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29
Dynamic resource allocation in cognitive radio networks
 IEEE Signal Process. Mag
, 2010
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Multiuser MISO transmitter optimization for intercell interference mitigation
 IEEE Trans. Signal Process
, 2010
"... The transmitter optimization (i.e., steering vectors and power allocation) for a MISO Broadcast Channel (MISOBC) subject to general linear constraints is considered. Such constraints include, as special cases, the sum power, the perantenna or pergroupofantennas power, and “forbidden interferenc ..."
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Cited by 18 (3 self)
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The transmitter optimization (i.e., steering vectors and power allocation) for a MISO Broadcast Channel (MISOBC) subject to general linear constraints is considered. Such constraints include, as special cases, the sum power, the perantenna or pergroupofantennas power, and “forbidden interference direction ” constraints. We consider both the optimal dirtypaper coding and the simple suboptimal linear zeroforcing beamforming strategies, and provide numerically efficient algorithms that solve the problem in its most general form. As an application, we consider a multicell scenario with partial cell cooperation, where each cell optimizes its precoder by taking into account interference constraints on specific users in adjacent cells. The effectiveness of the proposed methods is evaluated in a simple system scenario including two adjacent cells, under different fairness criteria that emphasize the bottleneck role of users near the cell “boundary”. Our results show that “active ” InterCell Interference (ICI) mitigation outperforms the conventional “static ” ICI mitigation based on fractional frequency reuse. Index Terms MISO broadcast channel, convex optimization, dirty paper coding, zero forcing beamforming, multicell scheduling, intercell interference mitigation.
MIMO Broadcast Channel Optimization under General Linear Constraints
, 901
"... Abstract — The optimization of the transmit parameters (powers and steering vectors) for the MIMO BC under general linear constraints is treated under the optimal DPC coding strategy and the simple suboptimal linear zeroforcing beamforming strategy. In the case of DPC, we show that “SINR duality ” ..."
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Cited by 14 (1 self)
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Abstract — The optimization of the transmit parameters (powers and steering vectors) for the MIMO BC under general linear constraints is treated under the optimal DPC coding strategy and the simple suboptimal linear zeroforcing beamforming strategy. In the case of DPC, we show that “SINR duality ” and “minmax duality ” yield the same dual MAC problem, and compare two alternatives for its efficient solution. In the case of zeroforcing beamforming, we provide a new efficient algorithm based on the direct optimization of a generalized inverse matrix. In both cases, the algorithms presented here address the problems in the most general form and can be applied to special cases previously considered, such as perantenna and pergroup of antennas power constraints, “forbidden interference direction ” constraints, or any combination thereof. I. MODEL AND BACKGROUND One channel use of the MIMO BC with an Mantenna transmitter and K singleantenna receivers is defined by yk = h H k x + zk, k = 1,..., K (1) where hk, x ∈ C M are the channel vector of user k and the transmitted signal vector, respectively, and zk ∼ CN (0, 1) is AWGN. The relevance of the above model for the downlink of a wireless system has been widely discussed. Also, the impact of nonideal channel state information and practical techniques for channel estimation and channel state feedback are wellunderstood (see for example [1], [2] and references therein). Here, we assume fixed channel vectors perfectly known to all terminals and focus on the optimization of the transmitter parameters. Let S denote a compact set of M ×M covariance matrices. The capacity region of the MIMO BC (1) subject to the input constraint E[xxH] = Σx ∈ S is given by the set of rate points
Multicell MIMO downlink with cell cooperation and fair scheduling: A largesystem limit analysis
 IEEE Trans. Inform. Theory
, 2011
"... We consider the downlink of a cellular network with multiple cells and multiantenna base stations, including a realistic distancedependent pathloss model, clusters of cooperating cells, and general “fairness ” requirements. Beyond Monte Carlo simulation, no efficient computation method to evaluat ..."
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Cited by 12 (1 self)
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We consider the downlink of a cellular network with multiple cells and multiantenna base stations, including a realistic distancedependent pathloss model, clusters of cooperating cells, and general “fairness ” requirements. Beyond Monte Carlo simulation, no efficient computation method to evaluate the ergodic throughput of such systems has been presented so far. We propose an analytic solution based on the combination of large random matrix results and convex optimization. The proposed method is computationally much more efficient than Monte Carlo simulation and provides surprisingly accurate approximations for the actual finitedimensional systems, even for a small number of users and base station antennas. Numerical examples include 2cell linear and threesectored 7cell planar layouts, with no intercell cooperation, sector cooperation, or full intercell cooperation. Index Terms Asymptotic analysis, fairness scheduling, intercell cooperation, largesystem limit, multicell MIMO downlink, weighted sum rate maximization.
Multiantenna wireless powered communication with energy beamforming.” (available online at arXiv:1312.1450
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An overview of massive MIMO: Benefits and challenges
 IEEE J. Sel. Topics Signal Process
, 2014
"... less communications refers to the idea equipping cellular base stations (BSs) with a very large number of antennas, and has been shown to potentially allow for orders of magnitude improvement in spectral and energy efficiency using relatively simple (linear) processing. In this paper, we present a c ..."
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Cited by 8 (4 self)
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less communications refers to the idea equipping cellular base stations (BSs) with a very large number of antennas, and has been shown to potentially allow for orders of magnitude improvement in spectral and energy efficiency using relatively simple (linear) processing. In this paper, we present a comprehensive overview of stateoftheart research on the topic, which has recently attracted considerable attention. We begin with an information theoretic analysis to illustrate the conjectured advantages of massive MIMO, and then we address implementation issues related to channel estimation, detection and precoding schemes. We particularly focus on the potential impact of pilot contamination caused by the use of nonorthogonal pilot sequences by users in adjacent cells. We also analyze the energy efficiency achieved by massive MIMO systems, and demonstrate how the degrees of freedom provided by massive MIMO systems enable efficient singlecarrier transmission. Finally, the challenges and opportunities associated with implementing massive MIMO in future wireless communications systems are discussed. Index Terms—Channel estimation, energy efficiency, massive MIMO systems, orthogonal frequency division multiplexing (OFDM), pilot contamination, precoding and detection, singlecarrier transmission, spectral efficiency, timedivision duplexing (TDD). I.
Maxmin SINR coordinated multipoint downlink transmission–duality and algorithms
 IEEE Trans. Signal Process
, 2012
"... Abstract—This paper considers the maxmin weighted signaltointerferenceplusnoise ratio (SINR) problem subject to multiple weightedsum power constraints, where the weights can represent relative power costs of serving different users. First, we study the power control problem. We apply nonlinear ..."
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Cited by 6 (3 self)
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Abstract—This paper considers the maxmin weighted signaltointerferenceplusnoise ratio (SINR) problem subject to multiple weightedsum power constraints, where the weights can represent relative power costs of serving different users. First, we study the power control problem. We apply nonlinear PerronFrobenius theory to derive closedform expressions for the optimal value and solution and an iterative algorithm which converges geometrically fast to the optimal solution. Then, we use the structure of the closedform solution to show that the problem can be decoupled into subproblems each involving only one power constraint. Next, we study the multipleinputsingleoutput (MISO) transmit beamforming and power control problem. We use uplinkdownlink duality to show that this problem can be decoupled into subproblems each involving only one power constraint. We apply this decoupling result to derive an iterative subgradient projection algorithm for the problem. Index Terms—Beamforming, multipleinputmultipleoutput (MIMO), uplinkdownlink duality.
Polite Waterfilling for Weighted Sumrate Maximization in MIMO BMAC Networks under Multiple Linear Constraints
"... Abstract—The algorithms in this paper exploit optimal input structure in interference networks and is a major advance from the stateoftheart. Optimization under multiple linear constraints is important for interference networks with individual power constraints, perantenna power constraints, and ..."
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Cited by 4 (2 self)
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Abstract—The algorithms in this paper exploit optimal input structure in interference networks and is a major advance from the stateoftheart. Optimization under multiple linear constraints is important for interference networks with individual power constraints, perantenna power constraints, and/or interference constraints as in cognitive radios. While for singleuser MIMO channel transmitter optimization, no one uses general purpose optimization algorithms such as steepest ascent because waterfilling is optimal and much simpler, this is not true for MIMO multiaccess channels (MAC), broadcast channels (BC), and the nonconvex optimization of interference networks because the traditional waterfilling is far from optimal for networks. We recently found the right form of waterfilling, polite waterfilling, for some capacity/achievable regions of the general MIMO interference networks, named BMAC networks, which include BC, MAC, interference channels, X networks, and most practical wireless networks as special cases. In this paper, we use weighted sumrate maximization under multiple linear constraints in interference tree networks, a natural extension of MAC and BC, as an example to show how to design highly efficiency and low complexity algorithms. Several times faster convergence speed and orders of magnitude higher accuracy than the stateoftheart are demonstrated by numerical examples.
Multicell random beamforming: achievable rate and degrees of freedom region
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Nonlinear Precoding Design for MIMO AmplifyandForward TwoWay Relay Systems
"... Abstract—In traditional multipleinput–multipleoutput (MIMO) channel and MIMO oneway relay system, nonlinear precoding design has shown significant performance gain over linear design. In this paper, we aim to study nonlinear precoding design for MIMO amplifyandforward (AF) twoway relay systems ..."
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Abstract—In traditional multipleinput–multipleoutput (MIMO) channel and MIMO oneway relay system, nonlinear precoding design has shown significant performance gain over linear design. In this paper, we aim to study nonlinear precoding design for MIMO amplifyandforward (AF) twoway relay systems, where nonlinear minimal mean square error (MMSE) decision feedback equalizers (DFEs) are used in two destinations, and linear transmit precoding is applied at the source and relay nodes. We first investigate nonlinear precoding design, where the precoding is only conducted at two sources for a fixedrelay precoder. After some transformations, we prove that this design problem is convex, and an efficient algorithm is provided to find the optimal solution. Then, we consider the nonlinear joint precoding design to further incorporate relay precoding. Due to the nonconvexity of this problem, we first propose an iterative algorithm (Algorithm I) to approach the optimal solution. It is proven that Algorithm I is convergent and can converge to a stationary point of the joint design problem. Moreover, we present a simplified iterative algorithm (Algorithm II) for joint precoding design to reduce the design complexity. It is found that Algorithm II almost achieves the same performance as Algorithm I in most cases. Our simulation results show that the proposed nonlinear joint precoding design significantly outperforms the linear joint precoding design. It is also shown that the choice between the proposed nonlinear source precoding design and the linear relay precoding design is dependent on specific conditions. Index Terms—Minimum mean square error (MMSE), multipleinput–multipleoutput (MIMO), nonlinear precoding, nonregenerative relay, twoway relaying. I.