interference control in lte-advanced: an artist4g … · interference control in lte-advanced: an...

Future Network & MobileSummit 2012 Conference Proceedings Paul Cunningham and Miriam Cunningham (Eds)

IIMC International Information Management Corporation, 2012 ISBN: 978-1-905824-30-4

Copyright © 2012 The authors www.FutureNetworkSummit.eu/2012 of 9

Interference Control in LTE-Advanced: An ARTIST4G Overview

Hendrik SCHOENEICH1, Ignacio BERBERANA2, Michael GRIEGER3, Raphaël VISOZ4

1Qualcomm CDMA Technologies GmbH, Nuremberg, GermanyEmail: [email protected]

2Telefónica I+D S.A.U., Don Ramón de la Cruz 82-84, 28006 Madrid, Spain Tel: +34 91 312 8792, e-mail: [email protected]

3Vodafone Chair Mobile Communications Systems, Technische Universität Dresden, Germany, Email: [email protected]

4Orange Labs, 38-40 rue du Général Leclerc 92794 Issy-Moulineaux cedex 9

Tel: +33145296405, Email: mailto:[email protected]

Abstract: This paper gives an overview of the interference cancellation and interference control study for LTE-A in the European research project ARTIST4G.

Keywords: LTE, LTE-A, 3GPP, interference cancellation, interference control, heterogeneous networks

1. Introduction Current state-of-the-art in LTE standardization has been largely driven by the goal to improve peak data rates and to get as close as possible to capacity limits. Higher-order modulation schemes, turbo-coding, hybrid ARQ (HARQ), larger bandwidths and an increased number of antennas are means to push and virtually achieve those limits. Therefore, further improvements based on these means is coming to its limits.

With this in mind the goal of the ARTIST4G project was to investigate, evaluate, and disseminate different classes of solutions beyond current state-of-the-art which should pave the way for a balanced user experience with ubiquitous quality for everyone, irrespective of the user position in the cell.

LTE and LTE-A are interference-limited systems in regions with dense cells, e.g., when a combination of macro and pico cells are used in a heterogeneous network. Therefore one of the key ARTIST4G topics is interference management, which can be further classified into two types:

• The first approach is to avoid interference at the receiver. This can be done by coordinating transmissions from different points such as to achieve orthogonality in time and frequency, or to mitigate interference through coherent or non-coherent precoding, in all cases leading to an increase in spectral efficiency.

• The second approach is to step back from the paradigm of avoiding or mitigating interference through transmitter-side techniques, but instead allow for a certain extent of interference which can then be cancelled or exploited at the receiver side.


This paper concentrates on a high-level view of the ARTIST4G results related to the latter point. Interference cancellation

• is well-known for quite some time to enhance link-level performance. When it comes to practical application the impact of real-world constraints is an important aspect which has to be considered. In ARTIST4G this was done by means of field tests. An example for field-test evaluation of interference exploitation where interference is constructively used to improve user rates on a system-level is given in Sec.2.

• is usually based on non-linear receiver algorithms which cannot be properly described by state-of-the-art prediction methods. Such methods – also referred to as link-to-system (L2S) mapping - are needed for fast performance prediction and system-level evaluations, where it is not feasible to apply the algorithms in all details. ARTIST4G designed L2S models for non-linear interference cancellation. This topic is described in Sec.3.

• is seen as a promising extension to the toolbox in modern cellular mobile radio systems. Its use in combination with interference avoidance techniques in a coordinated way is handled in Sec.4.

2. Interference exploitation Moving to interference exploitation techniques, we can take advantage of pilot-based interference channel estimation at the receiver side. Advanced receiver concepts have been developed and evaluated with multiple antennas, especially successive interference cancellation (SIC) techniques which allow the radio resource to be shared and reused. In addition and inspired by the turbo principle, the MIMO receiver can operate in an iterative fashion to further improve system performance. Note that these types of receivers are referred to as turbo SIC receivers in 3GPP terminology in contrast to their classical non-iterative counterparts. Finally when used in uplink, these receivers can be distributed among several base stations to further enhance the reception algorithm, assuming fast and synchronized communications between the base stations. In this case, the major technical challenges are:

- the time synchronization of all users (and thus, all base stations) - the multi-cell channel estimation - the effective multi-cell signal processing that is implementable on state of the art

base station platforms - the backhaul delay and throughput constraints

Since the complexity of such a system is very high and important issues can easily be overlooked during simulations, the performance of joint signal processing needs to be assessed under real-world conditions in order to assess the feasibility and performance of current systems and to allow smart upgrades.


�Figure 1: Gain of using SIC in a non-cooperative receiver compared to non-cooperative reception without

SIC.

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�Figure 2: User rate CDF for non-cooperative detection and joint detection of two base-stations

Figure 1 shows the performance of multiple receiver strategies in a field trial incorporating two mobiles and sixteen base stations in downtown Dresden (an area with a varied building morphology, as depicted in Figure 1). The mobiles were carried on a measurement car that traversed a route of 16 km travelling at a speed of about 6 km/h. The evaluation includes joint signal processing of multiple base stations as well as advanced interference cancellation reception. For details on the field trial architecture and the signal processing algorithms, we refer the reader to [1]. Figure 2 shows that the user throughput distribution is more uniform when using a joint detection scheme (red curve with triangles) which can be further improved with interference cancellation receivers (red-dashed curve with triangles).

However, joint detection has the disadvantage of requiring the exchange of receive signals over a backhaul link that connects the two base stations. An alternative is distributed successive interference cancellation (DSIC) which is based on the exchange of decoded information, and thus has much lower backhaul requirements. Even though such an implementation shows gains compared to local SIC, it does not achieve the performance of joint detection (compare blue curve with squares with red curves with triangles) because of missing array- and multiplexing-gain.

Recent literature on interference management is unequally spread among interference avoidance and interference exploitation techniques since using the former with


oversized feedbacks mechanism have showed performance close to capacity [2]. Although closed loop techniques (i.e. feeding back information about the communication channel) have proved to be superior to open loop techniques in many useful scenarios, it is wise remembering that in a classical transmitter(s)/receiver(s) communication scheme through an unknown channel, any technique based on a priori knowledge of the channel faces physical limits because the causality principle imposes that the channel can be predicted up to a certain extent, whereas those based on a posteriori knowledge only faces technological limits imposed by algorithms’ complexity. As a matter of fact, the best solutions will certainly mix the two approaches, with enough flexibility to adapt to the propagation conditions

3. Performance Prediction Within the ongoing global research effort on future wireless communications systems, adaptive allocation of time, code, space and frequency resources based on channel state information (CSI) and users' requirements is widely recognized as a key feature to approach the capacity of MIMO broadband frequency-selective channels. The traditional Radio Resource Management (RRM) and Slow link adaptation (SLA) have been built on a link-to-system interface, referred to as average value interface[3], in which the individual radio link performance is evaluated through Monte-Carlo simulations averaged over the fast fading statistics. For this approach to be valid the RRM and LA timescales must be large compared to the fast fading dynamics. On the opposite, current wireless systems evolve toward an enhanced reactivity of RRM and Fast Link Adaptation (FLA) protocols in order to jointly optimize the medium access control and physical layers. A new type of link-to-system interface, referred to as actual value interface [3], has emerged in which advanced RRM and FLA mechanisms are designed and optimized so as to exploit feedback metrics representative of the instantaneous individual radio link performance (based on performance prediction methods). In parallel, the success of turbo codes [4] has inspired new potentially capacity achieving coded modulations and, through the so-called turbo principle [5], revolutionised the reception theory. New spatial multiplexing architectures and non-orthogonal multiple-access techniques based on powerful coding schemes have been proposed to achieve very high spectral efficiency, whose relevance is, however, conditional upon iterative processing at the receiver. These two trends, namely, Cross Layer optimization and turbo processing, call for the developpment of new physical layer abstractions that can capture the iterative receiver performance per iteration and per user conditional to a given channel estimate. This was recently tackled in ARTIST4G [6] for the class of iterative Minimum-Mean-Square-Error-Interference Cancellation (MMSE-IC) joint decoding receivers (or also known as Turbo-SIC). In [7] the proposed L2S was applied to a an uplink Multi-User MIMO scenario with perfect channel state information at receiver. In [8] the proposed L2S was extended to semi-blind channel estimation in a Single User MIMO scenario. Results for both L2S types are presented in the following, for more details the interested reader is referred to the documents cited above. Further improvement of the L2S is still ongoing. The main final objective within ARTIST4G consists in designing LA and scheduling algorithms based on these physical layer abstractions. More details on this are given in Sec.4.

Multi User MIMO perfect CSIR

The case of two users equipped with 2 transmit antennas communicating simultaneously with a four antenna base station is considered. One link between one transmit and one receive antenna always experiences a 3-tap channel model. Each tap follows a Rayleigh distribution with an equi-power repartition profile between the taps (EQ-3 model). The


modulation and coding scheme for each user is a Space Time Bit Interleaved Coded Modulation (ST-BICM) based on QPSK modulation and rate ½ convolutional code. The proposed L2S revealed very accurate for this choice of modulation and coding scheme as shown in Fig. 3.

-5 0 5 1010

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Iteration 1 - User 1

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-2 0 2 4 610

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InstantaneousFER

0 2 4 610

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Figure 3: MU-MIMO perfect CSIR, Each blue point gives the instantaneous simulated BLER and the red curves the predicted one.

Single User MIMO imperfect CSIR

Single user ST-BICM transmitted on a frequency selective (EQ3) Block fading (P=2) 4x4 MIMO channel, Rate-1/2 �binary Non-Recursive Non-Systematic Convolutional (NRNSC) code with generator polynomials (5, 7)�, pseudo-random interleaver and QPSK. The channel is re-estimated at each iteration and the first channel estimate in the first iteration (it. 1) is based on pilot only whose mean squared error is denoted as MSE it.1 in Fig.4. The proposed L2S extension is able to capture the channel estimate MSE per iteration and thus it is very accurate for this choice of modulation and coding scheme as demonstrated in Fig. 4 where a comparison between prediction and simulation of the average BLER for varied MSE it.1 is shown.


Figure 4: Simulated average BLER vs. predicted average BLER for QPSK with different initial channel estimate MSE at SNR=1dB. The pairs of simulation and prediction are results are for different iteration

numbers (up to 5) in the iterative receiver

4. Flexible Interference Control In the context of ARTIST4G, the concept of Flexible Interference Control (FIC) procedures has been introduced in order to identify and develop possible combinations of innovations in terms of interference management, i.e., interference avoidance and interference exploitation, that may help to optimize the performance of the network in different operating scenarios. The interference avoidance schemes to be considered can be classified into two main groups:

1. Signal processing algorithms for interference avoidance like new methods for the multi-user multi-cell MIMO beamforming, either with Joint Processing (JP) CoMP or with coordinated beamforming, and 3D beamforming.

2. Resource allocation and scheduling algorithms for interference avoidance like enhanced ICIC and coordinated scheduling mechanisms.

Interference exploitation schemes considered are interference cancellation techniques and cooperative receivers.

For the discovery of FIC concepts, a methodology has been proposed which encompasses several steps:

1. Firstly, the compatibility of the different innovations is analysed in order to identify if any combination is precluded.

2. A second step in the methodology intends to identify those innovations that may be complementary, in the sense that they may be used either in conjunction or in different operational conditions to improve the overall network capacity or any other KPI.


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Figure 5: Methodology for the identification of FIC mechanisms

Four FIC concepts have been identified and proposed for evaluation:

• Joint Transmission Interference Cancellation Receiver (JTICR). The concept proposed here is based on the combination of Joint Transmission and successive interference cancellation receivers for a more efficient operation of the downlink. The two modes in the concept are not applied simultaneously but in different operational conditions, mainly associated with the operation in HetNet environments. The two modes are considered to be compatible in the sense that they require a similar receiver structure in the UE, and complementary in the sense that they are useful in different interference situations. Joint transmission is expected to be used at the cell edge, whilst interference cancellation will be used in high interference conditions, e.g., in the range extension area of a femtocell. In addition to these two operating modes, a third one, denominated baseline, is also considered, which should be used in normal operating conditions.

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Figure 6: JTICR FIC concept possible states and transitions

• Full signal and channel IC in combination with ABS adaptation. The idea for this concept is based on the fact that almost blank subframes (ABS) are used in heterogeneous deployments with range expansion in downlink direction to avoid interference to resources used for data channel transmissions but there is still the need for interference cancellation due to LTE legacy signal and channel interference. The FIC concept proposes to adapt ABS patterns to the current situation in the system and to the receiver capabilities. Interference cancellation on synchronization signals, pilot signal, and broadcasting channel (CRS, PSS, SSS, and PBCH) from the interfering macro is applied in the UE receiver to extend the range of picos and ultimately a pronounced offloading effect with improved association to picos and improved served throughput.


• Coordinated scheduling for advanced receivers. The concept looks at the feedback information required to support coordinated scheduling to UEs that incorporate advanced receivers. It is also proposed to use power control to facilitate the interference cancellation.

• Adaptive Resource Allocation Algorithms for Multiuser MIMO Systems with Iterative MMSE-IC Joint Decoding. The concept proposed looks at resource allocation algorithms that allow taking advantage of Iterative MMSE-IC receivers at the base station to maximize the capacity in Multiuser MIMO systems, taking into account the discrete channel inputs and non-ideal modulation-coding schemes (MCS) present in practical systems.

The support of these concepts has implications on the backhaul capabilities in terms of delay and synchronization required in the involved nodes, as well as the definition of new signaling processes.

5. Conclusions Applying interference cancellation to practical systems like heterogeneous deployments of 3GPP LTE needs more than implementing advanced receiver algorithms. Real-world impacts should be added that could limit the benefits of the algorithms under consideration. The performance has to be predictable for RRM and system-level evaluation – a non-trivial task given that the algorithms at hand are non-linear and do not only depend on the signal-plus-noise-and-intererence-ratio but on the combination of signal-to-noise-ratio and signal-to-interference-ratio. And interference management needs to be taken into account. All these aspectes were treated in detail in ARTIST4G and the interested reader is referred to [6,9,10] for a much more detailed elaboration of those topics.

Acknowledgment The research leading to these results has received funding from the European Commission’s seventh framework program FP7-ICT-2009 under grant agreement no 247223 also referred to as ARTIST4G.

References [1] M. Grieger, P. Marsch, and G. Fettweis, “Large scale field trial results on uplink CoMP with multi antenna base stations”, in VTC-Fall 2011] [2] G. Caire and S. Shamai, "On the Achievable Throughput of a Multiantenna Gaussian Broadcast Channel," in IEEE Transactions on Information Theory, 2003. [3] S. Hämäläinen, P. Slanina, M. Hartman, A. Lappeteläinen, and H. Holma, ``A novel interface between link and system level simulations,'' Proc. ACTS Mobile Telecommunications Summit'97, Aalborg, Denmark,pp. 599--604, Oct. 1997. [4] C. Berrou, A. Glavieux, and P. Thitimajshima, ``Near Shannon limit error-correcting coding and decoding,'' Proc. IEEE ICC'93, Geneva, Switzerland, pp. 1064--1070, May 1993. [5] J. Hagenauer, ``The turbo principle: Tutorial introduction and state of the art,'' Proc. 1st International Symposium on Turbo Codes, Brest, France, pp. 1--12, Sept. 1997. [6] D2.3 - Advanced link-to-system modelling ARTIST4G - Deliverable https://ict-artist4g.eu/projet/deliverables. (2011, Mar.) [7] R. Visoz, A. O. Berthet, and M. Lalam, “Semi-Analytical Performance Prediction Methods for Iterative MMSE-IC Multiuser MIMO Joint Decoding,” IEEE Trans. Commun., vol. 58, no. 9, Sept. 2010. [8] B. Ning, R. Visoz, A.O. Berthet, “Semi-Analytical Performance Prediction Method for Iterative MMSE-IC Detection and Semi-blind Channel Estimation,” Proc. IEEE VTC’11 Spring, Hungrary, Budapest, May 2011 [9] D2.2 – Advanced receiver signal processing techniques – evaluation and characterization – Deliverable https://ict-artist4g.eu/projet/deliverables. (2010, Dec.)


[10] D2.4 - Flexible interference control – concepts – Deliverable https://ict-artist4g.eu/projet/deliverables. (2011, June)

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