Centralized coordinated scheduling in LTEAdvanced networks
 Oscar D. RamosCantor^{1}Email author,
 Jakob Belschner^{2},
 Ganapati Hegde^{1} and
 Marius Pesavento^{1}
https://doi.org/10.1186/s1363801709045
© The Author(s) 2017
Received: 13 February 2017
Accepted: 22 June 2017
Published: 11 July 2017
Abstract
This work addresses the problem associated with coordinating scheduling decisions among multiple base stations in an LTEAdvanced downlink network in order to manage intercell interference with a centralized controller. To solve the coordinated scheduling problem, an integer nonlinear program is formulated that, unlike most existing approaches, does not rely on exact channel state information but only makes use of the specific measurement reports defined in the 3GPP standard. An equivalent integer linear reformulation of the coordinated scheduling problem is proposed, which can be efficiently solved by commercial solvers. Extensive simulations of medium to largesize networks are carried out to analyze the performance of the proposed coordinated scheduling approaches, confirming available analytical results reporting fundamental limitations in the cooperation due to outofcluster interference. Nevertheless, the schemes proposed in this paper show important gains in average user throughput of the celledge users, especially in the case of heterogeneous networks.
Keywords
4G mobile communication Scheduling algorithms Integer linear programming1 Introduction
Interference is one of the main limiting factors of today’s cellular communication networks in terms of user and network throughputs, especially when operating with full frequency reuse to achieve high spectral efficiency [1–4]. In modern cellular networks, the demand for high data rates is constantly increasing [5], with the users expecting to enjoy excellent network performance irrespective of their geographic location and the load conditions of the network. Thus, new solutions are required in order to fulfill the ever increasing requirements, in particular for the users located at the celledge suffering from large path loss and strong intercell interference. Promising advances in this aspect have been made with multiantenna technology [6–9], network densification with interference management schemes [10–12], and Coordinated MultiPoint (CoMP) transceiver techniques [13, 14].
In this work, CoMP network operation is studied, where the base stations (BSs) are prompted to cooperate with each other with the objective of improving the overall network performance, even at the expense of their individual cell or user throughputs [15]. In the literature, three main CoMP schemes are considered for the downlink scenario [16, 17]. These are (i) Joint Transmission (JT), where multiple BSs simultaneously transmit a common message to a user equipment (UE), usually located at the celledge, (ii) Dynamic Point Selection (DPS), where at each transmission time, the UE can be served by a different BS without triggering handover procedures, and (iii) Coordinated Scheduling (CS), where the BSs jointly make the scheduling decisions in order to manage the interference experienced by the UEs in the cooperation cluster [18]. This paper focuses on the last CoMP scheme.
The performance of the abovementioned CoMP schemes heavily depends on the channel state information (CSI) available at the transmitter. This CSI can be of different types such as instantaneous channel coefficients or user’s average achievable downlink data rates, among others. In practical downlink networks, where perfect global knowledge of the instantaneous channel coefficients is not available at the BSs, CSI is typically obtained in form of achievable data rate measurement reports generated by the UEs, averaged over multiple time/frequency/space dimensions and quantized to reduce the signaling overhead. In this work, the CoMP problem formulation is based on practical considerations of the CSI, in form of periodic achievable data rate measurement reports, in the following referred to as CSI reports.
The network architecture, in which the CoMP schemes are implemented, also influences the performance of such schemes. Two main CoMP network architectures are typically considered, namely, centralized and decentralized [19]. In the case of centralized CoMP, a central controller is connected to multiple BSs via backhaul links. This central controller is in charge of gathering and using the CSI reports, in order to make a coordinated decision among the connected BSs. For the decentralized CoMP case, decisions are individually made by each BS based on the information exchanged with neighboring BSs. In the case of centralized CoMP, high coordination gains are achievable at the expense of high computational complexity and large signaling overhead. On the other hand, decentralized CoMP requires significantly less information exchange with lower coordination gains. This work focuses on centralized CoMP.
Over the past years, important research has been carried out regarding CoMP schemes under different network architectures and CSI assumptions. In [20] and [21], JT and DPS schemes based on the enhanced CSI reports supported by Long Term Evolution (LTE)Advanced Release 11, in the following denoted as \(\text {CSI}_{\text {lte}}^{\text {R11}}\) reports, have been investigated. The results therein show throughput gains for the celledge users mainly, and the possibility to improve mobility management by means of DPS. Barbieri et al. studied CS as a complement of enhanced intercell interference cancellation (eICIC) in heterogeneous networks in [22]. In their scheme, cooperation takes place in form of CS supported by beamforming in order to mitigate the interference caused by the macro BSs, to the UEs connected to the small cells. Multiple CSI reports are generated, where all possible precoders the macro BS can select from a finite precoder codebook are considered for the cooperation. The results present negligible gain for eICIC with CS, in comparison to eICIConly. In [23], a cloudradio access network (CRAN) architecture is used for centralized CoMP JT in heterogeneous networks, which enables the cooperation of larger cluster sizes. In that case, gains over eICIConly are observed, especially for large cluster sizes. Authors in [24] propose centralized and decentralized CoMP CS schemes that utilize \(\text {CSI}_{\text {lte}}^{\text {R11}}\) reports, in which muting is applied to one BS at a time. A BS is called muted if it does not transmit data on a specific time/frequency resource. It has been shown that under this muting condition, both centralized and decentralized schemes achieve the same performance, favoring the decentralized scheme due to the reduced information exchange. Moreover, in [25], the authors extend the cooperation scheme of [24], to introduce muting of more than one BS per scheduling decision in a larger network. A greedy CS algorithm is presented to solve the centralized problem, which yields limited additional gain with respect to the decentralized scheme with overlapping cooperation clusters. The coordination scheme of [25] consists in a greedy optimization procedure. It is therefore suboptimal and further investigation regarding the optimally achievable performance of coordination, in the case of \(\text {CSI}_{\text {lte}}^{\text {R11}}\) reports, has not been carried out. Additionally, the results are focused on macroonly networks, where the gains of cooperation are restricted due to similar interfering power levels experienced from multiple BSs.

The CS problem, where BSs cooperate by muting time/frequency resources based on standardized \(\text {CSI}_{\text {lte}}^{\text {R11}}\) reports, is formulated as an integer nonlinear program (INLP).

The nonlinear CS with muting problem is reformulated into a computationally tractable equivalent integer linear program (ILP), which enjoys of low computational complexity and can be efficiently solved by commercial solvers.

A configurable heuristic algorithm is proposed as an extension to the greedy algorithm in [25], which achieves an excellent tradeoff between performance and computational complexity.
2 System model
characterizing the serving conditions between BSs and UEs, where it is assumed that only one BS serves UE n over all PRBs. It is further assumed, for simplicity, that the UEs are quasistatic, such that no handover procedures are triggered between the BSs. Therefore, the connection matrix C is assumed to be constant during the considered operation time.
The set of indices of BSs within the cooperation cluster that interfere with UE \({n\in \mathcal {N}}\) is defined as \({\mathcal {I}_{n}=\{m~~c_{n,m}=0,~\forall m\in \mathcal {M}\}}\), with cardinality \({\mathcal {I}_{n}=M1}\). Moreover, since UE n experiences different interfering power levels from the \(\mathcal {I}_{n}\) interfering BSs, the set \(\mathcal {I}_{n}' \subseteq \mathcal {I}_{n}\) of indices of the M ^{′} strongest interfering BSs of UE n, is defined such that \(\left \mathcal {I}_{n}'\right =M'\). Therefore, the set \(\mathcal {I}_{n}'\) contains the indices of the M ^{′} interfering BSs with the highest total received power at UE n, as calculated in (2). The number of strongest interfering BSs is bounded as 0≤M ^{′}≤M−1, where the sets \(\mathcal {I}_{n}'\) and \(\mathcal {I}_{n}\) are identical, if M ^{′}=M−1. The sets \(\mathcal {I}_{n}\) and \(\mathcal {I}_{n}'\) apply for all PRBs in the reporting period.
\(I_{n,l}^{\text {oc}}\) is the average outofcluster interference, and σ ^{2} is the noise power assumed, without loss of generality, to be constant for all UEs over all PRBs. It is worth noting that the outofcluster interference \(I_{n,l}^{\text {oc}}\) is assumed to be independent of the muting decision matrix \(\bar {\boldsymbol {\alpha }}\), since the central controller is not aware of the muting decisions made by the BSs outside of the cooperation cluster.
where f(γ _{ n,l }) denotes a mapping from the SINR of UE n on PRB l, to the achievable data rate.
In the literature, it is common to assume that the central controller has perfect CSI knowledge, in form of the instantaneous channel coefficients, h _{ n,m,l }, as introduced in (1). Thus, the computation of the SINR, \(\gamma _{n,l}\left (\bar {\boldsymbol {\alpha }}_{l}\right)\), and the achievable data rates, r _{ n,l }, as defined in (5) and (7), respectively, is carried out in a straightforward manner for any possible muting decision \(\bar {\boldsymbol {\alpha }}\). However, in practical conditions such as in LTEAdvanced networks, the CSI is typically available in form of achievable data rate measurement reports, i.e., CSI reports, which contain average information of multiple time/frequency/space resources for a subset of possible muting decisions \(\bar {\boldsymbol {\alpha }}\), as defined in (4). Thus, the processing and signaling overhead is reduced, at the expense of limited CSI knowledge for the CoMP CS scheme.
3 CSI reporting for LTEAdvanced CoMP CS
To enable opportunistic scheduling and CoMP operation in LTEAdvanced, CSI estimation is supported by the transmission of CSI reference signals (CSIRSs) from the BSs [29, 30]. One main feature of the CSIRSs is the possibility to configure muted CSIRSs, i.e., CSIRSs with zero transmission power, enabling the UEs to estimate CSI from specific neighboring BSs without interference from the serving BS. Therefore, the UEs can generate multiple \(\text {CSI}_{\text {lte}}^{\text {R11}}\) reports that reflect different serving and interfering conditions in the network [20, 21]. These \(\text {CSI}_{\text {lte}}^{\text {R11}}\) reports are typically composed of a channel quality indicator (CQI), a precoding matrix index (PMI), and a rank indication (RI) [31]. The CQI reflects the measured/estimated SINR and the corresponding achievable data rate of a UE, when assuming a downlink transmission of rank indicated by the RI and transmit precoding vector taken from a finitelength codebook as indexed by the PMI.
For a cooperation cluster with M BSs, a total of J=2^{ M }−1 muting decisions can be made per PRB \({l\in \mathcal {L}}\). In this work, the practical case is considered, where the CS operation is managed by the central controller based on the \(\text {CSI}_{\text {lte}}^{\text {R11}}\) reports provided by the UEs. Since the SINRs and achievable data rates of UE \({n\in \mathcal {N}}\), as defined in (5) and (7), respectively, are dominated by its strongest interfering BSs [24], in the following, it is assumed that the UEs generate a total of \(J'=2^{M{\prime }} \text {CSI}_{\text {lte}}^{\text {R11}}\) reports per PRB, with J ^{′}<J. Then, each \(\text {CSI}_{\text {lte}}^{\text {R11}}\) report only considers the M ^{′} strongest interfering BSs of UE n, as described by the set \(\mathcal {I}_{n}'\), introduced in Section 2.
i.e., \(\phantom {\dot {i}\!}\alpha _{n,m^{\prime },l,j}=1\), if the (strongest) interfering BS m ^{′}, is muted on PRB l, under interference scenario j. The definition in (8) considers only the set of strongest interfering BSs of UE n, i.e., \(\mathcal {I}_{n}'\). Therefore, a constant muting state of the remaining BSs in the cooperation cluster is required, for all \(\mathcal {J}'\) interference scenarios. In the following, it is assumed without loss of generality that α _{ n,m,l,j }=0, \(\forall m\notin \mathcal {I}_{n}', \forall j\in \mathcal {J}'\). Although the definition of the muting pattern in (8) is similar to the definition of the muting decision in (4), the two concepts are different. The muting pattern describes the assumed muting conditions during the generation of the \(\text {CSI}_{\text {lte}}^{\text {R11}}\) reports for the different interference scenarios, while the muting decision is imposed by the central controller, to the BSs within the cooperation cluster, as the result of the implementation of the CS with muting scheme.
Furthermore, the outofcluster interference and the noise powers are also assumed to be constant terms among all the J ^{′} interfering scenarios considered in the \(\text {CSI}_{\text {lte}}^{\text {R11}}\) reports.
To complete the information for the \(\text {CSI}_{\text {lte}}^{\text {R11}}\) reports, r _{ n,l,j } denotes the achievable data rate of UE \({n\in \mathcal {N}}\), on PRB \({l\in \mathcal {L}}\), under interference scenario \(j\in \mathcal {J}'\). The calculation of r _{ n,l,j } follows the definition in (7), as a function of γ _{ n,l,j } given by (9). In this work, it is assumed that the UEs reflect their achievable data rates for the interference scenarios \(j\in \mathcal {J}'\), into the CQI values to be reported in the \(\text {CSI}_{\text {lte}}^{\text {R11}}\) reports. Additionally, it is further assumed that the BSs can translate the CQI values from the \(\text {CSI}_{\text {lte}}^{\text {R11}}\) reports, into the corresponding achievable data rates in order to support the cooperative scheduling process. Therefore, in the following, the description is focused on the achievable data rates extracted from the \(\text {CSI}_{\text {lte}}^{\text {R11}}\) reports.
By inspecting (9) and (10), the SINR of UE \({n\in \mathcal {N}}\), on PRB \({l\in \mathcal {L}}\), increases when muting additional interfering BSs. Correspondingly, the achievable data rate of UE n, on PRB l, increases or remains constant with the increased SINR, if the function f(γ _{ n,l,j }) is nondecreasing.
Example 1
\(\text {CSI}_{\text {lte}}^{\text {R11}}\) reports for UE n, on PRB l, with M ^{′}=2
Int. scenario  Mut. ind.  Mut. pattern  Achiev. 

\((j\in \mathcal {J}')\)  \((\mathcal {J}_{n,j})\)  (α _{ n,l,j })  data rate 
1  \(\{\varnothing \}\)  [0,0,0,0]  r _{ n,l,1} 
2  {1,2}  [1,1,0,0]  r _{ n,l,2} 
3  {1}  [1,0,0,0]  r _{ n,l,3} 
4  {2}  [0,1,0,0]  r _{ n,l,4} 
4 CS with muting
4.1 Proposed INLP—problem formulation
is used to denote the scheduling decision for all UEs on each PRB l, while the M×L matrix variable \(\bar {\boldsymbol {\alpha }}\), with elements as introduced in (4), refers to the muting decision for all BSs on each PRB l. In the following, an INLP is proposed, to carry out joint BS muting and UE scheduling in a coordinated network.
where g(·) corresponds to a function of the achievable data rates of UE n, denoted by r _{ n,l,j }, over the PRBs assigned to UE n, as described by the nth row of \(\bar {\mathbf {S}}\), denoted by \(\bar {\boldsymbol {s}}_{n}\), and the muting decision matrix \(\bar {\boldsymbol {\alpha }}\).

No UEs are connected to BS m, i.e., c _{ n,m }=0, \({\forall n\in \mathcal {N}}\).

PRB l is not assigned to any UE served by BS m, i.e., \(\bar {s}_{n,l}=0,~\forall n\in \mathcal {N}~\text {such that}~c_{n,m}=1\).
In (16), \(\rho \left (\boldsymbol {r}_{n,l},\bar {\boldsymbol {\alpha }}_{l},\mathcal {I}_{n}'\right)\) is a lookup table function that selects the achievable data rate of UE n, on PRB l, based on the muting decision \(\bar {\boldsymbol {\alpha }}_{l}\) of the strongest interfering BSs of UE n, as indexed by \(\mathcal {I}_{n}'\). The lookup table function ρ(·) selects the achievable data rate from the J ^{′}×1 vector, r _{ n,l }, with elements \(r_{{n,l,j}},~\forall j\in \mathcal {J}'\), obtained from the \(\text {CSI}_{\text {lte}}^{\text {R11}}\) reports of UE n, on PRB l.
Example 2
Lookup table function \(\rho \left (\boldsymbol {r}_{n,l},\bar {\alpha }_{l},\mathcal {I}_{n}'\right)\) for UE n, on PRB l, with M ^{′}=2
\(\bar {\alpha }_{m,l},~\forall m\in \mathcal {I}_{n}'\)  \(\rho \left (\boldsymbol {r}_{n,l},\bar {\alpha }_{l},\mathcal {I}_{n}'\right)\) 

[0,0]  r _{ n,l,1} 
[0,1]  r _{ n,l,4} 
[1,0]  r _{ n,l,3} 
[1,1]  r _{ n,l,2} 
Due to the utilization of the \(J'\text {CSI}_{\text {lte}}^{\text {R11}}\) reports, the achievable data rate of UE \({n\in \mathcal {N}}\), on PRB \({l\in \mathcal {L}}\), under interference scenario \(j\in \mathcal {J}'\), is constant in the problem formulation and limited to the set of reported muting patterns. In Section 3, it has been mentioned that the achievable data rate of UE n, on PRB l, under interference scenario j depends on the function f(γ _{ n,l,j }). If f(·) is piecewise nondecreasing, it follows that additional (strongest) interfering BSs are only muted if the achievable data rate of UE n is increased.
Moreover, the scheduling and muting matrix variables, \(\bar {\mathbf {S}}\) and \(\bar {\boldsymbol {\alpha }}\), are binary as described by the constraints in (15d) and (15e), respectively.

As mentioned in Section 3, given M ^{′} strongest interfering BSs per UE \({n\in \mathcal {N}}\), a total of \(\phantom {\dot {i}\!}{J'=2^{M^{\prime }}}\) interfering scenarios per UE n are available. Hence, two special cases of the problem formulation are observed:

(i) If M ^{′}=0, each UE n generates one \(\text {CSI}_{\text {lte}}^{\text {R11}}\) report under the assumption of no BS muting. At the central controller, the CS with muting problem formulation becomes a PF scheduler without any cooperation.

(ii) If M ^{′}=M−1, all the interfering BSs within the cooperation cluster can be muted to improve the performance of any UE, on each PRB \({l\in \mathcal {L}}\). If the network size is large, finding the solution while assuming cooperation of all interfering BSs for all UEs approximates an exhaustive search.

Due to the combinatorial nature of the problem formulation, it is classified as nondeterministic polynomialtime (NP)hard.

The problem is nonlinear because of the relation between the muting and the scheduling decision variables, \(\bar {\boldsymbol {\alpha }}_{l}\) and \(\bar {s}_{n,l}\), respectively, in the constraints in (15c).
Although the number of reported interference scenarios \(\phantom {\dot {i}\!}J'=2^{M^{\prime }}\) can be limited by selecting a small value M ^{′} of (strongest) interfering BSs per UE \({n\in \mathcal {N}}\), the CS with muting INLP formulation in (15) also depends on the number of UEs, i.e., N, and the number of PRBs, denoted by L. For certain network scenarios, N and L can be large. Therefore, given the nonlinear nature of the problem in (15), finding a solution with commercial solvers may either not be possible or inefficient in terms of computation time. In the following, separability, reducibility, and lifting concepts are used, in order to formulate parallel ILP subproblems that scale better with the network size.
4.2 Proposed ILP—parallelized subproblem formulation
4.2.1 Separability
When analyzing the objective function described by (15a), the total PF metric corresponds to the sum of the individual PF metrics for all UEs. Furthermore, at each UE \({n\in \mathcal {N}}\), it is assumed that the total instantaneous achievable data rate is equivalent to the linear combination of the decoupled achievable data rates per scheduled PRBs, as given by (15c). Therefore, it is possible to separate the CS with muting problem in (15), into L independent subproblems, corresponding to the scheduling decision of one PRB each. By performing this parallelization, the computation time is reduced without affecting the quality of the solution, i.e., the solution of the parallelized CS with muting problem remains optimal.
4.2.2 Reducibility
The cardinality of the set \(\mathcal {N}_{m,l}'\), denoted as \(\mathcal {N}_{m,l}'\), is bounded as \({1\leq \mathcal {N}_{m,l}'\leq \sum _{n\in \mathcal {N}}c_{n,m}}\), where the lower bound implies that only one UE provides the maximum PF metric, among all unique muting indicator sets on PRB l. The upper bound corresponds to the case when each UE reports different muting indicator sets with respect to the other UEs connected to BS m.
At the central controller, all the achievable data rates, \(r_{{n,l,j}}, \forall n\in \mathcal {N}_{m,l}', \forall m\in \mathcal {M}, \forall l\in \mathcal {L}, \forall j\in \mathcal {J}'\), are per definition set to zero, for the interference scenarios where UE n does not provide the maximum PF metric, among the UEs connected to the same BS m. The set \(\mathcal {N}_{l}'=\underset {m\in \mathcal {M}}{\cup }\ \mathcal {N}_{m,l}'\) is used to denote the indices of UEs to be considered in the reformulated ILP, on PRB l. The cardinality of the set \(\mathcal {N}_{l}'\) is described as \(M\leq \mathcal {N}_{l}'\leq N\). In the special case of M ^{′}=0, all UEs report only one interference scenario where no cooperative interfering BS is muted, and thus, \(\mathcal {N}_{l}'=M\).
4.2.3 Lifting
with ∧ denoting the logical and operator. Hence, the nonlinear constraints in (15c) reduce to a linear combination of the achievable data rates, i.e., r _{ n,l,j }, and the new decision variable, s _{ n,l,j }.
4.2.4 Problem reformulation
where the objective in (22a) is to maximize the sum of the PF metric over all UEs. The constraints in (22b) restrict the scheduling decisions of the strongest interfering BSs of UE \({n\in \mathcal {N}_{l}'}\), i.e., \(\forall m\in \mathcal {J}_{n,j}\), in order to agree with the muting state considered in the interference scenario \(j\in \mathcal {J}'\). If PRB l is assigned to UE n, under the condition of muting the (strongest) interfering BSs indexed by the set \(\mathcal {J}_{n,j}\in \mathcal {J}_{n}\), then no other UE connected to the muted BSs can be simultaneously scheduled on the same PRB l. Thus, if s _{ n,l,j }=1 in (22b), the second term on the lefthandside must be equal to zero. Furthermore, in the case that s _{ n,l,j }=0, the constraints in (22b) ensure that single user transmissions are carried out, where each BS \({m\in \mathcal {J}_{n,j}}\) is allowed to schedule a maximum of one UE per PRB, over all possible interference scenarios \(j\in \mathcal {J}'\). Since it is possible that specific BSs, within the cooperation cluster, do not belong to the set of strongest interfering BSs of any UE, the constraints in (22c) complement the restriction on the single user transmissions from (22b). Additionally, the total instantaneous achievable data rate of UE n, on PRB l, denoted by r _{ n,l }, is calculated in (22d) as the achievable data rate for the selected interference scenario j, as defined by the coordinated decision variable s _{ n,l,j }. It is worth noting that there is a onetoone mapping between r _{ n,l,j } and s _{ n,l,j }, thus, there is no requirement for a lookup table function as used in (16). Furthermore, the constraints in (22e) are incorporated as a preprocessing step to ensure that no PRB is scheduled to UEs for which a maximum PF metric for the corresponding interference scenario j is not available. Finally, the coordinated decision variable S _{ l } is binary as described by the constraints in (22f).
It can be easily proven that the problem formulations in (15) and (22) are equivalent. Furthermore, the proposed parallelized formulation in (22), reduces significantly the CS with muting problem complexity, allowing its application even for largesize networks.
4.3 Generalized greedy heuristic algorithm
The greedy heuristic deflation algorithm in [25] (see algorithm in Section II) iteratively solves the CS with muting problem per PRB \({l\in \mathcal {L}}\), where at each iteration one BS is muted, corresponding to the BS \({m\in \mathcal {M}}\) that, when muted, maximizes the sum of the PF metrics among all UEs on PRB l. The algorithm stops when muting any additional BS does not improve the sum of the PF metrics with respect to the previous iteration. There is no guarantee that the heuristic algorithm yields a globally optimal point because the quality of the scheduling decision depends directly on the gain achieved from muting one interfering BS at a time.
defines the muting patterns to be evaluated. In (23), the binomial coefficients of the set \(\mathcal {M}'\), of possible muted BSs, are evaluated by selecting \(\hat {m}\) BSs at a time. The configuration parameter \({1\leq \tilde {m}\leq M1}\), controls the complexity of the proposed generalized greedy heuristic algorithm by determining the muting patterns to be evaluated. If \(\tilde {m}=1\), the generalized greedy heuristic algorithm reduces to the heuristic algorithm from [25]. In the case that \(\tilde {m}=M1\), the generalized greedy heuristic algorithm performs an exhaustive search.
5 Simulation results
In this section, extensive simulation results are presented to evaluate the performance of the CoMP CS schemes with respect to a PF scheduler without any cooperation, referred to as “noncoop. PFS”. The proposed parallelized subproblem formulation as presented in Section 4.2, labeled as “CSILP”, is examined together with the greedy algorithm described in [25], denoted as “CSGA”, and the proposed generalized greedy algorithm of Section 4.3, labeled as “CSGG”. In the simulations, M ^{′}=2 strongest interfering BSs per UE are considered.
5.1 CS with muting—performance analysis
with β=0.97, denoting the forgetting factor parameter used to trade off user throughput and fairness [38]. The total instantaneous achievable data rate of UE n, at the previous transmission time, denoted by r _{ n }(t−1), is calculated as given by e.g., (22d).
Initially, the performance of the CS with muting algorithms in terms of average user throughput, is studied with respect to the data rates the users can achieve per symbol and to the noise power level considered in the calculation of these achievable data rates. In practical systems such as LTEAdvanced, finite modulation and coding schemes (MCSs) are used which restrict the achievable data rates per symbol to a given range [39, 40]. For the current analysis, two cases are considered with respect to the maximum achievable data rate: (i) the MCS is unbounded, denoted as “Unb. MCS”, and (ii) a maximum achievable data rate of 5.4 bits/symbol is used, referred to as “B. MCS”. Similarly, there are two assumptions with respect to the noise power level, where in a first case, noise free decoding is assumed, denoted as “N.less”, which considers that σ ^{2}=ε, with ε arbitrarily small but larger than zero, and in a second, a typical receiver noise figure of 9 dB is considered, referred to as the “Noisy” case.
Average percentage of muted resources per BS
Scheduling  Noncoop.  CS  CS  CS 

scheme  PFS  ILP  GA  GG 
Unb. MCS & N.less  0  0.67  0.53  0.67 
Unb. MCS & Noisy  0  0.22  0.21  0.22 
B. MCS & N.less  0  0.08  0.08  0.08 
B. MCS & Noisy  0  0.08  0.07  0.08 
5.2 CS with muting—potential gains
In this section, system level simulation results are presented in order to demonstrate the achievable gains of the CS with muting schemes for LTEAdvanced macroonly and heterogeneous networks in an urban deployment. In both cases, N=630 UEs are served over L=10 PRBs, by M=21 BSs in the macroonly network and M=42 BSs in the heterogeneous case where, one pico cell is located within the coverage area of a macro BS with a separation distance of 125m from the macro BS. The UEs are uniformly distributed in the macroonly case, while in the heterogeneous network the UEs are located in a hotspot fashion, where 2/3 of the UEs are deployed in the vicinity of the pico BSs. In the heterogeneous networks cell range expansion is used with a SINR offset of 6dB for the small cells. The outofcluster interference is modeled using the wraparound technique [41], where additional BSs are deployed surrounding the M BSs of interest. Additionally, \(\text {CSI}_{\text {lte}}^{\text {R11}}\) reporting with periodicity of 5ms is applied. Full buffer conditions, ideal link adaptation and rank one transmissions are assumed, i.e., all users are always active and demand as much data as possible, there are no decoding errors and only transmit beamforming is applied, respectively. For more information on 3GPPcompliant system level simulations, including channel and pathloss models, the interested reader is referred to [29] (See 3GPP Case 1 and Case 6.2 from Section A.2.1).
Average percentage of muted resources
Network  CSILP  CSGA  CSGG 

Macroonly  0.11  0.10  0.10 
Heterogeneous  0.13  0.08  0.09 
It is worth to remark that the performance of equivalent decentralized CoMP CS schemes is expected to be upper bounded by their centralized counterparts, as shown in [25]. Hence, the results provided in this work give a reference of the maximum expected performance for the decentralized schemes. A decentralized CoMP CS scheme is proposed and compared with the results in the paper at hand in [42].
Finally, focusing on the proposed parallelized CSILP, it is recognizable that the simplifications proposed in Section 4.2, enable the implementation of such a CS with muting approach even for medium to largesize networks. Hence, instead of solving the CS with muting problem by considering the total of N=630 UEs per PRB \({l\in \mathcal {L}}\), only \(\mathcal {N}_{l}'=136\) and \({\mathcal {N}_{l}'=213}\) UEs were included in average for the macroonly and the heterogeneous network, respectively. That implies a reduction of 78 and 66% in the problem size, for each of the cases, respectively.
6 Conclusions
In this paper, the coordinated scheduling with muting problem in the framework of LTEAdvanced networks with a centralized controller has been studied. A novel integer nonlinear program formulation has been proposed to solve the problem optimally, where a computationally efficient equivalent integer linear program reformulation has been proposed to extend the applicability of the derived scheme even to largesize networks.
Extensive system level simulation results show that coordinated scheduling with muting can potentially improve the celledge user performance, with higher gains in heterogeneous networks. Nevertheless, these gains are limited by the remaining uncoordinated interference and the finite time/frequency/space resources to be shared in the network.
The evaluation of the proposed integer linear program formulation, as well as the stateoftheart heuristic greedy algorithm, for alternative traffic models in the nonfull buffer case, are recommended for future studies. In the case of low demand, the possibility of reducing residual interference and increasing the degrees of freedom for the cooperation can further enhance the performance gains of the mentioned coordinated scheduling schemes.
Declarations
Acknowledgments
We acknowledge support by the German Research Foundation and the Open Access Publishing Fund of Technische Universität Darmstadt.
Competing interests
The authors declare that they have no competing interests.
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Authors’ Affiliations
References
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