- Research Article
- Open Access
Decentralising Multicell Cooperative Processing: A Novel Robust Framework
© Agisilaos Papadogiannis et al. 2009
- Received: 28 January 2009
- Accepted: 17 April 2009
- Published: 2 June 2009
Multicell cooperative processing (MCP) has the potential to boost spectral efficiency and improve fairness of cellular systems. However the typical centralised conception for MCP incurs significant infrastructural overheads which increase the system costs and hinder the practical implementation of MCP. In Frequency Division Duplexing systems each user feeds back its Channel State Information (CSI) only to one Base Station (BS). Therefore collaborating BSs need to be interconnected via low-latency backhaul links, and a Control Unit is necessary in order to gather user CSI, perform scheduling, and coordinate transmission. In this paper a new framework is proposed that allows MCP on the downlink while circumventing the aforementioned costly modifications on the existing infrastructure of cellular systems. Each MS feeds back its CSI to all collaborating BSs, and the needed operations of user scheduling and signal processing are performed in a distributed fashion by the involved BSs. Furthermore the proposed framework is shown to be robust against feedback errors when quantized CSI feedback and linear precoding are employed.
- Channel State Information
- Mobile Station
- Ergodic Capacity
- Feedback Error
- Imperfect Channel State Information
Cellular systems employing aggressive frequency reuse and especially full frequency reuse have recently attracted the attention due to the increasing demand for high quality and throughput wireless services (mobile Internet), together with the scarcity of radio spectrum. Although these systems lead to significant gains in spectrum usage, they incur important losses in cell throughput resulting from the increased amount of intercell interference (ICI). This mainly affects users located on the cell edge as they are more prone to ICI originating from neighbouring cells. Therefore ICI is a factor causing significant performance and fairness degradation in the network . Furthermore ICI degrades performance of Multiple Input Multiple-Output (MIMO) systems; hence it impedes their deployment in a cellular context .
Multicell cooperative processing (MCP) has been recognized as an effective solution for ICI mitigation [1, 3, 4]. In MCP enabled systems BSs are grouped into cooperation clusters, each of which contains a subset of the network BSs. The BSs of each cluster exchange information and jointly process signals by forming virtual antenna arrays distributed in space. They can be seen as multiuser MIMO systems where the antennas are no longer collocated but remote. Notably, MCP has been shown to reduce ICI and boost performance; this especially suits the downlink as interference mitigation burdens the network infrastructure and not the receivers .
However, MCP comes at the cost of increased signaling and infrastructural overheads. On the downlink of cellular systems operating in Frequency Division Duplexing (FDD) mode, the overheads of MCP are related to the inherent need for Channel State Information (CSI) at the transmitter of multiuser MIMO systems and also to the distributed nature of collaborative BS processing . The overheads related to MCP can be divided into two main categories.
CSI estimation: users estimate a greater number of channel coefficients than a multiuser MIMO system, equal to the total number of cooperating antennas.
CSI Feedback: feedback of the estimated high number of channel coefficients from users to BSs.
Time synchronisation: collaborating BSs need to be tightly synchronised in time.
Control Unit: the CU gathers CSI from the BSs, performs scheduling, and designs the transmission parameters according to the chosen transmission strategy.
Low-latency backhaul links: collaborating BSs are connected with the CU via low-latency links in order to exchange CSI, scheduling decisions, and transmission parameters.
Note that the signaling overheads are independent of the architectural conception for MCP, whereas the infrastructural overheads mentioned above are related to the existing conception for the architecture of MCP.
A natural way for mitigating the aforementioned overheads is to limit the number of cooperating BSs per cluster. A simple technique that has been proposed is limited static clustering, where BS cooperation groups are of limited size and remain static; only neighbouring BSs collaborate [6, 7]. This has been shown to be a good trade-off between performance and overhead. However, even higher performance gains can be attained if the limited clusters are formed dynamically; in this case the cooperating BSs are not the neighbouring ones but rather the ones that interfere the most [8, 9]. In addition, ways of optimizing system performance under a constrained backhaul have been considered . However, these contributions attempt mainly to mitigate the signaling overheads as they imply a CU per cooperation cluster and low-latency inter-BS links. In order to facilitate the deployment of MCP, it is desirable that the infrastructural overheads entailed by the existing conception for MCP (CU, low-latency backhaul links) are alleviated, and this aspect is not addressed in the abovementioned contributions.
According to the typical framework for MCP on the downlink of FDD systems, a Mobile Station (MS) estimates the channels related to the BSs of its cooperation cluster (CSI estimation). Then it feeds back to the BS of its cell (usually the one that it receives the maximum SNR from, defined as Master BS) either full or partial CSI (i.e., long-term or quantized CSI). Subsequently, the BS forwards this local information (CSI) to the CU of the cluster which gathers local CSI from all cooperating BSs. Local CSI for a BS is defined as the CSI related to the MSs belonging to its cell. Nonlocal CSI for a BS is defined as the CSI of the MSs belonging to different cells of the cooperation cluster. The CU selects the users to be served (scheduling phase) and calculates the transmission parameters which are then sent to the corresponding BSs for the transmission to take place (transmission phase). Therefore in the existing conception, a CU and the CU to BSs low-latency links are necessary [11–13], a fact which demands substantial changes to the current system architecture and a significant increase in costs.
In  a framework for decentralising MCP has been proposed which aims at keeping the necessary infrastructural overheads and costs for accommodating MCP to a minimum. It is assumed that each BS collects local together with nonlocal CSI; each MS sends its CSI estimate to all cooperating BSs. In this case, each BS can perform scheduling, and design the transmission parameters independently without the need of any CSI exchange with a central entity; the same scheduling decisions are made by each BS. The proposed framework has a potential sensitivity to feedback errors since MSs utilise several radio links in order to communicate their CSI to the collaborating BSs. This was not fully evaluated in  which only assumed unquantized (unlimited) feedback. However, limited feedback schemes are of practical importance . Therefore in this paper a more realistic quantized feedback model is considered, and its sensitivity to feedback errors under the proposed framework is investigated. It is shown that the proposed decentralized framework is robust against feedback errors under a realistic digital feedback model.
The paper is structured in the following way: in Section 2 the system model is introduced. In Section 3 the linear precoding framework for transmission, together with the models for quantized feedback, and feedback errors are presented. In Section 4 the typical centralised conception for MCP is described while in Section 5 the proposed decentralised framework for MCP is discussed. In Section 6 numerical results are shown related to feedback errors proving the robustness of the decentralised MCP approach. The paper is concluded in Section 7.
where represents the channel vector of the th user, is the vector containing the transmit antenna symbols, and is the independent complex circularly symmetric additive Gaussian noise coefficient. An average per antenna power constraint has been considered, for . It is assumed that the system operates in FDD mode and that each MS obtains a perfect estimate of its own channel state . In addition, we consider delayless feedback links which are utilised by the MSs in order to feed back their CSI to the system infrastructure. The users feed back limited CSI (quantized CSI) which can be corrupted by errors introduced by the feedback channel.
where and represent the respective power allocation levels. In this paper equal power allocation is considered across MSs for simplicity.
Notation. Lower and upper case boldface symbols denote vectors and matrices, respectively; and denote the transpose and the Hermitian transpose, respectively. stands for the Euclidean norm of a vector, denotes the cardinality of a set, refers to the th element of a matrix diagonal, and represents the complex space of dimensions. denotes the expectation operator, and represents the angle between vectors and .
In MCP enabled networks each group of collaborating BSs forms a distributed antenna array. Therefore all the typical multiuser MIMO precoding techniques can be applied in order for the ICI to be mitigated. In this paper linear precoding is considered for MCP transmission as it provides a good trade-off between performance and complexity, and it is more robust to imperfect CSI compared to nonlinear schemes . Furthermore linear precoding together with the more practical quantized feedback can be optimal under certain circumstances . In addition to this, linear precoding scales optimally when a large number of MSs is available and opportunistic scheduling is employed .
The term corresponds to the intercell interference power.
With equal power allocation and an equal power constraint per BS, for , the expression for the power allocation vector (10) reduces to .
3.1. Quantized Limited Feedback
where results from the inner product rule. The quantity determining the efficiency of quantization is the quantization error defined as . The codebook should be user specific in order to avoid multiple users quantizing their channel direction to the same vector.
After quantization, MS i feeds back to the system the index k in binary form which corresponds to the quantization vector that best describes its channel direction. Therefore this piece of information is defined as Channel Direction Information (CDI). The more the feedback bits are, the larger the quantization codebook is, which leads to a better approximation of the MS's channel direction. Apart from CDI, the scheduling entity needs some information regarding the channel quality of each user in order to be able to make user selection decisions; this is defined as Channel Quality Information (CQI). In this paper we consider the unquantized channel norm as the fed back CQI which does not capture the interuser interference. This is because we are interested in investigating the precoding performance and not the efficiency of scheduling; therefore the consideration of more complex CQI metrics is unnecessary.
A random codebook has been considered as optimization of the codebook design is beyond the scope of this paper. Thus the codebook is comprised of unit norm complex Gaussian random vectors, and .
Hence the concatenated quantized channel matrix of the system is . The binary indices corresponding to the CSI vectors (lines of ) are fed back to the scheduling entity through one or several radio channels, depending on the MCP framework, that introduce errors. Therefore the concatenated channel possessed by the scheduling entity is , where is a function of the errors introduced by the feedback channel.
3.2. Feedback Error Model
The probability of bit errors is considered to be identical and independent across different radio links. The received feedback can be protected from errors by the use of appropriate error correction techniques requiring the addition of a number of bits in the fed back sequence. However such consideration is beyond the scope of this paper which aims at examining the impact of feedback errors in the worst case scenario, when no error detection or correction schemes are employed.
The typical conception for MCP entails that collaborating BSs are interconnected via low-latency backhaul links. These links are responsible for carrying the necessary signals that allow cooperating BSs to act jointly, perform user scheduling and design the transmission parameters for the scheduled users. Under a linear precoding framework these parameters are the beamforming weights applied by each BS antenna of the cooperation cluster. The entity coordinating this joint action is a Control Unit accommodated in each cooperation cluster. It gathers global user CSI and centrally performs MS scheduling and signal processing operations.
- (1)Phase 1.
MSs estimate the CSI related to all cooperating BSs through downlink pilots. In this paper perfect channel estimation is assumed and thus each MS estimates the channel vector .
Incase limited digital feedback is employed, MSs quantize the direction of their channel estimate; that is, MS quantizes its channel direction to .
- (2)Phase 2.
MSs feedback their CSI (CDI and CQI) to their Master BS with the proper power and modulation and coding scheme (MCS) in order for the BS to be able to decode the information. All cooperating BSs gather local CSI, the CSI of the MSs belonging to their cells.
forward the local CSI to the CU of the cluster through the low-latency backhaul links. The CU collects global CSI affected by the errors on the feedback channel (Figure 1).
- (3)Phase 3.
The CU schedules MSs based on global CSI .
The CU designs the beamforming weights for each BS antenna and communicates them together with the scheduling decisions to the corresponding BSs for the transmission to take place.
This framework requires a significantly increased infrastructural cost comparing to the conventional cellular systems, as there is a demand for low-latency interbase links and a CU per cooperation cluster. Furthermore there is a need for an increased communication protocol complexity in order for these entities to interoperate properly. These facts inevitably imply changes in the current architecture of cellular systems in order for MCP to be enabled. However it is highly desirable that changes to the current structure of cellular systems are kept to a minimum when MCP capabilities are enabled.
where denotes the binomial coefficient. The bit sequence is in error if at least one of its bits is in error; thus for a specific bit error probability , the more the feedback bits are, the more likely an error occurs. From this perspective it is of interest that the codebook size is kept as small as possible. However the smaller the codebook, the less accurately it can approach the actual channel state of the user, which leads to an inferior performance. Therefore a trade-off exists between the needed codebook precision and its size with respect to feedback errors.
- (1)Phase 1 (identical with the centralised framework).
In case limited digital feedback is employed, MSs quantize the direction of their channel estimate; that is, MS quantizes its channel direction to .
- (2)Phase 2.
MSs feedback their CSI (CDI and CQI) to all cooperating BSs by utilising the radio links connecting them with the collaborating BSs. Each MS feeds back its CSI omnidirectionally, and this transmission is done with the proper power and MCS in order for all cluster BSs to be able to decode the information. All cooperating BSs gather global CSI, the CSI of the MSs of all cooperating cells (Figure 2).
- (3)Phase 3.
The BSs schedule MSs independently based on their acquired global CSI. Cluster BSs are synchronised and employ the same scheduling algorithm. In case there are no feedback errors, BSs receive the same input parameters (global CSI ), and the schedulers end up selecting exactly the same MSs. If feedback links introduce errors, the fed back CSI (CDI and CQI) needs to be protected by the use of appropriate techniques.
Each BS designs the complete beamforming matrix and utilises the antenna weights corresponding to it; that is, BS utilises for transmission the th line of the precoding matrix .
Under this framework, infrastructural costs and signaling protocol complexity are minimised when MCP is enabled as neither a CU per cluster nor the low latency links connecting it with the cooperating BSs are required. Hence, the structure of MCP enabled cellular networks can remain almost the same with the structure of the conventional cellular systems. Note that under this framework, radio feedback overhead remains the same, comparing to the conventional centralised framework, provided that the same resources are allocated to the terminal for feeding back its CSI by each cooperating BS.
In case errors are introduced to the fed back information, the decentralised framework can be more sensitive than the centralised one as error patterns can be different on each employed feedback link. In the centralised framework, each MS utilises only one radio link for feeding back its channel state information (CSI transmitted to the Master BS only); therefore there is only one error pattern affecting feedback information per MS in this case. In the decentralized framework MSs feed back their CSI to all cooperating BSs; thus each BS k might acquire a different version of the global CSI .
Hence feedback errors can potentially cause a further performance degradation to the decentralised framework than the centralised one compared to the no error case. Furthermore, it is interesting to investigate how close these two frameworks perform under the existence of feedback errors.
We assume that each MS obtains a perfect estimate of the channel vector associated to all cooperating BSs ( ). This estimate is quantized and then fed back omnidirectionally (CDI and CQI feedback). In the centralised framework each MS's feedback is received only by its Master BS (Figure 1). In the decentralised framework all cooperating BSs receive the CSI feedback in order for the decentralised cooperation to take place (Figure 2). An important parameter which determines the BS transmission power is the System SNR which is the average SNR a user experiences at the edge of the cell without taking into account ICI.
6.1. Codebook Size
6.2. Impact of Feedback Errors
Feedback errors inevitably degrade performance of both frameworks because some useful information is lost by the intervention of bit errors in the fed back CSI. This is because scheduling performance is degraded due to the corrupted CSI information, and also precoding matrix design is affected due to the same corrupted CSI. The decentralised framework can be more sensitive to scheduling degradation as imperfect CSI might result in a selection of different users by some of the cooperating BSs, depending on the scheduling algorithm employed, which will inevitably increase interuser interference. However, round-robin scheduling is robust to CSI feedback errors as scheduling decisions are not made based on CSI. This scheduling algorithm is selected for the present evaluation which focuses on the impact of feedback errors on the design of precoding matrices. Note that with the absence of feedback errors the performance of the two frameworks under any kind of scheduling and transmission strategy coincides.
Multicell Cooperative Processing promises significantly improved spectral efficiency and fairness for future cellular systems. However, this comes at the cost of increased infrastructural complexity and centralised processing related to the existing conception for MCP. According to this, cooperating BSs need to be connected to a Control Unit which plays the role of the cluster head. It gathers local CSI from the BSs, performs user scheduling, and designs the transmission parameters. In this paper a new framework has been proposed that allows MCP on the downlink to take place in a decentralised fashion; neither a CU is needed nor the low-latency backhaul links. Each BS receives CSI feedback from all the users of the cluster (global CSI) and designs transmission independently. The performance of the proposed framework has been evaluated under the realistic assumption of quantized limited feedback together with linear precoding and while feedback errors are introduced by the channel. It has been shown that the proposed scheme shows little degradation on the ergodic achievable sum-rate compared to the centralised alternative, which can be eliminated with a more intelligent codebook design and the addition of feedback protection schemes (error detection/correction). The decentralised framework allows MCP to be implemented with very few changes upon the current network architecture.
- Karakayali MK, Foschini GJ, Valenzuela RA: Network coordination for spectrally efficient communications in cellular systems. IEEE Wireless Communications 2006, 13(4):56-61. 10.1109/MWC.2006.1678166View ArticleGoogle Scholar
- Catreux S, Driessen PF, Greenstein LJ: Simulation results for an interference-limited multiple-input multiple-output cellular system. IEEE Communications Letters 2000, 4(11):334-336. 10.1109/4234.892193View ArticleGoogle Scholar
- Shamai S, Zaidel BM: Enhancing the cellular downlink capacity via co-processing at the transmitting end. Proceedings of the IEEE Vehicular Technology Conference (VTC '01), May 2001, Rhodes, Greece 3: 1745-1749.Google Scholar
- Zhang H, Dai H: Cochannel interference mitigation and cooperative processing in downlink multicell multiuser MIMO networks. EURASIP Journal on Wireless Communications and Networking 2004, 2004(2):222-235.MATHView ArticleGoogle Scholar
- Spencer QH, Peel CB, Swindlehurst AL, Haardt M: An introduction to the multi-user MIMO downlink. IEEE Communications Magazine 2004, 42(10):60-67. 10.1109/MCOM.2004.1341262View ArticleGoogle Scholar
- Venkatesan S: Coordinating base stations for greater uplink spectral efficiency in a cellular network. Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC '07), September 2007, Athens, GreeceGoogle Scholar
- Boccardi F, Huang H: Limited downlink network coordination in cellular networks. Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC '07), September 2007, Athens, GreeceGoogle Scholar
- Papadogiannis A, Gesbert D, Hardouin E: A dynamic clustering approach in wireless networks with multi-cell cooperative processing. Proceedings of the IEEE International Conference on Communications (ICC '08), May 2008, Beijing, China 4033-4037.Google Scholar
- Papadogiannis A, Bang HJ, Gesbert D, Hardouin E: Downlink overhead reduction for multi-cell cooperative processing enabled wireless networks. Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC '08), September 2008, Cannes, FranceGoogle Scholar
- Marsch P, Fettweis G: A framework for optimizing the downlink of distributed antenna systems under a constraint backhaul. Proceedings of the European Wireless Conference (EW '07), April 2007, Paris, FranceGoogle Scholar
- Parkvall S, Dahlman E, Furuskar A, et al.: LTE-advanced—evolving LTE towards IMT-advanced. Proceedings of the IEEE Vehicular Technology Conference (VTC '08), September 2008, Calgary, CanadaGoogle Scholar
- Y. Song, L. Cai, K. Wu, and H. Yang, “Collaborative MIMO Based on Multiple Base Station Coordination,” Contribution to IEEE 802.16m, IEEE C802.16m-07/162, August 2007.Google Scholar
- Alcatel Shanghai Bell, Alcatel Lucent, “Collaborative MIMO for LTE-A Downlink,” 3GPP TSG RAN WG1 Meeting 53bis, R1-082501,Warsaw, Poland, June-July 2008.Google Scholar
- Papadogiannis A, Hardouin E, Gesbert D: A framework for decentralising multi-cell cooperative processing on the downlink. Proceedings of the 4th IEEE Broadband Wireless Access Workshop (BWA '08), December 2008, New Orleans, La, USAGoogle Scholar
- Love DJ, Heath RW Jr., Lau VKN, Gesbert D, Rao BD, Andrews M: An overview of limited feedback in wireless communication systems. IEEE Journal on Selected Areas in Communications 2008, 26(8):1341-1365.View ArticleGoogle Scholar
- Jafar SA, Srinivasa S: On the optimality of beamforming with quantized feedback. IEEE Transactions on Communications 2007, 55(12):2288-2302.View ArticleGoogle Scholar
- Yoo T, Goldsmith A: On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming. IEEE Journal on Selected Areas in Communications 2006, 24(3):528-541.View ArticleGoogle Scholar
- Peel CB, Hochwald BM, Swindlehurst AL: A vector-perturbation technique for near-capacity multiantenna multiuser communication—part I: channel inversion and regularization. IEEE Transactions on Communications 2005, 53(1):195-202. 10.1109/TCOMM.2004.840638View ArticleGoogle Scholar
- Kaltenberger F, Gesbert D, Knopp R, Kountouris M: Performance of multi-user MIMO precoding with limited feedback over measured channels. Proceedings of IEEE Global Communications Conference (GLOBECOM '08), December 2008, New Orleans, La, USAGoogle Scholar
- Jindal N: MIMO broadcast channels with finite-rate feedback. IEEE Transactions on Information Theory 2006, 52(11):5045-5060.MATHMathSciNetView ArticleGoogle Scholar
- Kountouris M, de Francisco R, Gesbert D, Slock DTM, Sälzer T: Efficient metrics for scheduling in MIMO broadcast channels with limited feedback. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '07), April 2007, Honolulu, Hawaii, USA 3: 109-112.Google Scholar
- CELTIC WINNER+ deliverable : D1.4 Initial Report on Advanced Multiple Antenna Systems. January 2009, http://projects.celtic-initiative.org/winner+/deliverables_winnerplus.html
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.