Areaclassified interference coordination for heterogeneous cellular network
 Yongming Huang^{1, 2}Email author,
 Shiwen He^{1},
 Shi Jin^{1},
 Luxi Yang^{1},
 Lei Jiang^{3} and
 Ming Lei^{3}
https://doi.org/10.1186/16871499201430
© Huang et al.; licensee Springer. 2014
Received: 13 May 2013
Accepted: 9 February 2014
Published: 21 February 2014
Abstract
In this paper, we aim to address the crosslayer interference in the heterogenous cellular network (HetNet). In order to exploit the overlapping characteristics of the HetNet coverage and achieve a good tradeoff between the interference coordination gain and the cost, an areaclassified interference coordination strategy is first proposed. The basic idea is that coverage of the HetNet is classified into four different areas such that areaspecific interference coordination can be used to increase the crosslayer cooperation efficiency. A new steepest slope method based on relative cooperation gain is proposed to realize efficient area classification. Then, a coordinated beamforming scheme based on areaspecific limited feedback is proposed to examine the effectiveness of this new strategy. It is shown that the proposed scheme could increase the success rate of user pairing and thus improve the throughput performance, with reduced feedback overhead in contrast to existing schemes. Its effectiveness is finally verified via numerical simulations.
Keywords
Heterogenous cellular network (HetNet) Crosslayer interference Coordinated beamforming Limited feedback1 Introduction
Driven by the development of new wireless user equipments (UEs) and the proliferation of bandwidthintensive applications, traffic load in cellular networks will increase in an explosive manner. The use of conventional cellular network framework is difficult to meet the new demands. To solve this issue, recently, a new framework called heterogeneous network (HetNet) has emerged as a flexible and costeffective solution[1–4]. It is realized by overlaying lowerpower access points such as relay node, picocell base station (BS), femtocell BS, and remote radio head (RRH) in the coverage of macrocell[5–7]. It is shown in[8] that the integration of the crosslayered macrocells and femtocells promises to significantly improve the area spectral efficiency of cellular network. Recently, the distribution of the achievable signaltointerferencenoise ratio (SINR) of the HetNet is derived in[9], and the achievable throughput of the HetNet is also analyzed in[10], both revealing that the HetNet has a potential of greatly improving the system performance in contrast to the conventional homogeneous cellular network.
In order to fully exploit the benefit of the lowerpower nodes in the HetNet, a method of cell range expansion is developed. It is realized by adding a positive bias value to the lowerpower node in the cell selection process; by this, it means that more users can associate to the lowerpower node cell even if the lowerpower node is not of the strongest signal. This method is useful for the load balancing and the exploitation of spatial reuse, and it also helps to mitigate the uplink (UL) intercell interference by reducing the UL transmit power[11–14]. However, in the downlink phase, the UEs in the range expansion area will suffer severe crosslayer interference from the macrocell base station (MBS). Therefore, it is important to develop new interference coordination technology for the HetNet.
In the 3GPP longterm evolution (LTE) R10 standard specification, a specific subframe called almost blank subframe (ABS) is adopted to partially address this problem. Since the MBS is kept silent within ABSs, the users in the lowerpower node cell can be allocated without suffering strong macrocell interference within ABSs. However, the effective use of ABSs requires the lowerpower nodes to have perfect knowledge of the ABS patterns such that they can make a proper user scheduling[11–14]. Besides this approach, interference control (IC) based on limited feedback of channel information, such as the best companion cluster (BCC) technology[15, 16], recently has also been intensively studied and applied in the HetNet[17–20]. In[17], an adaptive strategy is proposed which uses joint beamforming to address the intercell interference between scheduled users only when the interference is significant. In[18], a prioritized selection (PS)based IC scheme is developed. Furthermore, the performance of lowcomplexity random beamforming transmission with user scheduling in the same scenarios is analyzed in[19]. Later, a joint selection (JS)based IC is presented to achieve more balanced performances between the macrocell users and the RRH cell users[20], which is efficient especially when the number of users is sufficiently large. However, it should be noted that this scheme requires that each user feeds back the preferred matrix index (PMI) and the best companion cluster index in a predetermined codebook, causing increased feedback overhead and limiting the freedom degree for user pairing. More recently, an efficient IC scheme based on heterogenous limited feedback is proposed, which fully exploits the inherent heterogeneous structure of user density and largescale channel effects[21]. Furthermore, a distributed scheduling policy based on the cumulative distribution function of the channel quality indicator is designed and analyzed for the HetNet[22]. Also, new interference alignment approaches are proposed to solve the intercell interference of the HetNet[23, 24]. In addition, radio resource allocation is also an important issue for the HetNet, which has attracted much attention. In[25], the authors propose a radio resource allocation framework for the HetNet and derive a resource allocation strategy that is asymptotically optimal on the proportional fairness metric.
In contrast to the homogeneous network, the ratios of the interference level to the desired signal strength for the users randomly distributed in a HetNet vary in a much broader range. This is due to the fact that the lowerpower nodes are overlaid in the macrocell. As a result, direct application of conventional interference coordination approaches in a HetNet usually has low efficiency. In this paper, different from previous works which focus on specific interference coordination scheme design based on a fixed or simple cooperative region, we study a new interference coordination strategy by exploiting an adaptive cooperation region. We propose to first classify the coverage of HetNet into a couple of areas based on coordination efficiency. Users located in different areas have different coordination requests and feedback different channel information so as to improve the coordination efficiency. A new method is then proposed to realize an efficient area classification, and as a particular application, an areaclassified spatial interference coordination scheme based on limited feedback is further developed, which has a much increased success rate of user pairing and thus improves the throughput performance. Simulation results finally show that the proposed scheme outperforms the conventional schemes even with reduced feedback overhead.
2 System model
The main concern for the above system is to mitigate the severe crosslayer interference between the macrocell and the RRH cell. An efficient way to address this issue is by cooperatively designing the beamforming vectors$\{{\mathit{w}}_{i}^{r},{\mathit{w}}_{i}^{m}\}$ and allocating the transmit powers[26–28]. This requires the exchange of channel state information (CSI) of the users between the MBS and the RRH BS. In frequency division duplex (FDD) systems, the CSI should be fed back from the users using the methods such as[29, 30] and then shared between the BSs, causing a large overhead. Though this problem has been intensively studied for the homogeneous network in the literature, the wide range of the SINR distribution resulting from heterogenous deployment has not been well exploited to reach a good tradeoff between the coordination gain and the overhead.
3 Areaclassified interference coordination
Due to the fact that RRH cells are overlaid in the macrocell, in general, all the RRH cell users suffer interference from the MBS. However, the users in different areas usually have significantly distinct orders of signaltointerference ratios (SIRs). Therefore, it is not efficient to employ a uniform interference coordination over all areas. In particular, performing interference coordination on the users with high SIRs usually brings marginal gain but requires additional cost such as the feedback of interference channels. The same issue exists in the macrocell. To improve the coordination efficiency, we propose to classify both the macrocell coverage and RRH cell coverage into two types of areas, i.e., the cooperative area and noncooperative area. The interference coordination is only used for the users located in the cooperative area.
where f_{ s }(·) denotes the size of the cooperative area,${\mathbb{E}}_{\mathcal{A}}(\xb7)$ denotes the expectation operator over the cooperative area, and${g}_{\text{th}}^{m}$ and${g}_{\text{th}}^{r}$ denote the minimum GCR requirements of the macro and RRH cooperative areas, respectively.
It is difficult to directly solve the above optimization problem due to the fact that the closedform expression of the average relative cooperation gain is hard to achieve. Alternatively, we propose to classify the cell coverage according to the reference signal receiving power (RSRP), where the RSRP is defined as the received power at the user terminal measured from the cellspecific reference signal within the considered frequency bandwidth. By this means, the cooperative area can be represented by a couple of parameters and has fewer drawbacks to determine. Before introducing the detailed method, we first provide some numerical results to illustrate some useful observations.
It is seen that the difference between the macrocell RSRP and the RRH cell RSRP is less than 5 dB only when the user has a distance of 160 ∼ 180 m to the macro BS, where the crosslayer interference is severe and the coordinated beamforming is necessary. In other cases, a cooperation between the macro and RRH BSs may not promise a large gain.
3.1 Area classification strategy

RRH noncentral area. This area is the edge region of the RRH cell. A user i belongs to the RRH noncentral area if its RSRPs satisfy the following condition:${\text{RSRP}}_{i}^{r}+\theta \le {\text{RSRP}}_{i}^{m}<{\text{RSRP}}_{i}^{r}+\beta $(6)
where${D}_{min}^{r}$ denotes the minimum distance between the RRH cell user and the RRH BS in km, R_{ m } denotes the macrocell service radius in km,${P}_{\mathit{\text{dB}}}^{r}$ and${P}_{\mathit{\text{dB}}}^{m}$ denote the transmit power of the RRH node and the macro BS in dB, respectively.

RRH central area. This area is the interior region of the RRH cell. A user i belongs to the RRH central area if its RSRPs satisfy the following condition:${\text{RSRP}}_{i}^{m}<{\text{RSRP}}_{i}^{r}+\theta $(8)
In Figure6, it is illustrated as the pastel striped areas tagged with number ②. A user located in the RRH central area usually does not require a cooperation between the macro BS and the RRH due to the fact that the strength of the interference is much less than that of the effective signal.

Macro noncentral area. This area is the edge region of the macrocell with the RRH cell. A user i belongs to the macrocell noncentral area if its RSRPs satisfy the following condition:${\text{RSRP}}_{i}^{r}+\beta \le {\text{RSRP}}_{i}^{m}<{\text{RSRP}}_{i}^{r}+\alpha $(9)
where${D}_{min}^{m}$ denotes the minimum distance between the macrocell user and the macro BS in kilometers, and D_{m _r} denotes the distance between the RRH and the macro BS in kilometers.

Macro central area. This area is the interior region of the macrocell. A user i belongs to macrocell central area if its RSRPs satisfy the following condition:${\text{RSRP}}_{i}^{r}+\alpha \le {\text{RSRP}}_{i}^{m}$(11)
It is worth mentioning that from Theorem 1 in the literature[13], we know that the abovementioned first three areas are in the shape of an ellipse. Based on the area classification, the user can request different levels of coordination according to its location in different areas, by feeding back different amounts of channel information. By this means, a better tradeoff between the coordination gain and the feedback overhead than the conventional approach can be achieved.
3.2 Classification criterion
It is easy to verify that a smaller θ gives a lower cooperation gain due to the fact that the ratio of the interference strength to the effective signal strength decreases with θ decreasing. While on the other hand, a smaller θ produces an enlarged RRH noncentral area, resulting in increased cooperation overhead.
where ξ is a threshold representing the desired relative cooperation gain for the RRH user. Note that the choice of θ obtains the boundary between the cooperative RRH noncentral area and the noncooperative RRH central area. The above criterion suggests that the boundary is determined by minimizing the size of the RRH cooperative area (maximizing θ) subject to a given maximum performance loss caused by the noncooperation of the RRH central area.
We note that based on the above criteria (16) and (17), it is still difficult to obtain closedform solutions of the area classification parameters θ and α. However, provided the detailed coordinated beamforming optimization approach, the optimal values of θ and α can be achieved via onedimensional numerical search. In particular, as a practically useful approach, θ and α can both be determined using a deepest slope method. As shown in Section 5.1, G_{ R }(θ) and G_{ M }(α) are nondecreasing and nonincreasing functions, respectively, with the slope varying in different intervals. Thus, it is reasonable to choose θ and α to be the ending points of the sharpest slope interval, such that the coordinated transmission strategy is employed only if it brings significant gain.
4 Areaclassified coordinated beamforming based on limited feedback
In order to examine the performance of the proposed areaclassified intercell interference coordination strategy above, as a typical application, in this section, we develop a coordinated beamforming scheme using areaspecific limited feedback. It is known in[15, 16] that if coordinated transmission strategy is performed based on a codebook, each user needs to feed back not only the index of the preferred precoding matrix or beamforming vector (PMI) and the channel quality indicator (CQI) usually defined as the SINR, but also the index of the company precoding matrix or beamforming vector to be used by the cooperative BS which causes the least interference. With these information, the MBS and the RRH can cooperatively select a pair of users (also called user pairing, each serves one user) to perform coordinated beamforming which imposes minimized interference to each other. To further improve the performance, a clusterstructured codebook is developed by clustering together the codewords with high correlation[31, 32]. Based on that, the user feeds back the index of the company cluster instead of the company precoding matrix or beamforming vector. This can significantly improve the success rate of user pairing.
where${\mathit{B}}_{i}^{m}$ and${\mathit{B}}_{i}^{r}$ denote the i th codeword cluster consisting of a set of correlated codewords, in the MBS and the RRH codebooks, respectively. Denote the number of codewords in the cluster${\mathit{B}}_{i}^{m}$ and${\mathit{B}}_{i}^{r}$ as${I}_{i}^{m}$ and${I}_{i}^{r}$, respectively. Then, the total numbers of codewords in the codebook B^{ m } and B^{ r } are${I}^{m}=\sum _{i=1}^{M}{I}_{i}^{m}$ and${I}^{r}=\sum _{i=1}^{N}{I}_{i}^{r}$, respectively. Without loss of generality, we assume that codeword cluster${\mathit{B}}_{i}^{m}$ consists of the$\left(\sum _{j=1}^{i1}{I}_{j}^{m}+1\right)$th to$\left(\sum _{j=1}^{i}{I}_{j}^{m}\right)$th codewords in the MBS codebook, and codeword cluster${\mathit{B}}_{i}^{r}$ consists of the$\left(\sum _{j=1}^{i1}{I}_{j}^{r}+1\right)$th to$\left(\sum _{j=1}^{i}{I}_{j}^{r}\right)$th codewords in the RRH codebook.
4.1 Areaspecific feedback scheme
Now we focus on the limited feedback design. In our proposed method, each user first determines its belonging area according to the RSRPs and then calculates feedback information correspondingly. If the user judges that it belongs to the central area, i.e., the macro central area or the RRH central area, it feeds back the area tag, the PMI and the SINR. Otherwise, if it belongs to the noncentral area, i.e., the macro noncentral area and the RRH noncentral area, it feeds back the company cluster index along with the area tag, the PMI and the SINR. The details are given as follows:

The RRH central area user determines the preferred codeword and computes the SINR as${\text{PMI}}_{i}^{r}=\underset{j=1,\dots ,{I}^{r}}{\text{max}}\u2225{\left({\mathit{h}}_{i,r}^{r}\right)}^{H}{\mathit{w}}_{j}^{r}\u2225$(23)
Note that, here, the crosslayer interference is only estimated based on the largescale fading.

The RRH noncentral area user determines the preferred codeword, the company cluster, and computes the SINR as${\text{PMI}}_{i}^{r}=\underset{j=1,\dots ,{I}^{r}}{\text{max}}\u2225{\left({\mathit{h}}_{i,r}^{r}\right)}^{H}{\mathit{w}}_{j}^{r}\u2225$(25)
It also means that this minimum interference codeword should belong to${\mathit{B}}_{{P}_{i}^{r}}^{m}$.

The macro central area user determines the preferred codeword index${\text{PMI}}_{i}^{m}$ and computes${\text{SINR}}_{i}^{m}$ using the method similar to that of the RRH central area user.

The macro noncentral area user determines the preferred codeword index${\text{PMI}}_{i}^{m}$, the company cluster index${P}_{i}^{m}$, and the SINR using the method similar to that of the RRH noncentral area user.
4.2 Areaclassified interference coordination scheme
Based on the above limited feedback method, we develop an areaclassified coordinated beamforming scheme summarized as Algorithm 1.
Remark 1.
Compared with the conventional codebookbased coordinated beamforming algorithms such as[18, 20], the proposed algorithm significantly increases the success rate of user pairing^{a} and thus improves the rate performance. On the other hand, the feedback burden is also reduced by the proposed areaspecific feedback method. Note that in the proposed algorithm, central users with noncoordinated transmission may suffer a certain performance loss, but such a loss is controlled to be below the given threshold via the area classification. Therefore, the throughput improvement benefiting from increased user pairing rate usually dominates the overall performance, as verified by the numerical results provided in the following section.
5 Numerical results
Simulation parameters
Parameters  Setting 

Bandwidth  10 MHz 
Number of subcarriers  2,048 
Thermal noise density  174 dBm/Hz 
Number of macro BSs  1 
Mobile users within macro range  6∼30 
Number of RRH BSs  1 
Mobile users within RRH range  6∼30 
MBS transmit power  46 dBm 
MUE distribution radius  289 m 
RRH node transmit power  30 dBm 
Codebook size  32, 64 
Codebook cluster size  4, 8 
Macro path loss model  128.1 + 37.6 log10(R) dB (R in km) 
RRH path loss model  140.7 + 36.7 log10(R) dB (R in km) 
Distance MBSRRH node  200 m 
Minimum distance MBSmacro user  35 m 
Minimum distance RRH nodeRRH user  10 m 
Scheduler  Proportional fairness 
For comparison, two relevant coordinated beamforming schemes, i.e., the prioritized selection (PS)based IC scheme (PSIC) in[18] and the joint selectionbased interference coordination scheme (JSIC) in[20] are simulated, too. In addition, the performance of the conventional zeroforcing (ZF) coordinated beamforming scheme and the TDMA interference coordination with maximum ratio transmitter (MRT) are simulated, too, both based on limited feedback CSI. Note that for the fairness, the comparing schemes and the proposed scheme all employ the same codebook, i.e., the clusterstructured discrete fourier transformation (DFT) codebook which is generated in[20].
5.1 Relative cooperation gain performance
Simulation results show that, in general, the average relative cooperation gain decreases with the value of θ decreasing or with the value of α increasing. This is due to the fact that the decreased θ and the increased α produce an enlarged RRH noncentral area and macro noncentral area, respectively. As a result, the difference between the RSRPs from the macro BS and the RRH node in these noncentral areas is increased, which degrades the interference coordination efficiency. This behavior of the average relative gain suggests that expanding the cooperation area cannot always bring significant gain.
The results also illustrate that the slope of the average relative cooperation gain with respect to α or θ is varying interval by interval. In particular, the sharpest slope only appears in a small interval, meaning that only in this interval did the interference coordination brought significant gain and was the most efficient. Based on this observation, it is reasonable to determine jointly the factors (ξ,θ) and (ζ,α) as the points that end the sharpest slope interval so as to achieve a good tradeoff between the cooperation gain and the cost. Such a method is called ‘the steepest slope method’. For example, in Figure7A, we can determine the value of the θ based on the turn point that ends the rightmost slope, i.e., the relative cooperation gain is 0.375 and the value of θ is 6.
5.2 Throughput performance
Feedback overhead comparison
Parameters  JSIC  Proposed scheme (0.375, 0.18) 

(θ,α)  (6,29)  
The number of feedback bits  300  225 
The number of matched user pairs  39  260 
6 Conclusions
In this paper, an areaclassified interference coordination strategy was first proposed for heterogeneous cellular networks. The basic principle was to classify the cell coverage into different areas and further perform areaspecific interference coordination. A new steepest slope method based on relative cooperation gain was provided to realize efficient area classification. Following this idea, an areaclassified coordinated beamforming scheme with limited feedback was further proposed for the HetNet. In this scheme, the proposed areaspecific limited feedback scheme could increase the success rate of user pairing and thus improve the throughput performance, and with reduced feedback overhead in contrast to existing schemes. The effectiveness of the proposed method was finally verified with simulation results.
Endnote
^{a}It is seen that in our scheme, two users are paired if any one of three conditions is satisfied, while in[20], the user pairing succeeds only if condition II is satisfied.
Declarations
Acknowledgements
This work was supported by the National Science and Technology Major Project of China under Grant 2013ZX03003006002; National Natural Science Foundation of China under Grants 61271018, 61372101, and 61222102; Research Project of Jiangsu Province under Grants BK20130019, BK2011597, BK2012021, and BE2012167; Open Research Fund of Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education; and NEC Research Fund.
Authors’ Affiliations
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