Towards Self-organized Resource Allocation in OFDMA Femtocells
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Minimising Cell Transmit Power: Towards Self-organized Resource Allocation in OFDMA Femtocells David López-Pérez, Xiaoli Chu King’s College London London, UK [email protected] Athanasios V. Vasilakos National Technical University of Athens Athens, Greece [email protected] Holger Claussen Alcatel-Lucent Bell-Labs Dublin, Ireland holger.claussen@alcatel- lucent.com ABSTRACT With the introduction of femtocells, cellular networks are moving from the conventional centralised architecture to a distributed one, where each network cell should make its own radio resource management decisions, while providing inter-cell interference mitigation. However, realising this dis- tributed cellular network architecture is not a trivial task. In this paper, we first introduce a simple self-organisation rule under which a distributed cellular network is able to converge into an efficient resource allocation pattern, then propose a novel resource allocation model taking realistic re- source allocation constraints into account, and finally eval- uate the performance of the proposed self-organisation rule and resource allocation model using system-level simulations. Primary Classification: A. General Literature, A.m MISCELLANEOUS; General Terms: Algorithms; Key- words: Femtocell, Interference, Resource Allocation. 1. BACKGROUND AND MOTIVATION Femtocells, which are low-cost, low-power cellular base stations (BSs) deployed by end-users in homes and offices, have been widely considered as a cost-effective solution to enhance indoor coverage and spectral efficiency in cellular networks. By reducing the distance between BSs and end- users and alleviating the traffic burden on macrocells, femto- cells can potentially improve spatial reuse, allow higher user data-rates and provide energy savings. However, femtocell roll-outs are facing their own technical challenges. For in- stance, since the user-provided backhaul connection between an femto BS and the operator’s core network usually has lim- ited capacity, it is difficult to manage inter-cell interference using classic centralised network planning and optimisation tools. This has led to a new inter-cell interference manage- ment problem that must be solved in a decentralised and distributed manner. However, to achieve good performance across the network with each cell taking its own radio re- source management decisions is an intricate problem [1]. 2. RADIO RESOURCE MANAGEMENT In orthogonal frequency division multiple access (OFDMA) -based networks, e.g., Long Term Evolution (LTE) and Wire- less Interoperability for Microwave Access (WiMAX), the smallest resource unit that can be assigned is a resource block (RB). An RB is comprised of a set of adjacent sub- carriers in the frequency domain and OFDM symbols in the Copyright is held by the author/owner(s). SIGCOMM’11, August 15–19, 2011, Toronto, Ontario, Canada. ACM 978-1-4503-0797-0/11/08. time domain. The scheduling question to be addressed by each cell is how RBs are to be allocated to users and how much transmit power is to be applied to each RB, so that network capacity is enhanced. This radio resource allocation problem is complex because users may have different quality of service demands and may experience various channel and interference conditions in each RB. Moreover, both LTE and WiMAX standards have a scheduling constraints, e.g., when more than one RB are allocated to a user, all these RBs must utilise the same Modulation and Coding Scheme (MCS) [2]. 3. OUR SELF-ORGANISATION RULE For the independent and dynamic optimisation of radio resource assignments at each cell, it is necessary to have an objective that results in a good self-organising behaviour. Accordingly, our proposed cell optimisation rule is defined as: each cell assigns MCSs, RBs and transmit power levels to users independently, while minimising the cell total trans- mit power and meeting its users’ throughput demands. The reasons for minimising the cell total transmit power are two: 1. A cell that aims at minimising its own transmit power mitigates inter-cell interference to neighbouring cells, because less power is allocated to those users with good channel conditions or lower throughput demands. This is straightforward from Shanon-Hartley theorem. 2. A cell that aims at minimising its own transmit power tends to allocate those RBs that are not being used by its neighbouring cells, because less transmit power is required for a less interfered and/or faded RB to get a targeted signal to interference plus noise ratio (SINR). Following this rule, a cell will tend to allocate users that are closer to the BS or have lower data-rate requirements (hence requiring lower MSCs as well as transmit powers) to RBs that are used by cell-edge users in neighbouring cells. 4. RESOURCE ALLOCATION MODEL The transmit power p f u,k,r that femtocell F f should assign to every subcarrier of RB k assign in the downlink to user u to achieve the SINR threshold γ r of its assigned MCS r is p f u,k,r = γ r · w u,k + σ 2 Γ f,u (1) Γ f,u is the channel gain between femto BS F f and user u, w u,k is the inter-cell interference power suffered by user u in RB k, and σ 2 is the noise power. Femtocell F f can ‘know’ Γ m,u and w u,k from its user measurement reports (MRs) [2]. Thus, the joint MCS, RB and power allocation problem in femtocell F f , following our proposed self-organisation rule, can be formulated as the following integer linear problem:
Transcript of Towards Self-organized Resource Allocation in OFDMA Femtocells
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Figure 1: Simulation scenario.
Table 1: System-Level Simulation ResultsDeployed
Scheme Rand. MNL IMOur
users Method
4 users/cellOutage 15.09% 8.62% 2.59% 0.86%
8RB networkTx.-ing Users 31.13 33.11 35.20 35.75
Mbps 7.65 8.24 8.77 8.83
8 users/cellOutage 19.87% 19.77% 15.52% 3.19%
8RB networkTx.-ing Users 58.36 58.59 60.47 69.63
Mbps 14.40 14.48 14.98 16.78
minχu,k,r
U∑u=1
K∑k=1
R∑r=1
pfu,k,r · χu,k,r (2a)
subject to: U∑u=1
R∑r=1
χu,k,r ≤ 1 ∀k (2b)
R∑r=1
ρu,r ≤ 1 ∀u (2c)
χu,k,r ≤ ρu,r ∀u, k, r (2d)
K∑k=1
R∑r=1
Θ · effr · χu,k,r ≥ TP requ ∀u (2e)
ρu,r ∈ {0, 1} ∀u, r (2f)
χu,k,r ∈ {0, 1} ∀u, k, r (2g)
where χu,k,r (2g) is a binary decision variable that is equal to1 if user u uses MCS r in RB k, or 0 otherwise, ρu,r (2f) is abinary decision variable that is equal to 1 if user u makes useof MCS r, or 0 otherwise, constraint (2b) makes sure thatRB k is only assigned to at most one user u, constraints (2c)and (2d) together guarantee that each user is allocated toat most one MCS, and constraint (2e) makes sure that eachuser u obtains a throughput no less than its demand TP req
u .Average channel quality in the form of instantaneous wu,k
averaged over tens of user MRs is utilised to compute pfu,k,r,
thus avoiding rapid fluctuations of pfu,k,r due to fast-fading,which may result in fast variations of resource assignments.One possible approach to solve (2) is to apply integer lin-
ear programming (ILP) techniques readily available in soft-ware packages. However, although ILP solvers are able tosolve (2) up to optimality, their running times are unpre-dictable (exponential in the worst case), which renders theminappropriate for real-time use at femto BSs. Hence, to solve(2) in short time, we propose a two-level decomposition ap-proach based on an heuristics search and a minimum costnetwork flow problem. Assuming that a MCS ru has been as-signed to user u ∀u, i.e., ρu,r∀u∀r is known a priori as part ofthe input, (2) can be reduced to a simpler form, i.e., a user-
to-RB assignment problem weighted by pfu,k,r∀u∀k. Thisassignment problem can be formulated as a minimum costnetwork flow problem, an optimally solved using a networksimplex algorithm. Thereafter, a heuristic search can beperformed over the MCS assignment solution space, where
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Figure 2: RB allocation.
for each MCS assignment to cell users, the optimal RB andpower allocation can be found by using our network sim-plex algorithm. Since network simplex runs fast, RB andpower allocations to users can be updated on a millisecondbasis (dealing with fast channel variations), while the MCSassignment can be updated in a half-second basis (dealingwith traffic load and mobility).
5. PERFORMANCE AND CONCLUSIONSThe performance of the proposed self-organisation rule
has been investigated in an enterprise femtocell scenario(Fig. 1). Simulation results show that compared to exist-ing radio resource management techniques in the literature,i.e., random [3], network listening mode (NLM) [4] and in-terference minimisation (IM) [5], our approach is able tosignificantly decrease the number of user outages, increasethe average number of simultaneously transmitting users inthe network (around 15%), and enhance the average networksum throughput (around 12%) (Tab. 1). This is because ourapproach (self-organization rule) achieves inter-cell interfer-ence coordination without the need of assigning orthogonalRBs among neighbouring cells. Instead, it allows all cells toallocate all RBs to their users in an intelligent manner: A cellthat minimises its own transmit power assigns less power tothose RBs allocated to users having good channel conditionsor with lower data-rate demands. Thus, neighbouring cellswill ‘see’ low interference in such RBs and will allocate themto users having bad channel conditions or with large data-rate demands, thereby improving spatial reuse. To illustratethis self-organising feature, Fig. 2 shows the transmit powerallocated by 3 neighbouring femtocells at a given time in8 available RBs. We can see that each femtocell tends toallocate higher power levels in RBs in which neighbouringfemtocells assign lower power levels and vice versa. In thisway, a same RB can be dynamically reused in neighbouringfemtocells. Therefore, our proposed sefl-organisation ruleand resource allocation model effectively introduce an im-plicit coordination among independently operating femto-cells through distributed dynamic radio resource allocations.
6. REFERENCES[1] V. Chandrasekhar et. al. Femtocell Networks: A
Survey. IEEE Comm. Mag., 46(9):59–67, Sep. 2008.
[2] E. Dahlman et. al. 3G Evolution. HSPA and LTE forMobile Broadband. Elsevier, 2 edition, Aug. 2008.
[3] V. Chandrasekhar et. al. Spectrum Allocation in TieredCellular Networks. IEEE Transactions onCommunications, 57(10):3059–3068, Oct. 2009.
[4] J. Ling et. al. On Resource Allocation in DenseFemto-Deployments. In IEEE COMCAS, Nov. 2009.
[5] J. Zhang et. al. Femtocells: Technologies andDeployment. John Wiley and Sons, Jan. 2010.
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