Showing papers by "Mats Bengtsson published in 2015"

Robust precoding design for multibeam downlink satellite channel with phase uncertainty

Abstract: In this work, we study the design of a precoder on the user downlink of a multibeam satellite channel. The variations in channel due to phase noise introduced by on-board oscillators and the long round trip delay result in outdated channel information at the transmitter. The phase uncertainty is modelled and a robust design framework is formulated based on availability and power constraints. The optimization problem is cast into the convex paradigm after approximations and the benefits of the resulting precoder are highlighted.

Subspace estimation and decomposition for hybrid analog-digital millimetre-wave MIMO systems

Abstract: In this work, we address the problem of channel estimation and precoding / combining for the so-called hybrid millimeter wave (mmWave) MIMO architecture. Our proposed channel estimation scheme exploits channel reciprocity in TDD MIMO systems, by using echoing, thereby allowing us to implement Krylov subspace methods in a fully distributed way. The latter results in estimating the right (resp. left) singular subspace of the channel at the transmitter (resp. receiver). Moreover, we also tackle the problem of subspace decomposition whereby the estimated right (resp. left) singular subspaces are approximated by a cascade of analog and digital precoder (resp. combiner), using an iterative method. Finally we compare our scheme with an equivalent fully digital case and conclude that a relatively similar performance can be achieved, however, with a drastically reduced number of RF chains - 4 ∼ 8 times less (i.e., massive savings in cost and power consumption).

Precoding design and user selection for multibeam satellite channels

Abstract: Precoding for the downlink of a multibeam satellite system has been recently shown, under ideal conditions, to be promising technique towards employing aggressive frequency reuse gainfully. However, time varying phase uncertainties imposed by the components and the channel, combined with delayed feedback perturbs the channel state information at the transmitter (CSIT). In this paper, we consider a power constrained robust formulation of the downlink precoding problem to counter the phase uncertainties. In particular it considers imposing conditions on the average signal to interference plus noise ratio (SINR), to deal with imperfect CSIT. In addition to the robust formulation, the primacy of user selection is highlighted and a new approach exploiting the satellite system design is proposed. Performance of the derived robust precoder in conjunction with the proposed location based user selection is then evaluated and the gains are tabulated.

Pilot clustering in asymmetric massive MIMO networks

Abstract: We consider the uplink of a cellular massive MIMO network. Since the spectral efficiency of these networks is limited by pilot contamination, the pilot allocation across cells is of paramount importance. However, finding efficient pilot reuse patterns is non-trivial especially in practical asymmetric base station deployments. In this paper, we approach this problem using coalitional game theory. Each cell has its own unique pilots and can form coalitions with other cells to gain access to more pilots. We develop a low-complexity distributed algorithm and prove convergence to an individually stable coalition structure. Simulations reveal fast algorithmic convergence and substantial performance gains over one-cell coalitions and full pilot reuse.

Fast-convergent distributed coordinated precoding for TDD multicell MIMO systems

Abstract: Several distributed coordinated precoding methods relying on over-the-air (OTA) iterations in time-division duplex (TDD) networks have recently been proposed. Each OTA iteration incurs overhead, which reduces the time available for data transmission. In this work, we therefore propose an algorithm which reaches good sum rate performance within just a few number of OTA iterations, partially due to non-overhead-incurring local iterations at the receivers. We formulate a scalarized multi-objective optimization problem where a linear combination of the weighted sum rate and the multiplexing gain is maximized. Using a well-known heuristic for smoothing the optimization problem together with a linearization step, the distributed algorithm is derived. When numerically compared to the state-of-the-art in a scenario with 1 to 3 OTA iterations allowed, the algorithm shows significant sum rate gains at high signal-to-noise ratios.

Energy Efficient Transmissions in Cognitive MIMO Systems With Multiple Data Streams

Abstract: We investigate energy-efficient communications for time-division multiple access (TDMA) multiple-input multiple-output (MIMO) cognitive radio (CR) networks operating in underlay mode. In particular, we consider the joint optimization over both the time resource and the transmit precoding matrices to minimize the overall energy consumption of a single cell secondary network with multiple secondary users (SUs), while ensuring their quality of service (QoS). The corresponding mathematical formulations turn out to be non-convex, and thus of high complexity to solve in general. We give a comprehensive treatment of this problem, considering both the cases of perfect channel state information (CSI) and statistical CSI of the channels from the SUs to the primary receiver. We tackle the non-convexity by applying a proper optimization decomposition that allows the overall problem to be efficiently solved. In particular, we show that when the SUs only have statistical CSI, the optimal solution can be found in polynomial time. Moreover, if we consider additional integer constraints on the time variable which is usually a requirement in practical wireless system, the overall problem becomes a mixed-integer non-convex optimization which is more complicated. By exploring the special structure of this particular problem, we show that the optimal integer time solution can be obtained in polynomial time with a simple greedy algorithm. When the SUs have perfect CSI, the decomposition based algorithm is guaranteed to find the optimal solution when the secondary system is under-utilized . Simulation results show that the energy-optimal transmission scheme adapts to the traffic load of the secondary system to create a win-win situation where the SUs are able to decrease the energy consumption and the PUs experience less interference from the secondary system. The effect is particularly pronounced when the secondary system is under-utilized.

Distributed precoding and user selection in MIMO interfering networks

Abstract: In this work we shed light on the problem of precoding and user selection in MIMO networks. We formulate the problem using the framework of stable matching, whereby a set of users wish to be matched to a set of serving base stations, such as to maximize the sum-rate performance of the system. Though the problem is NP-hard, we propose a suboptimal heuristic that tackles the problem in a distributed fashion: we apply a many-to-one stable matching algorithm to generate a sequence of matchings, and the Weighted MMSE algorithm to perform the precoding. We benchmark our algorithm againt the recently proposed Weighted MMSE with User Assignment algorithm [1].

Distributed Low-Overhead Schemes for Multi-Stream MIMO Interference Channels

Abstract: Our aim in this paper is to propose fully distributed schemes for transmit and receive filter optimization. The novelty of the proposed schemes is that they only require a few forward-backward iterations, thus causing minimal communication overhead. For that purpose, we relax the well-known leakage minimization problem, and then propose two different filter update structures to solve the resulting nonconvex problem: though one leads to conventional full-rank filters, the other results in rank-deficient filters, that we exploit to gradually reduce the transmit and receive filter rank, and greatly speed up the convergence. Furthermore, inspired from the decoding of turbo codes, we propose a turbo-like structure to the algorithms, where a separate inner optimization loop is run at each receiver (in addition to the main forward-backward iteration). In that sense, the introduction of this turbo-like structure converts the communication overhead required by conventional methods to computational overhead at each receiver (a cheap resource), allowing us to achieve the desired performance, under a minimal overhead constraint. Finally, we show through comprehensive simulations that both proposed schemes hugely outperform the relevant benchmarks, especially for large system dimensions.

Overhead-aware distributed CSI selection in the MIMO interference channel

Abstract: We consider a MIMO interference channel in which the transmitters and receivers operate in frequency-division duplex mode. In this setting, interference management through coordinated transceiver design necessitates channel state information at the transmitters (CSI-T). The acquisition of CSI-T is done through feedback from the receivers, which entitles a loss in degrees of freedom, due to training and feedback. This loss increases with the amount of CSI-T. In this work, after formulating an overhead model for CSI acquisition at the transmitters, we propose a distributed mechanism to find for each transmitter a subset of the complete CSI, which is used to perform interference management. The mechanism is based on many-to-many stable matching. We prove the existence of a stable matching and exploit an algorithm to reach it. Simulation results show performance improvement compared to full and minimal CSI-T.

Interference alignment-aided base station clustering using coalition formation

Abstract: Base station clustering is necessary in large interference networks, where the channel state information (CSI) acquisition overhead otherwise would be overwhelming. In this paper, we propose a novel long-term throughput model for the clustered users which addresses the balance between interference mitigation capability and CSI acquisition overhead. The model only depends on statistical CSI, thus enabling long-term clustering. Based on notions from coalitional game theory, we propose a low-complexity distributed clustering method. The algorithm converges in a couple of iterations, and only requires limited communication between base stations. Numerical simulations show the viability of the proposed approach.

Virtual Full-Duplex Buffer-Aided Relaying in the Presence of Inter-Relay Interference

Abstract: In this paper, we study virtual full-duplex (FD) buffer-aided relaying to recover the loss of multiplexing gain caused by half-duplex (HD) relaying in a multiple relay network, where each relay is equipped with a buffer and multiple antennas, through joint opportunistic relay selection (RS) and beamforming (BF) design. The main idea of virtual FD buffer-aided relaying is that the source and one of the relays simultaneously transmit their own information to another relay and the destination, respectively. In such networks, inter-relay interference (IRI) is a crucial problem which has to be resolved like self-interference in the FD relaying. In contrast to previous work that neglected IRI, we propose joint RS and BF schemes taking IRI into consideration by using multiple antennas at the relays. In order to maximize average end-to-end rate, we propose a weighted sum-rate maximization strategy assuming that adaptive rate transmission is employed in both the source to relay and relay to destination links. Then, we propose several BF schemes cancelling or suppressing IRI in order to maximize the weighted sum-rate. Numerical results show that our proposed optimal, zero forcing, and minimum mean square error BF-based RS schemes asymptotically approach the ideal FD relaying upper bound when increasing the number of antennas and/or the number of relays.

Pilot Clustering in Asymmetric Massive MIMO Networks

Abstract: We consider the uplink of a cellular massive MIMO network. Since the spectral efficiency of these networks is limited by pilot contamination, the pilot allocation across cells is of paramount importance. However, finding efficient pilot reuse patterns is non-trivial especially in practical asymmetric base station deployments. In this paper, we approach this problem using coalitional game theory. Each cell has its own unique pilots and can form coalitions with other cells to gain access to more pilots. We develop a low-complexity distributed algorithm and prove convergence to an individually stable coalition structure. Simulations reveal fast algorithmic convergence and substantial performance gains over one-cell coalitions and full pilot reuse.

Overhead-Aware Distributed CSI Selection in the MIMO Interference Channel

Abstract: We consider a MIMO interference channel in which the transmitters and receivers operate in frequency-division duplex mode. In this setting, interference management through coordinated transceiver design necessitates channel state information at the transmitters (CSI-T). The acquisition of CSI-T is done through feedback from the receivers, which entitles a loss in degrees of freedom, due to training and feedback. This loss increases with the amount of CSI-T. In this work, after formulating an overhead model for CSI acquisition at the transmitters, we propose a distributed mechanism to find for each transmitter a subset of the complete CSI, which is used to perform interference management. The mechanism is based on many-to-many stable matching. We prove the existence of a stable matching and exploit an algorithm to reach it. Simulation results show performance improvement compared to full and minimal CSI-T.

Adaptive Pilot Clustering in Heterogeneous Massive MIMO Networks

Abstract: We consider the uplink of a cellular massive MIMO network. Acquiring channel state information at the base stations (BSs) requires uplink pilot signaling. Since the number of orthogonal pilot sequences is limited by the channel coherence, pilot reuse across cells is necessary to achieve high spectral efficiency. However, finding efficient pilot reuse patterns is non-trivial especially in practical asymmetric BS deployments. We approach this problem using coalitional game theory. Each BS has a few unique pilots and can form coalitions with other BSs to gain access to more pilots. The BSs in a coalition thus benefit from serving more users in their cells, at the expense of higher pilot contamination and interference. Given that a cell's average spectral efficiency depends on the overall pilot reuse pattern, the suitable coalitional game model is in partition form. We develop a low-complexity distributed coalition formation based on individual stability. By incorporating a base station intercommunication budget constraint, we are able to control the overhead in message exchange between the base stations and ensure the algorithm's convergence to a solution of the game called individually stable coalition structure. Simulation results reveal fast algorithmic convergence and substantial performance gains over the baseline schemes with no pilot reuse, full pilot reuse, or random pilot reuse pattern.

Estimation of the clipping level in OFDM systems

Abstract: We consider scenarios such as machine-to-cellular communications, where a low-cost transmitter communicates with a high-quality receiver. Then, digital pre-distortion of the non-linear power amplifier may be too expensive. In order to investigate the feasibility of receiver- side compensation of the transmitter RF impairments, we study estimation of the clipping level in OFDM systems. Both blind and pilot based schemes are proposed and numerical evaluations show that iterative decoding can be done using the estimated clipping level without any significant performance loss.

Linear transceiver optimization in multicell MIMO based on the generalized benders decomposition

Abstract: We study the maximum sum rate optimization problem in the multiple-input multiple-output interfering broadcast channel. The multiple-antenna transmitters and receivers are assumed to have perfect channel state information. In this setting, finding the optimal linear transceiver design is an NP-hard problem. We show that a reformulation of the problem renders the application of generalized Benders decomposition suitable. The decomposition provides us with an optimization structure which we exploit to apply two different optimization approaches. While one approach is guaranteed to converge to a local optimum of the original problem, the other approach hinges on techniques which can be promising for devising a global optimization method.

Linear Transceiver Optimization in Multicell MIMO Based on the Generalized Benders Decomposition

Abstract: We study the maximum sum rate optimization problem in the multiple-input multiple-output interfering broadcast channel. The multiple-antenna transmitters and receivers are assumed to have perfect channel state information. In this setting, finding the optimal linear transceiver design is an NP-hard problem. We show that a reformulation of the problem renders the application of generalized Benders decomposition suitable. The decomposition provides us with an optimization structure which we exploit to apply two different optimization approaches. While one approach is guaranteed to converge to a local optimum of the original problem, the other approach hinges on techniques which can be promising for devising a global optimization method.

Robust Optimal Power Distribution for Hyperthermia Cancer Treatment

Abstract: We consider an optimization problem for spatial power distribution generated by an array of transmitting elements. Using ultrasound hyperthermia cancer treatment as a motivating example, the signal ...