- Research Article
- Open Access
Joint Power Control, Base Station Assignment, and Channel Assignment in Cognitive Femtocell Networks
© John Paul M. Torregoza et al. 2010
- Received: 1 September 2009
- Accepted: 29 January 2010
- Published: 5 May 2010
Cognitive radio and femtocells are recent technology breakthroughs that aim to achieve throughput improvement by means of spectrum management and interference mitigation, respectively. However, these technologies are limited by the former's susceptibility to interference and the latter's dependence on bandwidth availability. In this paper, we overcome these limitations by integrating cognitive radio and femtocell technology and exploring its feasibility and throughput improvement. To realize this, we propose an integrated architecture and formulate a multiobjective optimization problem with mixed integer variables for the joint power control, base station assignment, and channel assignment scheme. In order to find a pareto optimal solution, a weighted sum approach was used. Based on numerical results, the optimization framework is found to be both stable and converging. Simulation studies further show that the proposed architecture and optimization framework improve the aggregate throughput as the client population rises, hence confirming the successful and beneficial integration of these technologies.
- Cognitive Radio
- Primary User
- Secondary User
- Spectrum Hole
- Aggregate Throughput
Regulatory bodies throughout the world have found that communication bandwidth is becoming scarce, with two of the major causes being inefficient use of the spectrum and ineffective interference mitigation . Studies have found that the majority of the spectrum bands, particularly the licensed bands, are inefficiently utilized. For example, cellular and ISM bands are overloaded in most parts of the world while UHF TV and amateur radio bands are under-utilized in some locations at some specific time instances [2, 3]. In accordance with this, increasing interference levels in the overloaded spectrum render interference mitigation schemes ineffective. In the ISM band, for instance, Bluetooth transmissions suffer from significant packet loss in the presence of WLAN interference . In this work, we make use of cognitive radio and femtocell technology to resolve inefficient spectrum utilization and high levels of interference, respectively.
Cognitive radio (CR) was proposed in an endeavor to allow opportunistic use of unutilized licensed resources, also called spectrum holes, by sensing the communication environment . The sensed information is used to change the communication parameters of cognitive radio users, called secondary users, using software-defined radios without producing significant interference to licensed users, called primary users. Cognitive radios also exhibit auto-configuration and intelligent sensing characteristics [6, 7]. On the other hand, femtocells are short-range, low-power base stations installed by customers to limit interference, thereby increasing network capacity, in a small area. The installed device, called a Femtocell Base Station (FBS), communicates with the main base station, called a Macrocell Base Station (MBS), either by dedicated channels or wired communication. The noise mitigation characteristic of the femtocell architecture is a result of "microization", a recent network concept in which a large area network is divided into smaller networks, thereby reducing the per base station load .
However, the aforementioned benefits derived from cognitive radios and femtocells may be nonexistent, if not minimal, when implemented separately, due to their respective limitations. In the case of cognitive radio, the spectrum management mechanism of this technology is limited by the user density as well as the communication behavior of primary clients . Contention for spectrum holes also exists due to the large number of secondary clients searching for spectrum holes. In addition, if the utilization of licensed bands is high, the amount of usable bandwidth decreases exponentially as the number of secondary users increases. In contrast, femtocell performance is dependent on the available bandwidth, since the FBSs operate under the same spectrum as MBSs . Upon these considerations, interference mitigation and interference control schemes need to be studied in order to improve cognitive radio performance. In a similar manner, spectrum management schemes should be considered for femtocell implementations. Based on this argument, the integration of cognitive radio and femtocell is expected to be mutually beneficial for these technologies. In addition, we introduce a compensation concept to address the backhaul communication issue for open access femtocells given in .
We propose a system architecture for a cognitive femtocell network architecture that incorporates cognitive radio and femtocells (Section 2).
We propose a joint power control, base station assignment, and channel assignment scheme for cognitive femtocell networks. In this scheme, the power of each node, as well as the selection of the base station to which a node connects, is controlled so as to maximize the aggregate throughput. In addition, we also propose a compensation scheme to compensate femtocell owners for usage of their resources. We also formulated a mixed integer multiobjective optimization model for the proposed schemes and found the global solution using the branch and bound method (Section 3).
Finally, we provided numerical and simulation results to show that the proposed joint power control, base station assignment, and channel assignment scheme for cognitive femtocell networks achieves better performance than conventional architectures (Section 4).
A common femtocell architecture is illustrated in Figure 2. The FBS in this figure communicates with the MBS through the Internet while other means, such as dedicated wireless channels, can be used for connectivity. Coverage enhancement and interference mitigation are two advantages derived from using the femtocell architecture. System coverage is extended since unreachable users can connect to the MBS through the FBSs. On the other hand, FBS installations can also reduce interference in highly dense areas, since private clients do not have to compete for the macrocell network resources . However, different installations of FBSs may interfere with each other, since these base stations usually reuse the same set of frequencies. In a worst-case scenario, such as the apartment setup in Figure 2, massive contention may occur. From this, we can see that the performance of femtocell architectures is limited by the available frequency spectrum . The femtocell architecture benefits from using cognitive radio in that it gains the capability of sensing its environment and can thereupon adapt its configuration based on the sensed information.
The MBS is the central entity of the whole system. As previously mentioned, this base station serves as the primary user of the licensed spectrum and is operated by the service providers. The FBSs are equipped with cognitive radio with spectrum sensing, spectrum management, spectrum sharing, and spectrum mobility functionalities, as defined in . With this feature, each FBS has the ability to sense its environment and change to different channels based on the sensed information. Each FBS connects to the MBS via TDMA slotted communication. Each FBS is given time slots used to forward data to the macrocell network.
In addition, we consider an open access femtocell architecture . In this architecture, femtocells allow connection of public clients in order for these clients to connect to the macrocell network. To support an open access femtocell architecture, a compensation scheme is needed, since the use of an open access femtocell architecture would decrease the effective throughput of private clients. Public clients normally associate with the MBS. In our open access scheme, these public clients may connect to other FBSs under the condition that the FBSs to which these clients connect must be compensated by the MBS. In this paper, these FBSs that accommodate public clients are compensated by being provided additional time slots in backhaul communication. Details of this compensation scheme are given in Section 3. Furthermore, the FBSs may also adjust their power to control their transmission range communication if the MBS requests coverage enhancement and for interference mitigation purposes.
In this paper, we assume that the MBS and FBS use OFDMA-based WiMAX and that resources are assigned based on a frequency-time basis. In addition, each base station is equipped with a single radio and can operate at a single frequency for each time instance. Also, for analysis purposes, its assumed that the cell is on a high-density area and that all clients need to transmit some data.
The problem this paper aims to resolve is the maximization of throughput performance by controlling the BS assignment of public clients, the channel allocation for each BS, and the transmission power. In controlling the power and channel allocation, the interference between clients and BSs is reduced. The BS assignment allows public clients to connect to FBSs subject to compensation from the MBS.
In this paper's model, two objectives are considered. The first objective is to maximize the achievable throughput in the network while the second deals with what we call femtocell compensation. The goal of the whole formulation is to maximize the achievable throughput of the system while minimizing the need for femtocell compensation. In the following subsections, the formulation for uplink and downlink scenarios, as well as the branch and bound method used to solve the scheme, is detailed.
3.1. Uplink Optimization Model
Total number of FBS
Total number of clients
Hardware noise and environmental noise
Maximum backhaul link capacity
Constant parameter for the SINR threshold of MBS
Constant parameter for the SINR threshold of the FBS
Constant parameter for the maximum transmission power of clients
Constant parameter for the maximum transmission power of MBS
Constant parameter for the maximum transmission power of FBS
Weight parameter for the weighted sum approach
Transmission power vector of clients
Transmission power vector of BS
Measured power of client at client using channel
Measured power of BS at BS
Uplink achievable throughput from client to BS using frequency
Downlink achievable throughput from BS to client using frequency
Transmission gain matrix between network elements. Note that index 0 represents
the MBS, indices 1 to represent FBSs, and indices to represent clients
Coverage scheme variable for set of public users associated with MBS
Coverage scheme constant for set of private users associated with an FBS
Coverage scheme variable for set of public users associated with FBS
Uplink channel assignment variable for client using frequency
Downlink channel assignment variable for BS using frequency
Uplink throughput objective function
Distributed uplink throughput objective function at each client
Downlink throughput objective function
Distributed downlink throughput for the MBS
Distributed downlink throughput at each FBS
Uplink compensation objective function
Downlink compensation objective function
Distributed compensation objective function at each client
Distributed downlink compensation objective function at each BS
Weighted sum problem for uplink model
Distributed uplink weighted sum problem
Weighted sum problem for downlink model
Distributed downlink weighted sum problem at the MBS
Distributed downlink weighted sum problem at each FBS
Distributed achievable throughput from client to BS using frequency at each client
Downlink achievable throughput from BS to client using frequency at each BS
As can be seen from (3), the first objective function for throughout maximization is a joint scheme for incorporating power control, BS assignment, and channel assignment. In this objective function, the power control scheme controls the transmission powers of client nodes in order to minimize the contributed interference. In addition, we define BS assignment as a representation of the BS to which these clients connect, that is, if they are connected to any FBSs or the MBS. The association of public clients to the FBSs is subject to the MBS load and interference conditions. Finally, the channel assignment is chosen from the available channels for each node. These parameters are computed for each time frame.
The multiobjective problem is formulated in (5a) to (5g). The first objective (5a) maximizes the aggregate uplink throughput given by (3). The second objective (5b) minimizes the compensation objective given by (4). Note that the compensation objective is minimized, since the ideal case is for public users to connect using the MBS. Also, an increase in the compensation for a certain FBS would result in a decrease in the backhaul rate for other FBSs. There are five major constraints for the formulation in (5a) to (5g). The first constraint (5c) assures that sets A, B, or C are disjoint. Moreover, this implies that each client associates with only one base station. The next two constraints, given by (5d) and (5e), denote the SINR constraints for each base station. The constraint in (5f) states that a client or FBS is assigned only one frequency. Finally, (5g) describes the limit in transmission power for each client in the uplink.
3.2. Downlink Optimization Model
3.3. Joint Power Control, Base Station Assignment, and Channel Assignment Scheme using Branch and Bound Method
Branch and bound method notation summary.
Current upper bound
Lower bound of the problem on set
Conditional gradient method over set
Set of unanalyzed sets
Offspring set of each iteration
Current iteration's parent set
Algorithm 1: Branch and bound method.
( ) BEGIN
( ) Initialize. , , ,
( ) Get Problem from LIVESET;
( ) while do
( ) Branch out sets from and from such that
( ) Prune ;
( ) Compute ,
( ) if then
( ) Append to LIVESET;
( ) Append to LIVESET;
( ) else
( ) Append to LIVESET;
( ) Append to LIVESET;
( ) end if
( ) Get Problem from LIVESET;
( ) if for a feasible solution then
( ) if then
( ) ;
( ) end if
( ) end if
( ) if then
( ) Prune from LIVESET;
( ) Get Problem from LIVESET;
( ) end if
( ) end while
( ) END.
We take a set from LIVESET and branch out to two sets, and , where (line 5). is then pruned from LIVESET, since it has already produced all its offspring (line 6). After pruning , the method decides the order in which and should be inserted into LIVESET (line 7–line 14). To determine the order, the value of the lower bound for each of and is computed. The set whose lower bound is lower is analyzed first. These series of steps are important, because if a solution is found traversing the set a lesser lower bound, then the likelihood of finding a better solution from the other set would be decreased. The set with a lesser lower bound is chosen as (line 15). A check procedure is conducted if the is a feasible solution for the unrelaxed problems in (6a) to (6g) and (10a) to (10h). If this is true, then the upper bound CURR is adjusted to and the current solution is (line 16–line 21). Another check is conducted if the lower bound of is greater than the upper bound CURR, and then no better solution can be found inside the set . is then pruned from LIVESET and the next parent is taken from LIVESET (line 21–24). If both conditions in line 16 and line 21 are not met, then the method branches out and prunes the current . The process repeats until LIVESET is empty.
The method presented in Algorithm 1 is a First-In Last-Out search technique applied to the branch and bound method. As mentioned previously, analyzing sets whose lower bounds are lesser than other sets reduces the probability of the need to analyze the other sets. As such, the computation time can be proven to be between and .
3.4. Distributed Implementation of the Joint Power Control, Base Station Assignment, and Channel Assignment Scheme
From (11) to (13), the uplink joint power control, base station assignment, and channel assignment scheme in (6a) to (6g) can be reformulated as (15) for each client.
Assume that , is the solution for (15) at each . Then , , , .
With the formulation for distributed implementation, the overall optimization problem can be solved by each element in the network. Each element computes the solution to the problem by running Algorithm 1 for each of their distributed problems given in (15), (23), and (24).
A numerical simulation was conducted to explore the consequences of the formulation and to show that the branch and bound method converges to the optimal solution. The numerical analysis is conducted using MATLAB. The numerical analysis scenario consists of one MBS, three FBSs (FBS_A, FBS_B, and FBS_C), one private client for each FBS (MC_1, MC_2, and MC_3, resp.), and randomly distributed public clients. The SINR threshold for the MBS is set at 0.8 while the SINR threshold for the FBSs is set to 0.3. These high SINR threshold values simulate high-density traffic in the area and assure that the FBSs will have to search for available spectrum holes. The MBS power is limited to 1 W while the FBS's power threshold is at 0.5 W.
The depletion of wireless spectrum resources is well documented in the literature. In a work by Staple and Werbach , the authors discussed the current status of the frequency spectrum. In addition, they also reported on current efforts to solve the problem of spectrum scarcity. One of the most recent efforts involves the use of software-defined radios with sensing capabilities, called cognitive radio. Mitola and Maguire  published a paper on cognitive radio in 1999, but it has only recently won attention by researchers as a viable solution to improving spectrum efficiency. A number of studies have been conducted to quantify the benefits of using cognitive radios. Srinivasa and Jafar  carried out a theoretical study on the throughput potential of cognitive radios using different cognitive perspectives. Several studies [15, 16] have also proposed architectures and applications for cognitive radios. The focus of these works was primarily on to how to handle base station hand-offs and decision mechanisms. In addition, research on spectrum management [6, 17, 18], power control , and node coordination  has been reported. In most of these works, the formulations are conducted for the spectrum configuration only. Fully-cognitive radios have generally not been addressed in recent works. Also, the coexistence of several cognitive radios has received little attention as compared to the minimization of interference to primary clients. Although several studies have concentrated on the interference contributions [20, 21], most either focus on cognitive radios or on primary client interference. These two concerns are addressed in our paper along with throughput improvement and coverage. Femtocell architecture [8, 22, 23] has been used to mitigate the noise in cognitive radio communications. Research on femtocells has shown that they are limited by the amount of usable bandwidth in a small area [8, 22]. However, most studies assume that adjacent FBSs would not interfere with each other. This assumption is valid for a moderately dense network. On the other hand, for a dense network, interference is unavoidable. This paper merges the cognitive capabilities with a femtocell architecture to mitigate this drawback. The assumption of noninterfering base stations can now be validated even for the case of dense networks.
This paper focused on the congestion problem in radio frequency transmission. With continuing improvements of wireless technology, the scarcity of spectrum resources has exponentially increased. Nevertheless, most of the radio frequency spectrum bands are not efficiently utilized, especially the licensed bands.
To resolve this problem, recent studies have developed the use of cognitive radio based on Mitola's 1999 proposal. Using cognitive radio, the wasted bandwidth in licensed bands can be used by unlicensed bands as long as the primary licensed users do not utilize them. However, the benefits of using cognitive radios are dependent on the transmission behavior of the primary clients. If no spectrum holes are available, the cognitive clients cannot transmit. To overcome this problem, the use of femtocell a architecture is a good alternative. Femtocells are consumer-installed base stations that enable short-range communication indoors. The advantage of using femtocells is the capacity gain derived from minimizing the range at which they are transmitting.
In this paper, we proposed a cognitive femtocell network architecture that aims to efficiently utilize the radio frequency spectrum while meeting the service requirements of the clients. A joint power control, base station assignment, and channel assignment scheme is derived to efficiently maximize the overall throughput. Results show that significant improvements can be derived in using this architecture as the number of nodes in the network increases. This paper also demonstrated that using a compensation mechanism assures mutual benefits in sharing private clients between femtocell owners and the primary network.
This work was supported by the National Research Foundation of Korea's (NRF) grant funded by the Korean government (MEST) (no. 2009-0074938). They would also like to extend our gratitude to the anonymous reviewers and editor for their valuable comments and suggestions which improved the earlier versions of the paper.
- Staple G, Werbach K: The end of spectrum scarcity. IEEE Spectrum 2004, 41(3):48-52. 10.1109/MSPEC.2004.1270548View ArticleGoogle Scholar
- Cabric D, Mishra SM, Brodersen RW: Implementation issues in spectrum sensing for cognitive radios. Proceedings of the 38th Asilomar Conference on Signals, Systems and Computer, November 2004, Pacific Grove, Calif, USA 1: 772-776.Google Scholar
- Weiss TA, Jondral FK: Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency. IEEE Communications Magazine 2004, 42(3):S8-S14.View ArticleGoogle Scholar
- Golmie N, Mouveaux F: Interference in the 2.4 GHz ISM band: impact on the bluetooth access control performance. IEEE International Conference on Communications (ICC '01), June 2001, Helsinki, Finland 8: 2540-2545.Google Scholar
- Mitola J III, Maguire GQ Jr.: Cognitive radio: making software radios more personal. IEEE Personal Communications 1999, 6(4):13-18. 10.1109/98.788210View ArticleGoogle Scholar
- Akyildiz IF, Lee W-Y, Vuran MC, Mohanty S: A survey on spectrum management in cognitive radio networks. IEEE Communications Magazine 2008, 46(4):40-48.View ArticleGoogle Scholar
- Akyildiz IF, Lee W-Y, Vuran MC, Mohanty S: NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Computer Networks 2006, 50(13):2127-2159. 10.1016/j.comnet.2006.05.001MATHView ArticleGoogle Scholar
- Chandrasekhar V, Andrews JG, Gatherer A: Femtocell networks: a survey. IEEE Communications Magazine 2008, 46(9):59-67.View ArticleGoogle Scholar
- Chandrasekhar V, Andrews J: Uplink capacity and interference avoidance for two-tier femtocell networks. IEEE Transactions on Wireless Communications 2009, 8(7):3498-3509.View ArticleGoogle Scholar
- Kurose JF, Ross KW: Computer Networking: A Top-Down Approach Featuring the Internet. 3rd edition. Pearson Education; 2005.Google Scholar
- Park JM, Kim S-L: Distributed throughput-maximization using the up- and downlink duality in wireless networks. Proceedings of the International Wireless Communications and Mobile Computing Conference (IWCMC '07), August 2007 97-102.Google Scholar
- Bertsekas DP: Nonlinear Programming. 2nd edition. Athena Scientific; 1999.MATHGoogle Scholar
- Li Y, Macuha M, Sousa E, Sato T, Nanri M: Cognitive interference management in 3G femtocells. Proceedings of the 20th Personnal, Indoor and Mobile Radio Communications Symposium (PIMRC '09), September 2009, Tokyo, JapanGoogle Scholar
- Srinivasa S, Jafar SA: The throughput potential of cognitive radio: a theoretical perspective. Proceedings of the 40th Asilomar Conference on Signals, Systems and Computers, October 2006 221-225.Google Scholar
- Ueda T, Takeuchi K, Kaneko S, Nomura S, Sugiyama K: Packet switch and its impact on dynamic base station relocation in mesh networks using cognitive radio. IEICE Transactions on Communications 2008, E91-B(1):102-109. 10.1093/ietcom/e91-b.1.102View ArticleGoogle Scholar
- Ge F, Chen Q, Wang Y, Bostian CW, Rondeau TW, Le B: Cognitive radio: from spectrum sharing to adaptive learning and reconfiguration. Proceedings of the IEEE Aerospace Conference, March 2008 1-10.Google Scholar
- Adamopoulou E, Demestichas K, Theologou M: Enhanced estimation of configuration capabilities in cognitive radio. IEEE Communications Magazine 2008, 46(4):56-63.View ArticleGoogle Scholar
- Quan Z, Cui S, Sayed AH: Optimal linear cooperation for spectrum sensing in cognitive radio networks. IEEE Journal on Selected Topics in Signal Processing 2008, 2(1):28-40.View ArticleGoogle Scholar
- Chen Y, Yu G, Zhang Z, Chen H-H, Qiu P: On cognitive radio networks with opportunistic power control strategies in fading channels. IEEE Transactions on Wireless Communications 2008, 7(7):2752-2761.View ArticleGoogle Scholar
- Geirhofer S, Tong L, Sadler BM: Cognitive medium access: constraining interference based on experimental models. IEEE Journal on Selected Areas in Communications 2008, 26(1):95-105.View ArticleGoogle Scholar
- Neel JO, Menon R, MacKenzie AB, Reed JH, Gilles RP: Interference reducing networks. Proceedings of the 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom '07), August 2007, Orlando, Fla, USA 96-104.Google Scholar
- Claussen H, Ho LTW, Samuel LG: Self-optimization of coverage for femtocell deployments. Proceedings of the 7th Annual Wireless Telecommunications Symposium (WTS '08), April 2008, Ponoma, Calif, USA 278-285.Google Scholar
- Kang SB, Seo YM, Lee YK, et al.: Soft QoS-based CAC scheme for WCDMA femtocell networks. Proceedings of the 10th International Conference on Advanced Communication Technology (ICACT '08), February 2008 1: 409-412.Google Scholar
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