arXiv subjects

Michael Joham

Publications and source records attributed to Michael Joham.

50 records · Page 3Linked to original sources

arXiv2021

Distributed Joint Multi-cell Optimization of IRS Parameters with Linear Precoders

We present distributed methods for jointly optimizing Intelligent Reflecting Surface (IRS) phase-shifts and beamformers in a cellular network. The proposed schemes require knowledge of only the intra-cell training sequences and corresponding received signals without explicit channel estimation. Instead, an SINR objective is estimated via sample means and maximized directly. This automatically includes and mitigates both intra- and inter-cell interference provided that the uplink training is synchronized across cells. Different schemes are considered that limit the set of known training sequences from interferers. With MIMO links an iterative synchronous bi-directional training scheme jointly optimizes the IRS parameters with the beamformers and combiners. Simulation results show that the proposed distributed methods show a modest performance degradation compared to centralized channel estimation schemes, which estimate and exchange all cross-channels between cells, and perform significantly better than channel estimation schemes which ignore the inter-cell interference.

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arXiv2021

Learning the CSI Recovery in FDD Systems

We propose an innovative machine learning-based technique to address the problem of channel acquisition at the base station in frequency division duplex systems. In this context, the base station reconstructs the full channel state information in the downlink frequency range based on limited downlink channel state information feedback from the mobile terminal. The channel state information recovery is based on a convolutional neural network which is trained exclusively on collected channel state samples acquired in the uplink frequency domain. No acquisition of training samples in the downlink frequency range is required at all. Finally, after a detailed presentation and analysis of the proposed technique and its performance, the "transfer learning'' assumption of the convolutional neural network that is central to the proposed approach is validated with an analysis based on the maximum mean discrepancy metric.

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arXiv2018

FDD Channel Estimation via Covariance Identification in Wideband Massive MIMO Systems

A method for channel estimation in wideband massive Multiple-Input Multiple-Output (MIMO) systems using covariance identification is developed. The method is useful for Frequency-Division Duplex (FDD) at either sub-6GHz or millimeter wave (mmWave) frequency bands and takes into account the beam squint effect caused by the large bandwidth of the signals. The method relies on the slow time variation of the channel covariance matrix and allows for the utilization of very short training sequences thanks to the exploitation of the channel structure, both in the covariance matrix identification and the channel estimation stages. As a consequence of significantly reducing the training overhead, the proposed channel estimator has a computational complexity lower than other existing approaches.

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arXiv2018

User Demand Based Precoding for G.fast DSL Systems

It can be observed that the achievable rate region of a G.fast DSL system is no longer rectangular, as it is the case for vectored VDSL systems, due to stronger crosstalk couplings at high frequencies. Therefore, alternative operating points that are not optimal in a sum-rate sense may be utilized to adapt the system performance to the users' actual demands. To this end, we propose a new precoding scheme based on defining a subset of prioritized users, where we optimize the sum-rate of the prioritized users under a minimum rate guarantee for the remaining users. We present a solution based on Lagrangian duality theory and propose a well-performing one-step heuristic solution. By means of simulations, we show that significant rate gains for the prioritized users can be obtained by the proposed precoding scheme.

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arXiv2018

A Bilinear Equalizer for Massive MIMO Systems

We present a novel approach for low-complexity equalizer design well-suited for cellular massive MIMO systems. Our design allows to exploit the channel structure in terms of covariance matrices to improve the performance in the face of pilot-contamination, while basically keeping the complexity of a matched filter. This is achieved by restricting the equalizer to functions which are bilinear in the received data signals and the observations from a training phase. The proposed design generalizes several previous approaches to equalizer design for massive MIMO. We show by asymptotic analysis that with the proposed design the achievable rate grows without bound for growing numbers of antennas even in the presence of pilot-contamination. We demonstrate with numerical results that the proposed design is competitive with more complex approaches in a practical cellular setup.

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arXiv2018

Covariance Matrix Estimation in Massive MIMO

Interference during the uplink training phase significantly deteriorates the performance of a massive MIMO system. The impact of the interference can be reduced by exploiting second order statistics of the channel vectors, e.g., to obtain minimum mean squared error estimates of the channel. In practice, the channel covariance matrices have to be estimated. The estimation of the covariance matrices is also impeded by the interference during the training phase. However, the coherence interval of the covariance matrices is larger than that of the channel vectors. This allows us to derive methods for accurate covariance matrix estimation by appropriate assignment of pilot sequences to users in consecutive channel coherence intervals.

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arXiv2017

Hybrid LISA Precoding for Multiuser Millimeter-Wave Communications

Millimeter-wave (mmWave) communications plays an important role for future cellular networks because of the vast amount of spectrum available in the underutilized mmWave frequency bands. To overcome the huge free space omnidirectional path loss in those frequency bands, the deployment of a very large number of antenna elements at the base station is crucial. The complexity, power consumption and costs resulting from the large number of antenna elements can be reduced by limiting the number of RF chains. This leads to hybrid precoding and combining, which, in contrast to the traditional fully digital precoding and combining, moves a part of the signal processing from the digital to the analog domain. This paper proposes new algorithms for the design of hybrid precoders and combiners in a multiuser scenario. The algorithms are based on the previously proposed Linear Successive Allocation method developed for the traditional fully digital version. It successively allocates data streams to users and suppresses the respective interstream interference in two stages, which perfectly matches the hybrid architecture. Furthermore, a low-complexity version is developed by exploiting the typical structure of mmWave channels. The good performance of the proposed method and its low-complexity version is demonstrated by simulation results.

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arXiv2016

QoS Constrained Power Minimization in the Multiple Stream MIMO Broadcast Channel

This work addresses the design of optimal linear transmit filters for the Multiple Input-Multiple Output (MIMO) Broadcast Channel (BC) when several spatial streams are allocated to each user.We also consider that the Channel State Information (CSI) is perfect at the receivers but is only partial at the transmitter. A statistical model for the partial CSI is assumed and exploited for the filter design. Similarly to the single-stream per user case, the problem is solved via Mean Square Error (MSE) dualities and interference functions. However, including more streams per user involves an additional complexity level since we must determine how to distribute the per-user rates among the streams. Such problem is solved using a projected gradient algorithm.

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arXiv2015

QoS Constrained Power Minimization in the MISO Broadcast Channel with Imperfect CSI

We consider the design of linear precoders and receivers in a Multiple-Input Single-Output (MISO) Broadcast Channel (BC). We aim at minimizing the transmit power while fullfiling a set of per-user Quality-of-Service (QoS) constraints expressed in terms of per-user average rate requirements. The Channel State Information (CSI) is assumed to be perfectly known at the receivers but only partially at the transmitter. To solve the problem we transform the QoS constraints into Minimum Mean Square Error (MMSE) constraints. We then leverage the MSE duality between the BC and the Multiple Access Channel (MAC), as well as standard interference functions in the dual MAC, to perform power minimization by means of an Alternating Optimization (AO) algorithm. Problem feasibility is also studied to determine whether the QoS constraints can be fulfilled or not. Finally, we present an algorithm to balance the average rates and manage situations that may be unfeasible or lead to an unacceptably high transmit power.

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arXiv2015

Channel Estimation in Massive MIMO Systems

We introduce novel blind and semi-blind channel estimation methods for cellular time-division duplexing systems with a large number of antennas at each base station. The methods are based on the maximum a-posteriori principle given a prior for the distribution of the channel vectors and the received signals from the uplink training and data phases. Contrary to the state-of-the-art massive MIMO channel estimators which either perform linear estimation based on the pilot symbols or rely on a blind principle, the proposed semi-blind method efficiently suppresses most of the interference caused by pilot-contamination. The simulative analysis illustrates that the semi-blind estimator outperforms state- of-the-art linear and non-linear approaches to the massive MIMO channel estimation problem.

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arXiv2012

A Framework for Joint Design of Pilot Sequence and Linear Precoder

Most performance measures of pilot-assisted multiple-input multiple-output (MIMO) systems are functions that depend on both the linear precoding filter and the pilot sequence. A framework for the optimization of these two parameters is proposed, based on a matrix-valued generalization of the concept of effective signal-to-noise ratio (SNR) introduced in a famous work by Hassibi and Hochwald. The framework applies to a wide class of utility functions of said effective SNR matrix, most notably a well-known mutual information expression for Gaussian inputs, an upper bound on the minimum mean-square error (MMSE), as well as approximations thereof. The approach consists in decomposing the joint optimization problem into three subproblems: first, we describe how to reformulate the optimization of the linear precoder subject to a fixed pilot sequence as a convex problem. Second, we do likewise for the optimization of the pilot sequence subject to a fixed precoder. Third, we describe how to generate pairs of precoders and pilot sequences that are Pareto optimal in the sense that they attain the Pareto boundary of the set of feasible effective SNR matrices. By combining these three optimization problems into an iteration, we obtain an algorithm which allows to compute jointly optimal pairs of precoders and pilot sequences with respect to some generic utility function of the effective SNR.

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arXiv2008

On the Convexity of the MSE Region of Single-Antenna Users

We prove convexity of the sum-power constrained mean square error (MSE) region in case of two single-antenna users communicating with a multi-antenna base station. Due to the MSE duality this holds both for the vector broadcast channel and the dual multiple access channel. Increasing the number of users to more than two, we show by means of a simple counter-example that the resulting MSE region is not necessarily convex any longer, even under the assumption of single-antenna users. In conjunction with our former observation that the two user MSE region is not necessarily convex for two multi-antenna users, this extends and corrects the hitherto existing notion of the MSE region geometry.

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arXiv2008

A General Rate Duality of the MIMO Multiple Access Channel and the MIMO Broadcast Channel

We present a general rate duality between the multiple access channel (MAC) and the broadcast channel (BC) which is applicable to systems with and without nonlinear interference cancellation. Different to the state-of-the-art rate duality with interference subtraction from Vishwanath et al., the proposed duality is filter-based instead of covariance-based and exploits the arising unitary degree of freedom to decorrelate every point-to-point link. Therefore, it allows for noncooperative stream-wise decoding which reduces complexity and latency. Moreover, the conversion from one domain to the other does not exhibit any dependencies during its computation making it accessible to a parallel implementation instead of a serial one. We additionally derive a rate duality for systems with multi-antenna terminals when linear filtering without interference (pre-)subtraction is applied and the different streams of a single user are not treated as self-interference. Both dualities are based on a framework already applied to a mean-square-error duality between the MAC and the BC. Thanks to this novel rate duality, any rate-based optimization with linear filtering in the BC can now be handled in the dual MAC where the arising expressions lead to more efficient algorithmic solutions than in the BC due to the alignment of the channel and precoder indices.

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arXiv2008

An Asymptotic Analysis of the MIMO BC under Linear Filtering

We investigate the MIMO broadcast channel in the high SNR regime when linear filtering is applied instead of dirty paper coding. Using a user-wise rate duality where the streams of every single user are not treated as self-interference as in the hitherto existing stream-wise rate dualities for linear filtering, we solve the weighted sum rate maximization problem of the broadcast channel in the dual multiple access channel. Thus, we can exactly quantify the asymptotic rate loss of linear filtering compared to dirty paper coding for any channel realization. Having converted the optimum covariance matrices to the broadcast channel by means of the duality, we observe that the optimal covariance matrices in the broadcast channel feature quite complicated but still closed form expressions although the respective transmit covariance matrices in the dual multiple access channel share a very simple structure. We immediately come to the conclusion that block-diagonalization is the asymptotically optimum transmit strategy in the broadcast channel. Out of the set of block-diagonalizing precoders, we present the one which achieves the largest sum rate and thus corresponds to the optimum solution found in the dual multiple access channel. Additionally, we quantify the ergodic rate loss of linear coding compared to dirty paper coding for Gaussian channels with correlations at the mobiles.

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