David Gesbert

A Dynamic Clustering Approach in Wireless Networks with Multi-Cell Cooperative Processing

Multi-cell cooperative processing (MCP) has recently attracted a lot of attention because of its potential for co-channel interference (CCI) mitigation and spectral efficiency increase. MCP inevitably requires increased signaling overhead and inter-base communication. Therefore in practice, only a limited number of base stations (BSs) can cooperate in order for the overhead to be affordable. The intrinsic problem of which BSs shall cooperate in a realistic scenario has been only partially investigated. In this contribution linear beamforming has been considered for the sum-rate maximisation of the uplink. A novel dynamic greedy algorithm for the formation of the clusters of cooperating BSs is presented for a cellular network incorporating MCP. This approach is chosen to be evaluated under a fair MS scheduling scenario (round-robin). The objective of the clustering algorithm is sum-rate maximisation of the already selected MSs. The proposed cooperation scheme is compared with some fixed cooperation clustering schemes. It is shown that a dynamic clustering approach with a cluster consisting of 2 cells outperforms static coordination schemes with much larger cluster sizes.

Binary Power Control for Sum Rate Maximization over Multiple Interfering Links

We consider allocating the transmit powers for a wireless multi-link (N-link) system, in order to maximize the total system throughput under interference and noise impairments, and short term power constraints. Employing dynamic spectral reuse, we allow for centralized control. In the two-link case, the optimal power allocation then has a remarkably simple nature termed binary power control: depending on the noise and channel gains, assign full power to one link and minimum to the other, or full power on both. Binary power control (BPC) has the advantage of leading towards simpler or even distributed power control algorithms. For N>2 we propose a strategy based on checking the corners of the domain resulting from the power constraints to perform BPC. We identify scenarios in which binary power allocation can be proven optimal also for arbitrary N. Furthermore, in the general setting for N>2, simulations demonstrate that a throughput performance with negligible loss, compared to the best non-binary scheme found by geometric programming, can be obtained by BPC. Finally, to reduce the complexity of optimal binary power allocation for large networks, we provide simple algorithms achieving 99% of the capacity promised by exhaustive binary search.

Degrees of Freedom of the Network MIMO Channel With Distributed CSI

In this paper, we discuss the joint precoding with finite rate feedback in the so-called network multiple-input multiple-output (MIMO) where the TXs share the knowledge of the data symbols to be transmitted. We introduce a distributed channel state information (DCSI) model where each TX has its own local estimate of the overall multiuser MIMO channel and must make a precoding decision solely based on the available local CSI. We refer to this channel as the DCSI-MIMO channel and the precoding problem as distributed precoding. We extend to the DCSI setting the work from Jindal in 2006 for the conventional MIMO broadcast channel (BC) in which the number of degrees of freedom (DoFs) achieved by zero forcing (ZF) was derived as a function of the scaling in the logarithm of the signal-to-noise ratio of the number of quantizing bits. Particularly, we show the seemingly pessimistic result that the number of DoFs at each user is limited by the worst CSI across all users and across all TXs. This is in contrast to the conventional MIMO BC where the number of DoFs at one user is solely dependent on the quality of the estimation of his own feedback. Consequently, we provide precoding schemes improving on the achieved number of DoFs. For the two-user case, the derived novel precoder achieves a number of DoFs limited by the best CSI accuracy across the TXs instead of the worst with conventional ZF. We also advocate the use of hierarchical quantization of the CSI, for which we show that considerable gains are possible. Finally, we use the previous analysis to derive the DoFs optimal allocation of the feedback bits to the various TXs under a constraint on the size of the aggregate feedback in the network, in the case where conventional ZF is used.

Team decision for the cooperative MIMO channel with imperfect CSIT sharing

We consider the problem of joint MIMO precoding across multiple distant cooperating transmitters. The transmitters are assumed to be sharing user data and aim at serving a group of users in a distributed MIMO broadcast-like fashion. Among application scenarios, we find the so-called network MIMO setup. The novelty of our setup resides in the fact that each of the transmitters obtains imperfect and importantly, different, estimates of the same global multi-user channel. Despite not sharing the same vision over the CSIT, the transmitters seek to jointly act in a consistent manner in designing the precoders. This problem in facts falls in the class of so-called Team Decision Theory problems. We present some solutions to the problem of beamforming design in this case and illustrate the benefits in practical network scenarios.

Throughput guarantees for wireless networks with opportunistic scheduling: a comparative study

In this letter we develop an expression for the approximate throughput guarantee violation probability (TGVP) for users in time-slotted networks for any scheduling algorithm with a given mean and variance of the bit-rate in a time-slot, and a given distribution for the number of time-slots allocated within a time-window. Based on this general result, we evaluate closed-form expressions for the TGVPs for four well-known scheduling algorithms. Through simulations we also show that our TGVP approximation is tight for a realistic network with moving users with correlated channels and realistic throughput guarantees.

QRP07-1: Throughput Guarantees for Wireless Networks with Opportunistic Scheduling

In this paper we analyze achievable throughput guarantees in wireless time-division multiplexing (TDM) networks. Approximations of the throughput guarantee violation probability (TGVP) for users communicating in time-slotted systems are obtained for any scheduling algorithm with a given mean and variance of the number of bits transmitted in a time- slot and a distribution for the number of time-slots allocated to a user within a time-window. We investigate the corresponding TGVPs for three scheduling algorithms, namely (i) Round Robin Scheduling, (ii) Maximum Carrier-to-Noise Ratio Scheduling, and (iii) Opportunistic Round Robin Scheduling, when the users channels are independently and identically distributed.

Network MIMO: Transmitters with no CSI can still be very useful

In this paper1 we consider the Network MIMO channel under the so-called Distributed Channel State Information at the Transmitters (D-CSIT) configuration. In this setting, the precoder is designed in a distributed manner at each Transmitter (TX) on the basis of local versions of Channel State information (CSI) of various quality. Although the use of simple Zero-Forcing (ZF) was recently shown to reach the optimal DoF for a Broadcast Channel (BC) under noisy, yet centralized, CSI at the TX (CSIT), it can turn very inefficient when faced with D-CSIT: The number of Degrees-of-Freedom (DoF) achieved is then limited by the worst CSI accuracy across TXs. To circumvent this effect, we develop a new robust transmission scheme improving the DoF. A surprising result is uncovered by which, in the regime of so-called weak CSIT, the proposed scheme is shown to be DoF-optimal and to achieve a centralized outerbound consisting in the DoF of a genie-aided centralized setting in which the CSIT versions of all TXs are available everywhere. Building upon the insight obtained in the weak CSIT regime, we develop a general D-CSIT robust scheme for the 3-user case which improves over the DoF obtained by conventional robust approaches for any arbitrary CSIT configuration.

A bias removal technique for the prediction-based blind adaptive multichannel deconvolution

The problem of identifying/equalizing a digital communication channel based on its temporally or spatially oversampled output has recently gained much attention (multichannel deconvolution). In particular, blind identification methods were proposed relying on the linear prediction of the received signals, making these methods well suited to an adaptive implementation. However, in practical situations with noise corrupted data, the estimated prediction coefficients are biased, causing serious impairment in the channel estimation. In this contribution we propose a low cost algorithm for the adaptive computation of the unbiased prediction coefficients, that does not require the the knowledge of the noise variance. The technique is based on the minimization of a constrained prediction criterion, which moreover provides an estimate of the noise level. We concentrate on the blind multichannel deconvolution context, but this bias removal technique may also be used in other kinds of linear prediction-based problems.

Robust regularized ZF in decentralized Broadcast Channel with correlated CSI noise

We consider in this work the Distributed Channel State Information (DCSI) Broadcast Channel (BC) setting, in which the various Transmitters (TXs) compute elements of the precoder based on their individual estimates of the global multiuser channel matrix. Previous works relative to the DCSI setting assume the estimation errors at different TXs to be uncorrelated, while we consider in contrast in this work that the CSI noises can be correlated. This generalization bridges the gap between the fully distributed and the centralized setting, and offers an avenue to analyze partially centralized networks. In addition, we generalize the regularized Zero Forcing (ZF) precoding by letting each TX use a different regularization coefficient. Building upon random matrix theory tools, we obtain a deterministic equivalent for the rate achieved in the large system limit from which we can optimize the regularization coefficients at different TXs. This extended precoding scheme in which each TX applies the optimal regularization coefficient is denoted as “DCSI Regularized ZF” and we show by numerical simulations that it allows to significantly reduce the negative impact of the distributed CSI configuration and is robust to the distribution of CSI quality level across all TXs.

Joint precoding over a master-slave coordination link

This paper considers the problem of transmitter (TX) cooperation with distributed channel state information (CSI), where two or more transmitters seek to jointly precode messages while communicating over a rate-limited coordination link. Specifically we address a so-called master-slave scenario where one master (M-) TX is endowed with perfect CSI while K slave (S-) TXs have zero prior CSI information. We are interested in possible strategies for how the M-TX may efficiently guide the S-TXs over the coordination links so as to maximize the networks figure of merit. Strategies related to communicating quantized CSI or quantized precoding decisions are described and compared. Optimal and sub-optimal low complexity approaches are shown, exhibiting gains over conventional methods.