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154
MultiCell 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 multicell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacitylimiting factor, multicell 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 multicell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacitylimiting factor, multicell cooperation can dramatically improve the system performance. Remarkably, such techniques literally exploit intercell 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 informationtheoretic limits, a review of the coding and signal processing algorithmic developments, and, going beyond that, consideration of very practical issues related to scalability and systemlevel integration. A few promising and quite fundamental research avenues are also suggested. Index Terms—Cooperation, MIMO, cellular networks, relays, interference, beamforming, coordination, multicell, distributed.
Transmitter Optimization for the MultiAntenna Downlink with PerAntenna Power Constraints
 IEEE TRANSACTIONS ON SIGNAL PROCESSING
, 2007
"... This paper considers the transmitter optimization problem for a multiuser downlink channel with multiple transmit antennas at the basestation. In contrast to the conventional sumpower constraint on the transmit antennas, this paper adopts a more realistic perantenna power constraint, because in ..."
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Cited by 135 (7 self)
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This paper considers the transmitter optimization problem for a multiuser downlink channel with multiple transmit antennas at the basestation. In contrast to the conventional sumpower constraint on the transmit antennas, this paper adopts a more realistic perantenna power constraint, because in practical implementations each antenna is equipped with its own power amplifier and is limited individually by the linearity of the amplifier. Assuming perfect channel knowledge at the transmitter, this paper investigates two different transmission schemes under the perantenna power constraint: a minimumpower beamforming design for downlink channels with a single antenna at each remote user and a capacityachieving transmitter design for downlink channels with multiple antennas at each remote user. It is shown that in both cases, the perantenna downlink transmitter optimization problem may be transformed into a dual uplink problem with an uncertain noise. This generalizes previous uplink–downlink duality results and transforms the perantenna transmitter optimization problem into an equivalent minimax optimization problem. Further, it is shown that various notions of uplink–downlink duality may be unified under a Lagrangian duality framework. This new interpretation of duality gives rise to efficient numerical optimization techniques for solving the downlink perantenna transmitter optimization problem.
Exploiting multiantennas for opportunistic spectrum sharing in cognitive radio networks
 IEEE J. Select. Topics in Signal Processing
, 2008
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Coordinated beamforming for the multicell multiantenna wireless system
 IEEE Trans. Wireless Commun
"... Abstract—In a conventional wireless cellular system, signal processing is performed on a percell basis; outofcell interference is treated as background noise. This paper considers the benefit of coordinating basestations across multiple cells in a multiantenna beamforming system, where multiple ..."
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Cited by 120 (6 self)
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Abstract—In a conventional wireless cellular system, signal processing is performed on a percell basis; outofcell interference is treated as background noise. This paper considers the benefit of coordinating basestations across multiple cells in a multiantenna beamforming system, where multiple basestations may jointly optimize their respective beamformers to improve the overall system performance. This paper focuses on a downlink scenario where each remote user is equipped with a single antenna, but where multiple remote users may be active simultaneously in each cell. The design criterion is the minimization of the total weighted transmitted power across the basestations subject to signaltointerferenceandnoiseratio (SINR) constraints at the remote users. The main contribution is a practical algorithm that is capable of finding the joint optimal beamformers for all basestations globally and efficiently. The proposed algorithm is based on a generalization of uplinkdownlink duality to the multicell setting using the Lagrangian duality theory. The algorithm also naturally leads to a distributed implementation. Simulation results show that a coordinated beamforming system can significantly outperform a conventional system with percell signal processing. I.
Sum Rate Characterization of Joint Multiple CellSite Processing
, 2005
"... The sumrate capacity of a cellular system model is analyzed, considering the uplink and downlink channels, while addressing both nonfading and flatfading channels. The focus is on a simple Wynerlike multicell model, where the system cells are arranged on a circle, assuming the cellsites are lo ..."
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Cited by 68 (11 self)
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The sumrate capacity of a cellular system model is analyzed, considering the uplink and downlink channels, while addressing both nonfading and flatfading channels. The focus is on a simple Wynerlike multicell model, where the system cells are arranged on a circle, assuming the cellsites are located at the boundaries of the cells. For the uplink channel, analytical expressions of the sumrate capacities are derived for intracell TDMA scheduling, and a “WideBand ” (WB) scheme (where all users are active simultaneously utilizing all bandwidth for coding). Assuming individual percell power constraints, and using the Lagrangian uplinkdownlink duality principle, an analytical expression for the sumrate capacity of the downlink channel is derived for nonfading channels, and shown to coincide with the corresponding uplink result. Introducing flatfading, lower and upper bounds on the average percell sumrate capacity are derived. The bounds exhibit an O(loge K) multiuser diversity factor for a number of users percell K ≫ 1, in addition to the array diversity gain. Joint multicell processing is shown to eliminate outofcell interference, which is traditionally considered to be a limiting factor in highrate reliable communications. This paper was presented in part at the 9
Weighted SumRate Maximization using Weighted MMSE for MIMOBC Beamforming Design
 IEEE Trans. on Wireless Comm
, 2008
"... Abstract—This paper studies linear transmit filter design for Weighted SumRate (WSR) maximization in the Multiple Input Multiple Output Broadcast Channel (MIMOBC). The problem of finding the optimal transmit filter is nonconvex and intractable to solve using low complexity methods. Motivated by r ..."
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Cited by 59 (2 self)
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Abstract—This paper studies linear transmit filter design for Weighted SumRate (WSR) maximization in the Multiple Input Multiple Output Broadcast Channel (MIMOBC). The problem of finding the optimal transmit filter is nonconvex and intractable to solve using low complexity methods. Motivated by recent results highlighting the relationship between mutual information and Minimum Mean Square Error (MMSE), this paper establishes a relationship between weighted sumrate and weighted MMSE in the MIMOBC. The relationship is used to propose two low complexity algorithms for finding a local weighted sumrate optimum based on alternating optimization. Numerical results studying sumrate show that the proposed algorithms achieve high performance with few iterations. Index Terms—MIMO systems, transceiver design, smart antennas, antennas and propagation. I.
An introduction to convex optimization for communications and signal processing
 IEEE J. SEL. AREAS COMMUN
, 2006
"... Convex optimization methods are widely used in the ..."
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Cited by 56 (2 self)
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Convex optimization methods are widely used in the
Large system analysis of linear precoding in correlated MISO broadcast channels under limited feedback
 IEEE TRANS. INF. THEORY
, 2012
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Zero Forcing Precoding and Generalized Inverses
"... We consider the problem of linear zero forcing precoding design, and discuss its relation to the theory of generalized inverses in linear algebra. Special attention is given to a specific generalized inverse known as the pseudoinverse. We begin with the standard design under the assumption of a tot ..."
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Cited by 52 (0 self)
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We consider the problem of linear zero forcing precoding design, and discuss its relation to the theory of generalized inverses in linear algebra. Special attention is given to a specific generalized inverse known as the pseudoinverse. We begin with the standard design under the assumption of a total power constraint and prove that precoders based on the pseudoinverse are optimal in this setting. Then, we proceed to examine individual perantenna power constraints. In this case, the pseudoinverse is not necessarily the optimal generalized inverse. In fact, finding the optimal inverse is nontrivial and depends on the specific performance measure. We address two common criteria, fairness and throughput, and show that the optimal matrices may be found using standard convex optimization methods. We demonstrate the improved performance offered by our approach using computer simulations.
Dynamic resource allocation in cognitive radio networks
 IEEE Signal Process. Mag
, 2010
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