Irfan Ghauri

Deterministic annealing design and analysis of the Noisy MIMO Interference Channel

We consider the Noisy MIMO Interference Channel (IFC) with linear transmitters and receivers and full CSI. The maximization of the Weighted Sum Rate (WSR) or transceiver design for Interference Alignment (IA) lead to cost functions with many local optima. Deterministic annealing is an approach that allows to track the variation of the known solution of one version of the problem into the unknown solution of the desired version by a controlled variation of a parameter called temperature. When the temperature parameter is chosen as inverse SNR (or noise power), the transceiver design for maximum WSR is known at low SNR and can be tracked to any desired SNR, yielding an elegant technique to find the global optimum. The solution includes filter design for the progressive switching on of streams as the SNR increases. For IA on the other hand, IA feasibility is unchanged when the MIMO crosslink channel matrices have a reduced rank equal to the maximum of the number of streams passing through them in forward and dual IFC (this would correspond to LOS channels in the case of single streams). The rank reduction simplifies IA design and feasibility analysis, and allows in particular a counting of the number of IA solutions. By choosing now the temperature parameter to be a scale factor for the remaining channel singular values, the solution for reduced rank channels can be evolved into that for arbitrary channels.

On the noisy MIMO interference channel with CSI through analog feedback

In this paper we propose a transmission protocol through which base stations (BSs) and user equipments (UEs) acquire the necessary channel state information (CSI) for Interference Alignment (IA) transmit (Tx) and receive (Rx) filters design. We focus our attention on the frequency-flat noisy MIMO interference channel (IFC) without symbol extension and with initial assumption of no CSI neither at the BS nor at the UE. Each device acquires the necessary CSI through channel training and analog feedback. We consider optimizing the sum rate by focusing in particular on the resulting degrees of freedom (DoF). This approach allows us to easily optimize any set of parameters to unveil the trade-off between the cost and the gains associated to CSI acquisition overhead.

Weighted sum rate maximization in the underlay cognitive MISO Interference Channel

In this paper we address the problem of Weighted Sum Rate (WSR) maximization for a K-user Multiple-Input Single-Output (MISO) cognitive Interference Channel (IFC) with linear transmit beamforming (BF) vectors in an underlay cognitive radio setting. We consider a set of L single-antenna Primary receivers to which the cognitive system can causes a limited amount of interference. We thus propose an iterative algorithm to determine the BF vectors for the secondary transmission. The optimization of the Lagrange multipliers involved in the optimization problem is based on the subgradient method. The expression of the BF vector can be interpreted as dual Uplink (UL) MMSE receiver that takes into account the interference caused by a fictitious link between the primary user and secondary base station. Finally Deterministic Annealing is applied to make the convergence of the algorithm easier.

Spatial interweave for a MIMO secondary interference channel with multiple primary users

In this paper we consider a Secondary Network, modeled as a K-user Multiple-Input-Multiple-Output (MIMO) interference channel (IFC) that coexists with a set of L multi antenna Primary receivers (PRx). The objective of our investigation is to design Interference Alignment (IA) beam-forming matrices at the secondary transmitters such that the interference received at the PRx is confined in a subspace of proper dimension. To solve this optimization problem we propose an iterative algorithm that is based on the algorithm described in [1]. The cognitive radio (CR) communication under investigation can take place exploiting the Spatial Domain of the complete network. For this reason it comes under the purview of Spatial Interweave CR paradigm. In addition we propose a set of feasibility conditions that the CR system should attain in order to admit an IA solution that satisfies the interference constraints on the primary users.

Software implementation of spatial interweave cognitive radio communication using OpenAirInterface platform

We present in this paper a software implementation of a cognitive radio (CR) communication based on long term evolution-time division duplex (LTE-TDD) channel reciprocity. We study the problem of calibration and beamforming design for a CR system in which a multiple-input single-output (MISO) secondary link, wants to opportunistically communicate without harming the primary SISO system. Specifically, we evaluate the CR communication through the EURECOMs experimental OpenAirInterface (OAI) software platform. We will show that it is feasible to restore the reciprocity after calibration in a non reciprocal channel, and provide an overview of challenges in the channel estimation for real conditions.

Reciprocity calibration techniques, implementation on the OpenAirInterface platform

The channel reciprocity is an advantageous hypothesis in cognitive radio (CR) technologies, however it is not practically exploitable due to miscellaneous discrepancies including the radio frequency (RF) impairments. Consequently, previous studies proposed calibration solutions to solve these RF impairments. This paper is a step ahead in the implementation of calibration algorithms through the EURE-COMs experimental OpenAirInterface (OAI) platform. It proposes to implement and evaluate a less computationally intensive multiple-input multiple-output (MIMO) calibration algorithm, according to a realistic scenario based on long term evolution-time division duplex (LTE-TDD) specification. The result from OAI simulator shows that it is feasible to restore the reciprocity after calibration in a non reciprocal channel, and provides an overview of the challenges in the channel estimation in a real time case.

The noisy MIMO interference channel with distributed CSI acquisition and filter computation

We study the frequency-flat noisy MIMO interference channel (IFC) without symbol extension and with initial assumption of no channel state information (CSI) neither at the base stations (BS) nor at the user equipments (UE). In the noisy IFC, interference is treated as noise and hence linear transmit (Tx) and receive (Rx) filtering is considered. A transmission strategy is proposed through which the BS and the UE get the necessary CSI for CSI based transmit and receive processing, by channel training and analog channel feedback, leading to transmission overhead. Both FDD and TDD scenarios are considered (the details for which are not too different in this distributed approach). In the limited overhead approach considered, discrepancies arise between the local estimates of the global CSI established at the various Tx/Rx units, on the basis of which the local Tx/Rx filters are computed. These filters are computed by optimizing a lower bound (and close approximation) of the weighted sum rate, accounting for partial CSI. The approach allows for simple approximate sum rate expressions that can easily be optimized for any set of system parameters to unveil the trade-off between the cost and the gains associated to CSI acquisition overhead. A centralized approach is briefly discussed also.