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Multi-Cell MIMO Cooperative Networks: A New Look at Interference
- J. Selec. Areas in Commun. (JSAC
, 2010
"... Abstract—This paper presents an overview of the theory and currently known techniques for multi-cell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacitylimiting factor, multi-cell cooperation can dramatically improv ..."
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Cited by 257 (40 self)
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Abstract—This paper presents an overview of the theory and currently known techniques for multi-cell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacitylimiting factor, multi-cell cooperation can dramatically improve the system performance. Remarkably, such techniques literally exploit inter-cell interference by allowing the user data to be jointly processed by several interfering base stations, thus mimicking the benefits of a large virtual MIMO array. Multicell MIMO cooperation concepts are examined from different perspectives, including an examination of the fundamental information-theoretic limits, a review of the coding and signal processing algorithmic developments, and, going beyond that, consideration of very practical issues related to scalability and system-level integration. A few promising and quite fundamental research avenues are also suggested. Index Terms—Cooperation, MIMO, cellular networks, relays, interference, beamforming, coordination, multi-cell, distributed.
Methodologies for analyzing equilibria in wireless games
- IEEE Signal Processing Magazine, Special issue on Game Theory for Signal Processing
, 2009
"... Under certain assumptions in terms of information and models, equilibria correspond to possible stable outcomes in conflicting or cooperative scenarios where intelligent entities (e.g., terminals) interact. For wireless engineers, it is of paramount importance to be able to predict and even ensure s ..."
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Cited by 48 (25 self)
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Under certain assumptions in terms of information and models, equilibria correspond to possible stable outcomes in conflicting or cooperative scenarios where intelligent entities (e.g., terminals) interact. For wireless engineers, it is of paramount importance to be able to predict and even ensure such states at which the network will effectively operate. In this article, we provide non-exhaustive methodologies for characterizing equilibria in wireless games in terms of existence, uniqueness, selection and efficiency.
Cooperative multicell precoding: Rate region characterization and distributed strategies with instantaneous and statistical CSI
- IEEE Trans. Signal Process
"... IEEE does not in any way imply IEEE endorsement of any of the Royal Institute of Technology (KTH)’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collect ..."
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Cited by 43 (2 self)
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IEEE does not in any way imply IEEE endorsement of any of the Royal Institute of Technology (KTH)’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to
Joint Base Station Clustering and Beamformer Design for Partial Coordinated Transmission in Heterogeneous Networks
, 2012
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Optimal Beamforming in Interference Networks with Perfect Local Channel Information
, 2010
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Distributed Interference Pricing for the MIMO Interference Channel
- PROC. IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS
, 2009
"... Abstract—We study distributed algorithms for updating transmit precoding matrices for a two-user Multi-Input/Multi-Output (MIMO) interference channel. Our objective is to maximize the sum rate with linear Minimum Mean Squared Error (MMSE) receivers, treating the interference as additive Gaussian noi ..."
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Cited by 23 (3 self)
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Abstract—We study distributed algorithms for updating transmit precoding matrices for a two-user Multi-Input/Multi-Output (MIMO) interference channel. Our objective is to maximize the sum rate with linear Minimum Mean Squared Error (MMSE) receivers, treating the interference as additive Gaussian noise. An iterative approach is considered in which given a set of precoding matrices and powers, each receiver announces an interference price (marginal decrease in rate due to an increase in interference) for each received beam, corresponding to a column of the precoding matrix. Given the interference prices from the neighboring receiver, and also knowledge of the appropriate cross-channel matrices, the transmitter can then update the beams and powers to maximize the rate minus the interference cost. Variations on this approach are presented in which beams are added sequentially (and then fixed), and in which all beams and associated powers are adjusted at each iteration. Numerical results are presented, which compare these algorithms with iterative water-filling (which requires no information exchange), and a centralized optimization algorithm, which finds locally optimal solutions. Our results show that the distributed algorithms perform close to the centralized algorithm, and by adapting the rank of the precoder matrices, achieve the optimal high-SNR slope. I.
Monotonic Convergence of Distributed Interference Pricing in Wireless Networks
"... Abstract—We study distributed algorithms for allocating powers and/or adjusting beamforming vectors in a peer-to-peer wireless network which may have multiple-input-single-output (MISO) links. The objective is to maximize the total utility summed over all users, where each user’s utility is a functi ..."
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Cited by 19 (3 self)
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Abstract—We study distributed algorithms for allocating powers and/or adjusting beamforming vectors in a peer-to-peer wireless network which may have multiple-input-single-output (MISO) links. The objective is to maximize the total utility summed over all users, where each user’s utility is a function of the received signal-to-interference-plus-noise ratio (SINR). Each user (receiver) announces an interference price, representing the marginal cost of interference from other users. A particular user (transmitter) then updates its power and beamforming vector to maximize its utility minus the interference cost to other users, which is determined from their announced interference prices. We show that if each transmitter update is based on a current set of interference prices and the utility functions satisfy certain concavity conditions, then the total utility is non-decreasing with each update. The proof is based on the convexity of the utility functions with respect to received interference, and applies to rate utility functions, and an arbitrary number of interfering MISO links. The extension to multi-carrier links is discussed as well as algorithmic variations in which the prices are not immediately updated after power or beam updates. I.
Distributed Resource Allocation Schemes: Pricing Algorithms for Power Control and Beamformer Design in Interference Networks
"... Achieving high spectral efficiencies in wireless networks requires the ability to mitigate and manage the associated interference. This becomes especially important in networks where many transmitters and receivers are randomly placed, so that in the absence of coordination a particular receiver is ..."
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Cited by 16 (0 self)
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Achieving high spectral efficiencies in wireless networks requires the ability to mitigate and manage the associated interference. This becomes especially important in networks where many transmitters and receivers are randomly placed, so that in the absence of coordination a particular receiver is likely to encounter significant interference from a neighboring transmitter. A challenge is then to provide a means
Joint linear precoder optimization and base station selection for an uplink MIMO network: A game theoretic approach
- in the Proceedings of the IEEE ICASSP
, 2012
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Local interference pricing for distributed beamforming
- in MIMO networks,” in Proc. MILCOM,
, 2009
"... Abstract-We study a distributed algorithm for adjusting beamforming vectors in a peer-to-peer wireless network with multiple-input multiple-output (MIMO) channels. Each transmitter precoding matrix has rank one, and a linear minimum mean squared error (MMSE) filter is applied at each receiver. Our ..."
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Cited by 11 (0 self)
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Abstract-We study a distributed algorithm for adjusting beamforming vectors in a peer-to-peer wireless network with multiple-input multiple-output (MIMO) channels. Each transmitter precoding matrix has rank one, and a linear minimum mean squared error (MMSE) filter is applied at each receiver. Our objective is to maximize the total utility summed over all users, where each user's utility is a function of the received signal-to-interference-plus-noise ratio (SINR). Given all users' beamforming vectors and receive filters, each receiver announces an interference price, representing the marginal cost of interference from other users. A particular transmitter updates its beamforming vector to maximize its utility minus the interference cost to other users. We show that if the utility functions satisfy certain concavity conditions, then the total utility is non-decreasing with each update. We also present numerical results that illustrate the effect of ignoring interference prices from all but the closest users, and relaxing requirements on the frequency of beam and price updates.
