployment, which will help us to clearly point out the proposed architectural modifications.
3.1. Typical topology of a femtocell deployment
3.2. Overview of the proposed modifications
3.2.1. RRM integration layer
3.2.2. Context information acquisition module
The context information acquisition module (CIAM), as shown in Figure 2, acquires low-level data from the IP router and the network interfaces, and transforms them to the proper form (i.e., high-level context information) in order to be utilized as input for the SAC and Capacity Distribution modules as well as for the Relay module (cf. Section 6). The main functionalities of CIAM regarding context acquisition are the initial set-up, monitoring, and gathering of context. The setup phase regards the setting of the initial operating parameters, while the monitoring and gathering processes monitor the statistical properties of the incoming traffic load and backhaul capacity in order to adjust, if necessary, the parameters originally selected by the user and to trigger the operation of the relay module.
3.2.3. Decode-and-forward relay module
An additional functionality implemented in the proposed framework is decode-and-forward relaying. Contrary to the classic setup (see Figure 1), we use wireless backhaul links towards the macro BS by adding an outdoor donor antenna connected through cable link with the femto BS. In this way, we integrate wireless relaying capabilities to the femtocells in order to protect the communication of the femtocell BS and the femto UEs using the wired backhaul link. Furthermore, as the macro UE communication with the femto BS will be performed on small distance links, there is an increased possibility of achieving a reduced power transmission in this part of the macrocell. This fact can lead to better SINR in other femtocells in the vicinity of the macro UE, which were not available to cooperate at that time. Also, as the second hop is performed via an outdoor antenna on the rooftop of the building, there is a good chance of having LOS condition with the macro BS. As a result, the required power in the second hop can also be reduced, while attaining the desired data rate in the end-to-end two-hop link.
4. SAC module
In order to perform the ISAC operation, the definition of common service classes is required for all the underlying networks. Thus, services with similar characteristics, regardless of their network origin, are classified to the same integrated service group (ISG). The number of ISG s should be at least four, corresponding to conversational, streaming, interactive, and background service classes as determined by 3GPP . However, each ISG can be further partitioned, if a more precise categorization is required. In the general case, corresponding to a total of k ISG s, the backhaul capacity is also divided to k primary virtual partitions (VPs) and each partition S j , 1 ≤ j ≤ k is associated to the j th ISG j . An additional (k + 1) partition, namely the Capacity Exchange (CE) partition, is also defined as required by the PPA process (cf. Section 4.2). A prerequisite for CA-FEI is that a method for indicating the QoS requirements of each traffic flow, such as DiffServ , should be employed at all coexisting networks. However, if a flow does not need any QoS or does not indicate that it needs a specific QoS treatment, then it will be treated as a background service (cf. Section 4.2.3).
4.1. Capacity partitioning
The aim of ISAC is to ensure that a service call with specific QoS requirements is admitted to the system only if there is adequate capacity to handle it. Consequently, the initial virtual capacity partitioning should be performed for the worst-case situation, where the traffic load intensity A j offered by each ISG j has its maximum value, while at the same time the backhaul capacity has its minimum value Cmin. During this critical system state, the VP of the delay insensitive background services, denoted in the following as VPB, is restricted to its minimum size. Assuming that VPB is the k th partition, then the partitioning problem is constrained to the first k – 1 VP s.
At first, the size of each of the k – 1 partitions has to be defined in such a way that the required upper bound G j of the blocking probability of the ISG j calls to be guaranteed. Therefore, as the initial size of the partitions should correspond to the maximum acceptable blocking probability, each S j 0 can be calculated directly from Equation (1) for the case of equality (i.e., P b,j(A j , m j ) = G j , ∀ j ∈ [1, (k − 1)]).
Subsequently, we have to ensure that the summation of the calculated capacities S j 0 of the partitions meets the limitation of Equation (2). If not, then the network administrator has to select and prioritize those ISGs for which the target blocking probability can be further increased. The administrator has to take into account both the nature of the network and the requirements of the users so that his decision to be as tailored as possible to the specific network environment. In case the femtocell is installed by a home user, then he should be guided by a properly designed setup process in order to make the right selections based on his preferences (cf. Section 6.1). Given the prioritized list of the selected ISGs, we iteratively follow the order of the list, and at each iteration we reduce by a j the size of each partition S j 0 until Equation (2) is valid.
- (c)Finally, if a part C L of the C min capacity is left unallocated after the initial partitioning process, then it is shared among the j partitions proportionally to their respective traffic load intensity A j . Thus, each partition is extended by a fraction of C L , which is normalized to a j . The initial size S j 0 of each partition VP j can be defined as(3)where l j is an integer, which is calculated by applying the floor function as follows:(4)
The additional capacity is calculated consecutively starting from the partition that is expected to handle the higher traffic load while ties are randomly resolved.
While the initial capacity partitioning guarantees a reliable operation for the worst-case scenario, at the same time it could lead to capacity underutilization under normal traffic conditions. Therefore, we employ the PPA process in order to sequentially transfer unutilized capacity from the k – 1 primary VPs to the CE partition and vice versa. Consequently, the VPs’ size is not fixed, but instead it is adjusted in response to the variations of the traffic load composition.
4.2.1. Size of VPs
The higher the value of d is, the more averaged over time B j becomes. The guarding factor g j is a positive integer (1 ≤ g j ≤ m j ), which guarantees that the size of the partition can be decreased only if it has at least g j ∙ a j available capacity for the future incoming ISG j service calls.
Furthermore, the size of a VP can be increased by a j only if a corresponding decrease of the CE partition by –a j , according to its occupancy, can be performed. If not, the capacity adjustment of the VP is scheduled and performed asynchronously, when conditions permit together with the inverse adjustment of the CE.
4.2.2. Size of the CE partition
As previously mentioned, the adjustment steps, which cannot be performed due to the restriction of Equation (8), are scheduled and performed asynchronously.
4.2.3. Size of the background services virtual partition (VPB)
Obviously, the background services can be accepted either at the VPB partition or at the CE partition.
4.3. Admission decision process
Consequently, ISAC extends the QoS-aware admission control of femtocells to all the underlying IP networks following the FI paradigm discussed at . Furthermore, ISAC does not require the ISP to exclusively provide any kind of QoS guarantees. On the contrary, the main admission policy of ISAC is to limit the acceptance of QoS demanding services to what the ISP is proven to be able to provide. After a service call is accepted and has been allocated capacity at one of the VPs, it is then forwarded to the CAM module.
5. CAM module
Hence, each admitted service call modifies the fraction of the current backhaul capacity, which is allocated to each serving network. Consequently, while the ISAC module is based on the minimum value of the backhaul capacity in order to be able to guarantee a minimum QoS for all admitted service calls, the CAM module distributes the total available backhaul capacity among the serving networks.
In other words, although a VP may be empty and reserved for a specific ISG, its respective physical capacity does not remain unused but instead can always be utilized by the underlying packet schedulers, as implied by Equation (10). Therefore, it becomes clear that designing CA-FEI as a two-tier scheme makes it possible to have, at the same time, a virtual (logical) capacity distribution for the admission control process and an actual (physical) capacity distribution for the serving networks. As a result, the proposed CA-FEI scheme is more flexible compared to typical one-tier schemes that utilize actual capacity partitions and thus it is able to achieve higher utilization of the backhaul capacity.
Furthermore, it can also be concluded from Equation (10) that if two networks have to serve the same traffic load, in terms of volume and distribution among the ISGs, these networks will also be allocated the same bandwidth. Consequently, the bandwidth distribution among the serving networks can be considered as fair because it is directly proportional to their ongoing traffic load.
The CIAM described in this section aims to transform low-level data acquired from the IP router and the various network interfaces into higher-level context input information for the RRM modules as well as for the relay module, while the latter can also provide to CIAM context information regarding the radio access links’ quality in the vicinity of the femtocell. For example, in cases where CIAM identifies high wired backhaul capacity utilization, it can trigger the relay module to route delay sensitive traffic or traffic incurred by macro UEs via the wireless backhaul link. On the contrary, when the relay module informs CIAM that the wireless backhaul link is congested or the femtocell’s radio environment experiences high interference levels, CIAM can inform the RRM integration layer in order the various partitions’ capacities to be restructured in ways that efficient QoS can be achieved for all ISGs. CIAM has two main functionalities regarding context acquisition, which are initial set-up phase and monitoring/gathering of context.
6.1. Initial setup phase
During the deployment phase, the user (i.e., home user or ICT technician) will be able to set, through a GUI, the initial operating parameters. Such parameters are the number of users that is expected to be served by the femtocell as well as their preferences regarding the services they usually use and the way they use them. As this can be a difficult task for the average home user, he will be offered, through the setup GUI, a set of typical values to select from. More advanced setup procedures may even autonomously select one of these sets through a question–answer process interacting with the user, aiming to discover his preferences. Furthermore, an “Advanced user mode” will allow more experienced users or ICT specialists to finely tune the overall process. The result of this initial setup phase will serve as a basis on which the CIAM will build its future outcomes.
In cases, where the home user has to configure the initial setup phase, he/she can assign priorities to specific UEs, which frequently utilize the femtocell for acquiring access to mobile services [e.g., the home user can define his/her CSG and determine the context in which a nearby macro UE can connect to the femtocell without degrading the quality of experience (QoE) of CSG’s users]. Another priority assignment procedure can take place for different service classes (e.g., the home user can define which type of services will have the highest priority and thus no QoS degradation is acceptable even in high backhaul capacity utilization situations). Given the fact that a mentality shift occurs in cellular operators’ industry towards giving several motivations to femtocell owners in order their FAPs to serve more macro UEs and thus offload mobile traffic from cellular operators’ infrastructure, the initial setup phase can become very important for the femtocell owner to experience considerable bill discounts.
In cases, where ICT specialists configure the initial setup process (e.g., public access and enterprise scenarios analyzed in ), the whole procedure can become even more complicated as hybrid or open access femtocells are assumed and thus service classes’ and user groups’ priorities should be configured taken into consideration the overall HetSNets environment.
6.2. Monitoring and gathering
During operation, the CIAM, through the monitoring and gathering processes, will keep track of the statistical properties of the incoming traffic load as well as of the backhaul capacity in order to be able to adjust, if needed, the values initially selected by the user. For example, capacity adjustments and the various partitions’ restructure can happen in cases of overload-state situations or in cases when context-aware prioritization based on the type of services that the femto-relay is currently supporting, occur. More specifically, if wired backhaul experiences intolerable delays (e.g., in overload-state situations or when delay-sensitive services cannot effectively be delivered), then the wireless backhaul is selected, as the required QoS levels can be more easily maintained, because of the fact that the data “remains” within one cellular operator’s network.
The first sub-functionality of monitoring is the sensing of context, meaning both the detection and reception of data from sensors without introducing additional traffic overhead. Efficient scanning is a matter of vital importance since real-time limitations have to be satisfied and QoE objectives should be maintained within acceptable levels . The key objective is the dynamic adjustment of the defined set of parameters by adopting event-based and on-demand monitoring policies . One example of monitoring is the sensing of the FAP’s radio environment in order to clarify if interference levels are below some predefined and desirable thresholds. If this is the case, then the relay module can inform CIAM about the wireless backhaul link alternative and consequently the CIAM will update the wired backhaul capacity partitions (i.e., PPA process described in Section 4), so that the latter can be restructured in favor of the QoS/QoE metrics for UEs. Femto-relay concept can also help in interference protection both in downlink and uplink scenarios. In downlink, a macro UE that has a weak direct link with eNB and is located in the vicinity of an open-access femto-relay can have an increased SINR if it selects to cooperate. Hence, the number of situations where two-tier interference could lead in outages can be reduced. In uplink, a macro UE that transmits with full power to maintain its connection with the eNB can instead choose a two-hop transmission through an open-access femto-relay. This cooperation will result in power reduction by the macro UE and incurred interference in nearby closed-access or non-selected femto-relays will be mitigated.
7. Decode-and-forward relay module
As we focus our interest on the uplink case, we identify the macro UEs as the main source of interference towards the femto BSs. This scenario is depicted in Figure 5, where two macro UEs are in the vicinity of four femtocells. Aiming to improve the SINR at the femto BSs, we propose to take advantage of femto BSs that currently do not serve any femto UE and to utilize them as relays for the macro UE. In this way, we can achieve reduction in transmission power of the macro UE compared to the power required for direct transmission. By decreasing the transmission power, the neighboring femtocells, which were not available to cooperate, will experience less macro interference, too.
Moreover, in conjunction with the backhaul protection schemes analyzed in Section 4, we suggest employing outdoor donor antennas towards the macro BS, enhancing the femto BS with wireless relaying capabilities. This extra wireless backhaul does not replace the existing wireline backhaul but rather complements it. In this way, new possibilities for better QoS provision to delay sensitive applications as well as better interference coordination between two-tier networks are opened. The selection of outdoor antennas in the backhaul link increases the probability of having LOS conditions with the macro BS, resulting in reduced transmission power and less interference to nearby femtocells. In the case where no outdoor antenna is installed the indoor antenna could be used but with increased pathloss towards the macro BS. Furthermore, by choosing to route the macro UE traffic through the wireless backhaul, further protection and better usage of the wireline backhaul capacity of the femtocells is achieved, because more capacity (i.e., bandwidth) is available for UEs served by the wired backhaul. More specifically, indoor users connected with a different interface to the femto BS will not have to share their backhaul capacity with UEs, which do not belong to the CSG .
In this case, the UE that is located in the femtocell’s vicinity will receive a message denoting the latter’s availability to cooperate. As the femtocell’s location is fixed, it can acquire the channel state information (CSI) of the wireless backhaul link towards the macro BS with good accuracy. This information is included in the message sent to the UE together with the CSI of the femto BS-UE link. By receiving this information the UE has to answer the following question: For the data rate required by its service, which route will provide the largest power reduction? In order for the UE to decide its uplink route, a comparison between the direct and the two-hop links is performed.
From the above description, we see that the exploitation of the spatial diversity provided by the deployment of femtocells could lead to a two-hop transmission requiring reduced transmission power compared to a direct transmission towards the macro BS. As a result, having an additional path to choose from may effectively reduce the interference arising from the transmissions of the macro UE.
Macrocell radius (m)
Femtocell radius (m)
Number of femtocells
Number of macro UEs
UE-Femto BS link: Adaptive modulation (QPSK, 16 QAM)
Femto BS-Macro BS link: Adaptive modulation (QPSK, 16 QAM)
Shadowing std UE-Macro BS (dB)
Shadowing std UE-Femto BS (dB)
Shadowing std femto BS-macro BS (dB)
UE transmit SNR (dBm)
Femto transmit SNR (dBm)
Receiver diversity gain (dB)
Penetration loss (dB)
Power reduction, macro UE data rate
In this case, we assume line-of-sight (LOS) conditions as the outdoor antenna will be on the rooftop and directed towards the macro BS. The shadowing standard deviation is equal to 6 dB.
In this link, the shadowing standard deviation has a greater value, equal to 10 dB.
8. Simulation results
In this section, we aim to evaluate, through event driven simulation, the two main functionalities that are introduced by the proposed modules which are (a) the ability of the RRM integration layer to provide a predetermined QoS even under varying traffic loads and (b) the ability of the relay module to contribute to the reduction of the overall local interference. For (a), we evaluate the performance of the RRM integration layer through a custom made event-driven simulator written in C/C++. As already referred, we assume that the incoming service requests arrive according to a Poisson process, while their duration is exponentially distributed. For (b), we followed the simulation guidelines in  for the development of an event-driven simulator implemented in Matlab assuming the simulation parameters shown in Table 1.
8.1. Perceived QoS under varying traffic loads
In this section, we study the ability of the CA-FEI scheme to continuously adjust to variations of the traffic load distribution. Hybrid Partitioning Schemes (HPS) and Fixed Partitioning Schemes (FPS)  can be considered as a base of reference for comparison because their operation can be approximated, up to some point, with the currently available in the market router and femtocell combinations. However, HPS offer better capacity utilization and are more immune against traffic variations compared to FPS and are thus better competitors for the proposed CA-FEI scheme. In our case, the employed HPS has a fixed partition, defined for each service, which requires specific QoS guarantees, while the remaining capacity is utilized as common pool partition for all service calls.
We assume a simulation scenario, where a femtocell coexists with a wire-line IP network sharing a 100-Mbps fast ethernet backhaul line. The period Δt of PPA is set to 100 ms, the guarding factor is uniformly set to 10 and the averaging time interval d is extended up to the duration of the simulation (i.e., the blocking rate B is recalculated each time PPA is executed). Both CA-FEI and HPS schemes arrange their capacity partitions expecting at the worst case a 35% reduction of the backhaul capacity as well as a traffic load distribution consisting of only delay sensitive services (40% Voice, 30% Video-telephony, and 30% Streaming video) all having a target GoS of 1%. In order to analyze the effect of PPA function, we further assume that the traffic load distribution presents an unpredictable short-term variation. Thus, the percentage of video-telephony calls is increased to 50%, while the percentage of streaming video calls is decreased to 10%. The following simulation results correspond to a confidence interval of less than 5% with a confidence level of 95%.
Total capacity utilization
Offered traffic load
Relative percentage of increase
In conclusion, at high traffic loads, HPS (and consequently FPS, used in current femtocell deployments) are inefficient in terms of capacity utilization. Hence, considering the increased traffic volume expected for LTE systems, we have proposed a framework, which provides a suitable environment for the femtocell operation through effective and efficient management of the total incoming traffic and the common limited backhaul capacity.
8.2. Impact of DF relay module on the overall system’s performance
where kTB is the thermal noise level equal to −174 dBm/Hz, NF is the prescribed maximum noise figure for the receiver and IM is the implementation margin while −3 dB is the diversity gain. The modulation schemes range from QPSK 1/3 to 16QAM 4/5 and we use the corresponding SINR and IM values from Table 22.6 of .
As mentioned in Section 7, the femtocells are assumed to have an outdoor donor antenna to support the wireless backhaul link towards the macro BS and a classic indoor coverage antenna. For simplicity, we assume NLOS conditions for UE-to-macro BS and UE-to-femto BS and LOS conditions for the femto BS-to-macro BS links. The UEs are also dropped uniformly in the macrocell area and are equipped with one transmit antenna. As a result, the maximum modulation order they can support is 16 QAM. Whenever a two-hop transmission is selected, a penetration loss of 20 dB is considered . Also, overhead due to signaling when a macro UE is hand-offed from the macro BS to a femto BS and vice versa is considered negligible in the data rate results that are presented in the following paragraphs.
We observe that as the available femtocells increase, there is a greater chance to achieve a transmission with reduced power by the interfering macro UEs and the backhaul antennas positioned on the roof of the building. The decrease in dBm ranges from 2.9 to 9.07 dBm for the extreme case, where all the femtocells are available (cf. Figure 5). For medium cooperation factors (i.e., 30–50%), we see that power reduction takes values between 4.5 and 6 dBm. As a result, to achieve the specified data rate, macro interference can be reduced whenever an additional path is available to select from (i.e., when a cooperating femtocell is near a macro UE). Moreover, the arising interference to femtocells currently serving UEs of CSG will be accordingly reduced.
From Figure 8, we observe that for each case, if the wired backhaul is available, we completely avoid the interference in the second hop. As a result, we see an improvement over the case of only wireless backhaul, which for the case of 30% has a range between 4.4 and 6.15 dBm and for the case of 40% between 5.1 and 7.3 dBm.
We see that when a macro UE receives an availability message from a femtocell with the best link, it can associate with it and relay its traffic through a two-hop link. As we mentioned before, the two-hop route will be selected, when the direct link’s data rate is below the two-hop’s one. Hence, by exploiting spatial diversity, the macro UEs can improve their data rate by 26% in the extreme case of 100% available femtocells. For more realistic cases of 30–50% femtocell availability, the improvement can range between 10 and 16%. We note that these values are the relative improvement achieved with our scheme compared to the classic femtocell setup.
We see that macro UEs can benefit from femtocell cooperation in cases where the wireless backhaul is supported by outdoor antennas. When no outdoor antennas are available, data rate sees almost no improvement due to the low transmit power of the femtocell and the penetration loss. On the other hand, when outdoor antennas are added to the network, we see significant improvement even for percentages below 50%. In the low percentage range, we observe that data rate increases between 1.3 and 5.5% for 30% femtocell availability, while for 40% femtocell availability, we see an increase of 1.8 to 6.7% compared to the case of only direct transmission in the uplink. Again, the above percentages are relative improvement compared to the reference case of the classic femtocells without wireless backhaul.
9. Conclusions and future work
This article elaborates on aspects of the recently introduced communication paradigm of HetSNets, which currently constituted the study item of 3GPP LTE-Advanced releases. Among a plethora of research issues examined in the HetSNets area, we dealt with open challenges such as the femtocells’ limited backhaul capabilities and the macrocell/femtocell cooperation by proposing networking resources and interference management solutions from the femtocell point of view. Therefore, a novel context-aware framework for the efficient integration of femtocells in IP and cellular infrastructures, called CA-FEI, is proposed. CA-FEI exploits context-aware mobile and wireless networking principles thus providing a controlled architectural environment, in which a femtocell is able to function properly. Simulation results show that CA-FEI outperforms competitive schemes used in real-market femtocell deployments regarding QoS, energy savings, and data rate enhancements achieving to (a) provide a predetermined QoS even under varying traffic loads via the RRM integration layer and (b) contribute to the reduction of the macrocell/femtocell interference via the relay module.
Regarding business-logic threats, cost efficiency and the real-market penetration rate are the major challenges for the proposed femto-relay concept. Hence, the femto-relays’ manufacturing and large-scale deployment should outperform traditional deployment solutions (e.g., operator-initiated fully-fledged eNBs’ and user-initiated uncoordinated femtocells installation) in terms of OPEX/CAPEX and delivered QoS/QoE, while new business-logic research pathways and noteworthy expected impact should be realized for all femto-relay beneficiaries. More specifically, cellular operators can exploit femto-relaying towards managing whole small cell networks being under their realm as a single HetSNets entity and thus be able to simultaneously tackle challenges on heterogeneous access and backhaul parts of their infrastructure. Mobile service providers can also increase their revenues by exploiting promising femto-relaying capacity gains, towards delivering novel and even more resource-consuming mobile applications. Vendors/manufacturers can benefit from developing elegant H/W solutions and customer-friendly small cell network products. Research and retail-oriented SMEs can also benefit from the envisioned femto-relay market growth, while citizens will be provided with a wider selection of mobile applications with better QoS, seamless service provisioning, and service outages minimization.
Based on the above considerations and regarding our related future work, we plan to extend the CA-FEI concepts applicability to larger-scale HetSNets environments investigating its flexibility to various HetSNets deployment scenarios. We aim to prove that operating femtocells as relays can improve HetSNets’ overall RRM objectives by extending the overall virtual backhaul link capacity in small cell areas, where excessive traffic is monitored at specific time intervals and that optimal exploitation of context information among all HetSNets entities can be the cornerstone of the proposed architectural solutions.
This work has been partially funded by the Research Projects GREENET (PITN-GA-2010-264759), CO2GREEN (TEC2010-20823), GREEN-T (CP8-006).
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