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See detailCoordinated Multicell Multiuser Precoding for Maximizing Weighted Sum Energy Efficiency
He, Shiwen; Huang, Yongming; Yang, Luxi et al

in IEEE Transactions on Signal Processing (2014), 62(3), 741-751

Energy efficiency optimization of wireless systems has become urgently important due to its impact on the global carbon footprint. In this paper we investigate energy efficient multicell multiuser ... [more ▼]

Energy efficiency optimization of wireless systems has become urgently important due to its impact on the global carbon footprint. In this paper we investigate energy efficient multicell multiuser precoding design and consider a new criterion of weighted sum energy efficiency, which is defined as the weighted sum of the energy efficiencies of multiple cells. This objective is more general than the existing methods and can satisfy heterogeneous requirements from different kinds of cells, but it is hard to tackle due to its sum-of-ratio form. In order to address this non-convex problem, the user rate is first formulated as a polynomial optimization problem with the test conditional probabilities to be optimized. Based on that, the sum-of-ratio form of the energy efficient precoding problem is transformed into a parameterized polynomial form optimization problem, by which a solution in closed form is achieved through a two-layer optimization. We also show that the proposed iterative algorithm is guaranteed to converge. Numerical results are finally provided to confirm the effectiveness of our energy efficient beamforming algorithm. It is observed that in the low signal-to-noise ratio (SNR) region, the optimal energy efficiency and the optimal sum rate are simultaneously achieved by our algorithm; while in the middle-high SNR region, a certain performance loss in terms of the sum rate is suffered to guarantee the weighed sum energy efficiency. [less ▲]

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See detailCompressive Sparsity Order Estimation for Wideband Cognitive Radio Receiver
Sharma, Shree Krishna UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in IEEE Transactions on Signal Processing (2014)

Compressive Sensing (CS) has been widely investigated in the Cognitive Radio (CR) literature in order to reduce the hardware cost of sensing wideband signals assuming prior knowledge of the sparsity ... [more ▼]

Compressive Sensing (CS) has been widely investigated in the Cognitive Radio (CR) literature in order to reduce the hardware cost of sensing wideband signals assuming prior knowledge of the sparsity pattern. However, the sparsity order of the channel occupancy is time-varying and the sampling rate of the CS receiver needs to be adjusted based on its value in order to fully exploit the potential of CS-based techniques. In this context, investigating blind Sparsity Order Estimation (SOE) techniques is an open research issue. To address this, we study an eigenvalue-based compressive SOE technique using asymptotic Random Matrix Theory. We carry out detailed theoretical analysis for the signal plus noise case to derive the asymptotic eigenvalue probability distribution function (aepdf) of the measured signal’s covariance matrix for sparse signals. Subsequently, based on the derived aepdf expressions, we propose a technique to estimate the sparsity order of the wideband spectrum with compressive measurements using the maximum eigenvalue of the measured signal’s covariance matrix. The performance of the proposed technique is evaluated in terms of normalized SOE Error (SOEE). It is shown that the sparsity order of the wideband spectrum can be reliably estimated using the proposed technique for a variety of scenarios. [less ▲]

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See detailMulti-portfolio optimization: A potential game approach
Yang, Yang UL; Rubio, Francisco; Scutari, Gesualdo et al

in IEEE Transactions on Signal Processing (2013)

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See detailReceive combining vs. multi-stream multiplexing in downlink systems with multi-antenna users
Bjornson, Emil; Bengtsson, M.; Ottersten, Björn UL

in IEEE Transactions on Signal Processing (2013), 13(61), 3431-3446

In downlink multi-antenna systems with many users, the multiplexing gain is strictly limited by the number of transmit antennas N and the use of these antennas. Assuming that the total number of receive ... [more ▼]

In downlink multi-antenna systems with many users, the multiplexing gain is strictly limited by the number of transmit antennas N and the use of these antennas. Assuming that the total number of receive antennas at the multi-antenna users is much larger than N, the maximal multiplexing gain can be achieved with many different transmission/reception strategies. For example, the excess number of receive antennas can be utilized to schedule users with effective channels that are near-orthogonal, for multi-stream multiplexing to users with well-conditioned channels, and/or to enable interference-aware receive combining. In this paper, we try to answer the question if the N data streams should be divided among few users (many streams per user) or many users (few streams per user, enabling receive combining). Analytic results are derived to show how user selection, spatial correlation, heterogeneous user conditions, and imperfect channel acquisition (quantization or estimation errors) affect the performance when sending the maximal number of streams or one stream per scheduled user-the two extremes in data stream allocation. While contradicting observations on this topic have been reported in prior works, we show that selecting many users and allocating one stream per user (i.e., exploiting receive combining) is the best candidate under realistic conditions. This is explained by the provably stronger resilience towards spatial correlation and the larger benefit from multi-user diversity. This fundamental result has positive implications for the design of downlink systems as it reduces the hardware requirements at the user devices and simplifies the throughput optimization. [less ▲]

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See detailSparse conjoint analysis through maximum likelihood estimation
Tsakonas, Efthymios; Jalden, J.; Sidiropoulos, N.D et al

in IEEE Transactions on Signal Processing (2013), 22

Conjoint analysis (CA) is a classical tool used in preference assessment, where the objective is to estimate the utility function of an individual, or a group of individuals, based on expressed preference ... [more ▼]

Conjoint analysis (CA) is a classical tool used in preference assessment, where the objective is to estimate the utility function of an individual, or a group of individuals, based on expressed preference data. An example is choice-based CA for consumer profiling, i.e., unveiling consumer utility functions based solely on choices between products. A statistical model for choice-based CA is investigated in this paper. Unlike recent classification-based approaches, a sparsity-aware Gaussian maximum likelihood (ML) formulation is proposed to estimate the model parameters. Drawing from related robust parsimonious modeling approaches, the model uses sparsity constraints to account for outliers and to detect the salient features that influence decisions. Contributions include conditions for statistical identifiability, derivation of the pertinent Cramér-Rao Lower Bound (CRLB), and ML consistency conditions for the proposed sparse nonlinear model. The proposed ML approach lends itself naturally to ℓ1-type convex relaxations which are well-suited for distributed implementation, based on the alternating direction method of multipliers (ADMM). A particular decomposition is advocated which bypasses the apparent need for outlier communication, thus maintaining scalability. The performance of the proposed ML approach is demonstrated by comparing against the associated CRLB and prior state-of-the-art using both synthetic and real data sets. [less ▲]

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See detailImproving Physical Layer Secrecy Using Full-Duplex Jamming Receivers
Zheng, Gan UL; Krikidis, Ioannis; Jiangyuan, Li et al

in IEEE Transactions on Signal Processing (2013), 20(61), 4962-4974

This paper studies secrecy rate optimization in a wireless network with a single-antenna source, a multi-antenna destination and a multi-antenna eavesdropper. This is an unfavorable scenario for secrecy ... [more ▼]

This paper studies secrecy rate optimization in a wireless network with a single-antenna source, a multi-antenna destination and a multi-antenna eavesdropper. This is an unfavorable scenario for secrecy performance as the system is interference-limited. In the literature, assuming that the receiver operates in half duplex (HD) mode, the aforementioned problem has been addressed via use of cooperating nodes who act as jammers to confound the eavesdropper. This paper investigates an alternative solution, which assumes the availability of a full duplex (FD) receiver. In particular, while receiving data, the receiver transmits jamming noise to degrade the eavesdropper channel. The proposed self-protection scheme eliminates the need for external helpers and provides system robustness. For the case in which global channel state information is available, we aim to design the optimal jamming covariance matrix that maximizes the secrecy rate and mitigates loop interference associated with the FD operation. We consider both fixed and optimal linear receiver design at the destination, and show that the optimal jamming covariance matrix is rank-1, and can be found via an efficient 1-D search. For the case in which only statistical information on the eavesdropper channel is available, the optimal power allocation is studied in terms of ergodic and outage secrecy rates. Simulation results verify the analysis and demonstrate substantial performance gain over conventional HD operation at the destination. [less ▲]

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See detailTransceiver design for distributed STBC based AF cooperative networks in the presence of timing and frequency offsets
Nasir, A.A.; Mehrpouyan, H.; Durrani, S. et al

in IEEE Transactions on Signal Processing (2013), 12(61), 3143-3158

In multi-relay cooperative systems, the signal at the destination is affected by impairments such as multiple channel gains, multiple timing offsets (MTOs), and multiple carrier frequency offsets (MCFOs ... [more ▼]

In multi-relay cooperative systems, the signal at the destination is affected by impairments such as multiple channel gains, multiple timing offsets (MTOs), and multiple carrier frequency offsets (MCFOs). In this paper we account for all these impairments and propose a new transceiver structure at the relays and a novel receiver design at the destination in distributed space-time block code (DSTBC) based amplify-and-forward (AF) cooperative networks. The Cramér-Rao lower bounds and a least squares (LS) estimator for the multi-parameter estimation problem are derived. In order to significantly reduce the receiver complexity at the destination, a differential evolution (DE) based estimation algorithm is applied and the initialization and constraints for the convergence of the proposed DE algorithm are investigated. In order to detect the signal from multiple relays in the presence of unknown channels, MTOs, and MCFOs, novel optimal and sub-optimal minimum mean-square error receiver designs at the destination node are proposed. Simulation results show that the proposed estimation and compensation methods achieve full diversity gain in the presence of channel and synchronization impairments in multi-relay AF cooperative networks. [less ▲]

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See detailA Bayesian Monte Carlo Markov Chain Method for Parameter Estimation of Fractional Differenced Gaussian Processes
Olivares Pulido, German UL; Teferle, Felix Norman UL

in IEEE Transactions on Signal Processing (2013), 61(9), 2405-2412

We present a Bayesian Monte Carlo Markov Chain method to simultaneously estimate the spectral index and power amplitude of a fractional differenced Gaussian process at low frequency, in presence of white ... [more ▼]

We present a Bayesian Monte Carlo Markov Chain method to simultaneously estimate the spectral index and power amplitude of a fractional differenced Gaussian process at low frequency, in presence of white noise, and a linear trend and periodic signals. This method provides a sample of the likelihood function and thereby, using Monte Carlo integration, all parameters and their uncertainties are estimated simultaneously. We test this method with simulated and real Global Positioning System height time series and propose it as an alternative to optimization methods currently in use. Furthermore, without any mathematical proof, the results from the simulations suggest that this method is unaffected by the stationary regime and hence, can be used to check whether or not a time series is stationary. [less ▲]

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See detailRobust MIMO precoding for several classes of channel uncertainty
Wang, J.; Bengtsson, M.; Ottersten, Björn UL et al

in IEEE Transactions on Signal Processing (2013), 12(61), 3056-3070

The full potential of multi-input multi-output (MIMO) communication systems relies on exploiting channel state information at the transmitter (CSIT), which is, however, often subject to some uncertainty ... [more ▼]

The full potential of multi-input multi-output (MIMO) communication systems relies on exploiting channel state information at the transmitter (CSIT), which is, however, often subject to some uncertainty. In this paper, following the worst-case robust philosophy, we consider a robust MIMO precoding design with deterministic imperfect CSIT, formulated as a maximin problem, to maximize the worst-case received signal-to-noise ratio or minimize the worst-case error probability. Given different types of imperfect CSIT in practice, a unified framework is lacking in the literature to tackle various channel uncertainty. In this paper, we address this open problem by considering several classes of uncertainty sets that include most deterministic imperfect CSIT as special cases. We show that, for general convex uncertainty sets, the robust precoder, as the solution to the maximin problem, can be efficiently computed by solving a single convex optimization problem. Furthermore, when it comes to unitarily-invariant convex uncertainty sets, we prove the optimality of a channel-diagonalizing structure and simplify the complex-matrix problem to a real-vector power allocation problem, which is then analytically solved in a waterfilling manner. Finally, for uncertainty sets defined by a generic matrix norm, called the Schatten norm, we provide a fully closed-form solution to the robust precoding design, based on which the robustness of beamforming and uniform-power transmission is investigated. [less ▲]

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See detailCooperative cognitive networks: Optimal, distributed and low-complexity algorithms
Zheng, Gan UL; Song, Shenghui; Wong, Kai-Kit et al

in IEEE Transactions on Signal Processing (2013), 11(61), 2778-2790

This paper considers the cooperation between a cognitive system and a primary system where multiple cognitive base stations (CBSs) relay the primary user's (PU) signals in exchange for more opportunity to ... [more ▼]

This paper considers the cooperation between a cognitive system and a primary system where multiple cognitive base stations (CBSs) relay the primary user's (PU) signals in exchange for more opportunity to transmit their own signals. The CBSs use amplify-and-forward (AF) relaying and coordinated beamforming to relay the primary signals and transmit their own signals. The objective is to minimize the overall transmit power of the CBSs given the rate requirements of the PU and the cognitive users (CUs). We show that the relaying matrices have unity rank and perform two functions: Matched filter receive beamforming and transmit beamforming. We then develop two efficient algorithms to find the optimal solution. The first one has a linear convergence rate and is suitable for distributed implementation, while the second one enjoys superlinear convergence but requires centralized processing. Further, we derive the beamforming vectors for the linear conventional zero-forcing (CZF) and prior zero-forcing (PZF) schemes, which provide much simpler solutions. Simulation results demonstrate the improvement in terms of outage performance due to the cooperation between the primary and cognitive systems. [less ▲]

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See detailIterative Precoder Design and User Scheduling for Block-Diagonalized Systems
Tran, Le-Nam; Bengtsson, Mats; Ottersten, Björn UL

in IEEE Transactions on Signal Processing (2012), 60(7), 3726-3739

The block diagonalization (BD) scheme is a low-complexity suboptimal precoding technique for multiuser multiple input-multiple output (MIMO) downlink channels, which completely precancels the multiuser ... [more ▼]

The block diagonalization (BD) scheme is a low-complexity suboptimal precoding technique for multiuser multiple input-multiple output (MIMO) downlink channels, which completely precancels the multiuser interference. Accordingly, the precoder of each user lies in the null space of other users' channel matrices. In this paper, we propose an iterative algorithm using QR decompositions (QRDs) to compute the precoders. Specifically, to avoid dealing with a large concatenated matrix, we apply the QRD to a sequence of matrices of lower dimensions. One problem of BD schemes is that the number of users that can be simultaneously supported is limited due to zero interference constraints. When the number of users is large, a set of users must be selected, and selection algorithms should be designed to exploit the multiuser diversity gain. Finding the optimal set of users requires an exhaustive search, which has too high computational complexity to be practically useful. Based on the iterative precoder design, this paper proposes a low-complexity user selection algorithm using a greedy method, in which the precoders of selected users are recursively updated after each selection step. The selection metric of the proposed scheduling algorithm relies on the product of the squared row norms of the effective channel matrices, which is related to the eigenvalues by the Hadamard and Schur inequalities. An asymptotic analysis is provided to show that the proposed algorithm can achieve the optimal sum rate scaling of the MIMO broadcast channel. The numerical results show that the proposed algorithm achieves a good trade-off between sum rate performance and computational complexity. When users suffer different channel conditions, providing fairness among users is of critical importance. To address this problem, we also propose two fair scheduling (FS) algorithms, one imposing fairness in the approximation of the data rate, and another directly imposing fairness in the product of the sq- ared row norms of the effective channel matrices. [less ▲]

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See detailPareto Characterization of the Multicell MIMO Performance Region With Simple Receivers
Björnson, Emil; Bengtsson, Mats; Ottersten, Björn UL

in IEEE Transactions on Signal Processing (2012), 60(8), 4464-4469

We study the performance region of a general multicell downlink scenario with multiantenna transmitters, hardware impairments, and low-complexity receivers that treat interference as noise. The Pareto ... [more ▼]

We study the performance region of a general multicell downlink scenario with multiantenna transmitters, hardware impairments, and low-complexity receivers that treat interference as noise. The Pareto boundary of this region describes all efficient resource allocations, but is generally hard to compute. We propose a novel explicit characterization that gives Pareto optimal transmit strategies using a set of positive parameters-fewer than in prior work. We also propose an implicit characterization that requires even fewer parameters and guarantees to find the Pareto boundary for every choice of parameters, but at the expense of solving quasi-convex optimization problems. The merits of the two characterizations are illustrated for interference channels and ideal network multiple-input multiple-output (MIMO). [less ▲]

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See detailBayesian equalization for LDPC channel decoding
Salamanca Mino, Luis UL; Murillo-Fuentes, Juan José; Pérez-Cruz, Fernando

in IEEE Transactions on Signal Processing (2012), 60(5), 2672-2676

We describe the channel equalization problem, and its prior estimate of the channel state information (CSI), as a joint Bayesian estimation problem to improve each symbol posterior estimates at the input ... [more ▼]

We describe the channel equalization problem, and its prior estimate of the channel state information (CSI), as a joint Bayesian estimation problem to improve each symbol posterior estimates at the input of the channel decoder. Our approach takes into consideration not only the uncertainty due to the noise in the channel, but also the uncertainty in the CSI estimate. However, this solution cannot be computed in linear time, because it depends on all the transmitted symbols. Hence, we also put forward an approximation for each symbol's posterior, using the expectation propagation algorithm, which is optimal from the Kullback-Leibler divergence viewpoint and yields an equalization with a complexity identical to the BCJR algorithm. We also use a graphical model representation of the full posterior, in which the proposed approximation can be readily understood. The proposed posterior estimates are more accurate than those computed using the ML estimate for the CSI. In order to illustrate this point, we measure the error rate at the output of a low-density parity-check decoder, which needs the exact posterior for each symbol to detect the incoming word and it is sensitive to a mismatch in those posterior estimates. For example, for QPSK modulation and a channel with three taps, we can expect gains over 0.5 dB with same computational complexity as the ML receiver. [less ▲]

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See detailRobust Monotonic Optimization Framework for Multicell MISO Systems
Björnson, Emil; Zheng, Gan UL; Bengtsson, Mats et al

in IEEE Transactions on Signal Processing (2012), 60(5), 2508-2523

The performance of multiuser systems is both difficult to measure fairly and to optimize. Most resource allocation problems are nonconvex and NP-hard, even under simplifying assumptions such as perfect ... [more ▼]

The performance of multiuser systems is both difficult to measure fairly and to optimize. Most resource allocation problems are nonconvex and NP-hard, even under simplifying assumptions such as perfect channel knowledge, homogeneous channel properties among users, and simple power constraints. We establish a general optimization framework that systematically solves these problems to global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles general multicell downlink systems with single-antenna users, multiantenna transmitters, arbitrary quadratic power constraints, and robust- ness to channel uncertainty. A robust fairness-profile optimization (RFO) problem is solved at each iteration, which is a quasiconvex problem and a novel generalization of max-min fairness. The BRB algorithm is computationally costly, but it shows better convergence than the previously proposed outer polyblock approximation algorithm. Our framework is suitable for computing benchmarks in general multicell systems with or without channel uncertainty. We illustrate this by deriving and evaluating a zero-forcing solution to the general problem. [less ▲]

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See detailOptimality Properties,Distributed Strategies, and Measurement-Based Evaluation of Coordinated Multicell OFDMA Transmission
Björnson, Emil; Jaldén, Niklas; Bengtsson, Mats et al

in IEEE Transactions on Signal Processing (2011), 59(12), 6086-6101

The throughput of multicell systems is inherently limited by interference and the available communication resources. Coordinated resource allocation is the key to efficient performance, but the demand on ... [more ▼]

The throughput of multicell systems is inherently limited by interference and the available communication resources. Coordinated resource allocation is the key to efficient performance, but the demand on backhaul signaling and computational resources grows rapidly with number of cells, terminals, and subcarriers. To handle this, we propose a novel multicell framework with dynamic cooperation clusters where each terminal is jointly served by a small set of base stations. Each base station coordinates interference to neighboring terminals only, thus limiting backhaul signalling and making the framework scalable. This framework can describe anything from interference channels to ideal joint multicell transmission. The resource allocation (i.e., precoding and scheduling) is formulated as an optimization problem (P1) with performance described by arbitrary monotonic functions of the signal-to-interference-and-noise ratios (SINRs) and arbitrary linear power constraints. Although (P1) is nonconvex and difficult to solve optimally, we are able to prove: 1) optimality of single-stream beamforming; 2) conditions for full power usage; and 3) a precoding parametrization based on a few parameters between zero and one. These optimality properties are used to propose low-complexity strategies: both a centralized scheme and a distributed version that only requires local channel knowledge and processing. We evaluate the performance on measured multicell channels and observe that the proposed strategies achieve close-to-optimal performance among centralized and distributed solutions, respectively. In addition, we show that multicell interference coordination can give substantial improvements in sum performance, but that joint transmission is very sensitive to synchronization errors and that some terminals can experience performance degradations. [less ▲]

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See detailSemidefinite Relaxations of Robust Binary Least Squares under Ellipsoidal Uncertainty Sets
Tsakonas, Efthymios; Jaldén, Joakim; Ottersten, Björn UL

in IEEE Transactions on Signal Processing (2011), 59(11), 5169-5180

The problem of finding the least squares solution s to a system of equations Hs = y is considered, when s is a vector of binary variables and the coefficient matrix H is unknown but of bounded uncertainty ... [more ▼]

The problem of finding the least squares solution s to a system of equations Hs = y is considered, when s is a vector of binary variables and the coefficient matrix H is unknown but of bounded uncertainty. Similar to previous approaches to robust binary least squares, we explore the potential of a min-max design with the aim to provide solutions that are less sensitive to the uncertainty in H. We concentrate on the important case of ellipsoidal uncertainty, i.e., the matrix H is assumed to be a deterministic unknown quantity which lies in a given uncertainty ellipsoid. The resulting problem is NP-hard, yet amenable to convex approximation techniques: Starting from a convenient reformulation of the original problem, we propose an approximation algorithm based on semidefinite relaxation that explicitly accounts for the ellipsoidal uncertainty in the coefficient matrix. Next, we show that it is possible to construct a tighter relaxation by suitably changing the description of the feasible region of the problem, and formulate an approximation algorithm that performs better in practice. Interestingly, both relaxations are derived as Lagrange bidual problems corresponding to the two equivalent problem reformulations. The strength of the proposed tightened relaxation is demonstrated by pertinent simulations. [less ▲]

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See detailExploiting Long-Term Channel Correlation in Limited Feddback SDMA through Channel Phase Codebook
Huang, Yongming; Yang, Luxi; Bengtsson, Mats et al

in IEEE Transactions on Signal Processing (2011), 59(3), 1217-1228

Improving channel information quality at the base station (BS) is crucial for the optimization of frequency division duplexed (FDD) multi-antenna multiuser downlink systems with limited feedback. To this ... [more ▼]

Improving channel information quality at the base station (BS) is crucial for the optimization of frequency division duplexed (FDD) multi-antenna multiuser downlink systems with limited feedback. To this end, this paper proposes to estimate a particular representation of channel state information (CSI) at the BS through channel norm feedback and a newly developed channel phase codebook, where the long-term channel correlation is efficiently exploited to improve performance. In particular, the channel representation is decomposed into a gain-related part and a phase-related part, with each of them estimated separately. More specifically, the gain-related part is estimated from the channel norm and channel correlation matrix, while the phase-related part is determined using a channel phase codebook, constructed with the generalized Lloyd algorithm. Using the estimated channel representation, joint optimization of multiuser precoding and opportunistic scheduling is performed to obtain an SDMA transmit scheme. Computer simulation results confirm the advantage of the proposed scheme over state of the art limited feedback SDMA schemes under correlated channel environment. [less ▲]

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See detailDistributed Multicell Beamforming with Limited Intercell Coordination
Huang, Yongming; Zheng, Gan UL; Bengtsson, Mats et al

in IEEE Transactions on Signal Processing (2011), 59(2), 728-738

This paper studies distributed optimization schemes for multicell joint beamforming and power allocation in time-division-duplex (TDD) multicell downlink systems where only limited-capacity intercell ... [more ▼]

This paper studies distributed optimization schemes for multicell joint beamforming and power allocation in time-division-duplex (TDD) multicell downlink systems where only limited-capacity intercell information exchange is permitted. With an aim to maximize the worst-user signal-to-interference-and-noise ratio (SINR), we devise a hierarchical iterative algorithm to optimize downlink beamforming and intercell power allocation jointly in a distributed manner. The proposed scheme is proved to converge to the global optimum. For fast convergence and to reduce the burden of intercell parameter exchange, we further propose to exploit previous iterations adaptively. Results illustrate that the proposed scheme can achieve near-optimal performance even with a few iterations, hence providing a good tradeoff between performance and backhaul consumption. The performance under quantized parameter exchange is also examined. [less ▲]

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See detailStatistical Precoding with Decision Feedback Equalization over a Correlated MIMO Channel
Järmyr, Simon; Ottersten, Björn UL; Eduard, Jorswieck

in IEEE Transactions on Signal Processing (2010), 58(12), 6298-6311

The decision feedback (DF) transceiver, combining linear precoding and DF equalization, can establish point-to-point communication over a wireless multiple-input multiple-output channel. Matching the DF ... [more ▼]

The decision feedback (DF) transceiver, combining linear precoding and DF equalization, can establish point-to-point communication over a wireless multiple-input multiple-output channel. Matching the DF-transceiver design parameters to the channel characteristics can improve system performance, but requires channel knowledge. We consider the fast-fading channel scenario, with a receiver capable of tracking the channel-state variations accurately, while the transmitter only has long-term, channel-distribution information. The receiver design problem given channel-state information is well studied in the literature. We focus on transmitter optimization, which amounts to designing a statistical precoder to assist the channel-tailored DF equalizer. We develop a design framework that encompasses a wide range of performance metrics. Common cost functions for precoder optimization are analyzed, thereby identifying a structure of typical cost functions. Transmitter design is approached for typical cost functions in general, and we derive a precoder design formulation as a convex optimization problem. Two important subclasses of cost functions are considered in more detail. First, we explore a symmetry of DF transceivers with a uniform subchannel rate allocation, and derive a simplified convex optimization problem, which can be efficiently solved even as system dimensions grow. Second, we explore the tractability of a certain class of mean square error based cost functions, and solve the transmitter design problem with a simple algorithm that identifies the convex hull of a set of points in R2. The behavior of DF transceivers with optimal precoders is investigated by numerical means. [less ▲]

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See detailCooperative Multicell Precoding : Rate Region Characterization and Distributed Strategies with Instantaneous and Statistical CSI
Björnson, Emil; Randa, Zakhour; David, Gesbert et al

in IEEE Transactions on Signal Processing (2010), 58(8), 4298-4310

Base station cooperation is an attractive way of increasing the spectral efficiency in multiantenna communication. By serving each terminal through several base stations in a given area, intercell ... [more ▼]

Base station cooperation is an attractive way of increasing the spectral efficiency in multiantenna communication. By serving each terminal through several base stations in a given area, intercell interference can be coordinated and higher performance achieved, especially for terminals at cell edges. Most previous work in the area has assumed that base stations have common knowledge of both data dedicated to all terminals and full or partial channel state information (CSI) of all links. Herein, we analyze the case of distributed cooperation where each base station has only local CSI, either instantaneous or statistical. In the case of instantaneous CSI, the beamforming vectors that can attain the outer boundary of the achievable rate region are characterized for an arbitrary number of multiantenna transmitters and single-antenna receivers. This characterization only requires local CSI and justifies distributed precoding design based on a novel virtual signal-to-interference noise ratio (SINR) framework, which can handle an arbitrary SNR and achieves the optimal multiplexing gain. The local power allocation between terminals is solved heuristically. Conceptually, analogous results for the achievable rate region characterization and precoding design are derived in the case of local statistical CSI. The benefits of distributed cooperative transmission are illustrated numerically, and it is shown that most of the performance with centralized cooperation can be obtained using only local CSI. [less ▲]

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