Interference Management for Future Wireless Networks
Communication Infrastructure, ACCESS, KTH
Abstract We will investigate efficient interference management strategies for wireless networks. With
increasing user-amount and data-rate, future wireless networks are characterized as interference-limited.
However, truly efficient approaches for combating interference are still largely unknown. This project aims
at developing theoretical limits and practical design principles for interference-limited wireless networks.
1. Introduction
Future wireless systems are characterized by large user-capacity, high speed, and high reliability. Yet,
fading and interference in wireless networks render these design objectives challenging. While the
development of diversity technologies such as MIMO, cooperative communications, relaying and multi-user
diversity, partially address wireless fading (and can in some circumstances even be utilized for increased
performance), truly efficient strategies for combating fading or further exploiting multi-user interference are
lacking, at least until very recent years.
One efficient interference mitigation approach is joint multi-cell processing (normally referred to as CoMP:
coordinated multiple point processing) [1], [2], [3]. Figure 1 shows a group of mobile terminals lie close to
the edges of cell boundaries. When they access the different base station by non-orthogonal channels, their
signals may interfere with each other. Joint processing across multi-cell, supported by the fiber backhaul can
greatly reduce the interference. Significant gains are also possible with relatively low degrees of cooperation
between the cells, for example by exchanging a limited amount of information related to the scheduling and
pre-coding used for the terminals that suffer most of the intercell interference. In such systems, it is
interesting to study algorithms that work in a distributed fashion over the network, reducing the need for
backhaul information exchange. All interference suppression schemes that operate at the transmitter side
require some amount of channel state information which has to be estimated at the receivers and fed back to
the transmitters; this reduces the spectral efficiency. Therefore, it is interesting to study schemes that use a
minimum number of feedback bits. The unavoidable delay between estimation and use of the channel state
information may affect the convergence properties of the decentralized resource management algorithms.
Figure 1: An example of CoMP. Users in the cellular boundaries interference each other. With the backhaul
support and CSIT, efficient interference management approaches can be used.
Currently, multi-cell processing mainly focuses on signal domain processing. Alternatively, one can use
finite field approaches, e.g., schemes inspired by network coding [4]. The benefits of finite-field network
coding over superposition coding (signal domain) have been shown recently in cooperative networks [5] or
relaying networks [6]. More complex network topologies, e.g. with multiple relay nodes, have so far received
little attention in the literature.
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2. Project description
This project aims at increasing the transmission efficiency, e.g., energy efficiency, error probability,
throughput, and network capacity for interference-limited wireless networks. Specifically, the following
aspects (or WPs) should be considered.
(1). Network coding for interference mitigation: We shall study how to mitigate or further exploit
interference [7] by network coding approaches. Theoretical limits and practical design principles shall be
studied, especially for the coding scheme in finite fields. Multi-dimensional codes with high complexity or
one-dimensional codes with low complexity shall be studied. The rate-reliability tradeoff shall also be
considered in the high SNR region. We shall also study the joint processing gain for the cellular networks
with relaying. Finite-field or soft network coding schemes (superposition in particular) shall be compared for
different scenarios. Information theoretic bounds and schemes inspired by positive coding theorems is also
considered. Preliminary work in this direction includes [8] where new achievable rates for an uplink with
collaborating base-stations were presented.
(2). Interference alignment [9]: Interference alignment is a new concept recently proposed for wireless
networks. The motivation of interference alignment is the principle of do-no-harm rather than winner-takesall. By coordinating the transmissions across a simple network the interference experienced at each receiver
can be perfectly aligned and subsequently easily mitigated or removed. This strategy has so far mainly been
investigated in terms of information-theoretic limits, and very little has been reported on the practical
realization of interference alignment. In this work, we propose to conduct a thorough investigation into the
application of interference alignment as a critical component in an overall interference management strategy.
Since inter-cell interference in downlink transmission has been recognized as a key bottleneck issue, we
particular focus on the downlink scenario. However, similar principles may be applied for uplink
transmission.
(3) Performance and complexity assessment: The development of new communication technologies and
concepts is greatly aided by the simultaneous development of analysis tools and techniques for assessing, and
further improving, the performance and implementation complexity of these technologies. This is made even
more pertinent by the often very high-dimensional signaling schemes envisioned in the interference
mitigation technologies under development. We therefore propose the application of tools such as (but not
limited to) large deviation theory to develop practically useful models for complexity and performance in
interference mitigation technologies. Preliminary work in this direction includes [10] which consider low
complexity joint decoding of several users in a MIMO-MAC setting.
(4) Radio resource allocation based on low-rate feedback: Several code-book based schemes have been
proposed to do scheduling and/or pre-coding and rate allocation based on low rate channel feedback.
However, most such schemes use separate feedback bits for scheduling and for pre-coding. We will study
schemes where the feedback scheme is optimized to use all the feedback bits jointly for all these purposes,
extending the preliminary results in [11] to also allow the scheduler to do multicasting. We will also study
the use of low-rate feedback for coordinated multipoint systems.
(5) System theoretic analysis of decentralized schemes: Decentralized resource allocation schemes may be
viewed as distributed control systems. We intend to use the joint expertise from the Optimization and
Systems Theory, Automatic Control and Signal Processing labs to analyze the convergence and robustness
properties of some typical decentralized resource allocation scheme.
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3. Deliverables.
D1. A report entitled “Jointly processing gain for backhaul support wireless networks with relaying”,
prospective joint work between communication theory and signal processing group.
D2. A paper entitled “Cross-layer resource allocation and coding for multiple relay cooperative
communications”, prospective joint work between communication theory and automatic control group.
D3. A paper entitled “Decoding complexity of multiple users operating at high spectral efficiencies”, with
prospective joint work between signal processing and communications theory groups.
D4. A paper is entitled “Scheduling and Precoding for Mixed Broadcast/Multicast Channels with Quantized
Channel Feedback”, with prospective joint work between signal processing and communications theory
groups.
D5. A paper on the topic, “Analysis of Convergence and Dynamics of a Decentralized Radio Resource
Allocation Scheme,” with prospective joint work by signal processing, automatic control and optimization
and system theory groups.
D6. A paper on the topic, “Resource allocation for coordinated multipoint systems” with prospective joint
work by signal processing and automatic control groups.
4. Project management
The proposed project is hosted by four research groups of ACCESS, namely, communication theory,
signal processing, automatic control and Optimization and System Theory. The project is co-lead by Ming
Xiao and Joakim Jaldén, the participating senior faculty are Björn Ottersten, Mats Bengtsson, Mikael
Skoglund, Mikael Johansson and Ulf Jonsson. Several PostDocs and Ph.D students will also be involved in
the project.
References
[1] S. Shamai, and B. Zaidel, “Enhancing the Cellular Downlink Capacity via CO-Processing at the Transmitting End,” IEEE Veh.
Tech. Conf. 2001.
[2] O. Somekh, B. Zaidel, S. Shamai, “Sum rate characterization of joint multiple cell-site processing,” IEEE Transactions on
Information Theory, Dec., 2007.
[3] S. Jing, D. Tse, J. Soriaga, et. al, “Multicell Downlink Capacity with Coordinated Processing,” EURASIP Journal onWireless
Communications and Networking, 2008.
[4] R. Ahlswede, N. Cai, S.-Y. Li, and R. Yeung, “Network information flow,” IEEE Transactions on Information Theory, vol. 46, no.
4, pp. 1204–1216, 2000.
[5] L. Xiao, T. Fuja, J. Kliewer, and D. Costello, “A network coding approach to cooperative diversity,” IEEE Transactions on
information theory, October, 2007.
[6] M. Xiao, and M. Skoglund, “Design of network codes for multi-user multi-relay networks,” IEEE ISIT July 2009.
[7] S. Katti, S. Gollakota, and D. Katabi, ”Embracing wireless interference: analog network coding”, ACM, SIGCOMM, 2007.
[8] H. Do, T. Oechtering, M. Skoglund "An achievable rate region for the Gaussian Z-interference channel with conferencing," in
Proc. Allerton Conference on Communication, Control, and Computing 2009
[9] Interference Alignment and Degrees of Freedom of the K-User Interference Channel, Cadambe, V.R.; Jafar, S.A.;
Information Theory, IEEE Transactions on, Volume 54, Issue 8, Aug. 2008 Page(s):3425 – 3441.
[10] P. Elia and J. Jalden, “General DMT optimality of LR-aided linear MIMO-MAC transceivers with worst-case complexity at
most linear in sum-rate”, 2010 IEEE Information Theory Workshop, Jan. 2010, accepted for publication.
[11] T. T. Kim, M. Bengtsson, and M. Skoglund, "Quantized feedback design for MIMO broadcast channels," in Proc. IEEE ICASSP
2007, April 2007
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