Browsing by Subject "Hardware impairments"

Now showing 1 - 5 of 5

Open Access
Analysis of DF relay selection in massive MIMO systems with hardware ımpairments
(IEEE, 2020) Kazemi, M.; Mohammadi, A.; Duman, Tolga M.
We consider a massive multiple-input multiple-output (m-MIMO) system in which a source communicates with a destination with the help of multiple single-antenna decode-and-forward (DF) relays. Employing optimal relay selection, we analyze the system performance in presence of hardware impairments (HWI) for two m-MIMO scenarios: massive-antenna source and single-antenna destination (m-MIMO I), and massive-antenna source and destination (m-MIMO II). We obtain lower bounds on the average signal-to-noise plus distortion ratio (SNDR) of the system and show that in the m-MIMO II regime, the HWI levels at the relays become the only limiting factors. Employing extreme value theory, we demonstrate that as the number of relays increases the end-to-end SNDR of the system tends to Gumbel and Weibull distributions for the m-MIMO I and m-MIMO II systems, respectively. In addition, for both arbitrary numbers of source and destination antennas and m-MIMO scenarios, we provide closed form expressions for optimal power allocation between the source and the selected relay, and the effects of HWI level distributions between the receiving and the transmitting parts of the relay (which can be exploited for optimal system design under cost constraints).
Open Access
Design of low complexity unsourced random access schemes over wireless channels
(2023-11) Özateş, Mert
The Sixth Generation and Beyond communication systems are expected to enable communications of a massive number of machine-type devices. The traffic generated by some of these devices will significantly deviate from those in conventional communication scenarios. For instance, for applications where a massive number of cheap sensors communicate with a base station (BS), the devices will only be sporadically active and there will be no coordination among them or with the BS. For such systems requiring massive random access solutions, a new paradigm called unsourced random access (URA) has recently been proposed. In URA, all the users employ the same codebook and there is no user identity. The destination is only interested in the list of messages being sent from the set of active users. While there are many interesting URA schemes developed in the recent literature, many significant challenges remain, in particular in designing low-complexity and energy-efficient solutions. With the motivation of addressing the current challenges in URA, we develop practical solutions for several scenarios. First, we propose and study URA over frequency-selective channels via orthogonal frequency division multiplexing to mitigate the fading effects. The decoder employs a joint activity detection and channel estimation algorithm coupled with treating interference as noise and successive interference cancellation (SIC). Our results show that the pro-posed scheme offers competitive performance with grant-based frequency division multiple-access while the performance loss due to the estimated channel state information is limited. We then examine the scenario for which the receiver is equipped with a massive number of antennas and develop a simple yet energy-efficient solution by dividing the transmission frame into slots where each active user utilizes a non-orthogonal pilot sequence followed by its polar encoded codeword. At the receiver, we first detect the transmitted pilot sequences by a generalized orthogonal matching pursuit algorithm and utilize a linear minimum mean square error (LMMSE) solution to estimate the channel vectors. We then perform iterative decoding based on maximal ratio combining and single-user decoding followed by SIC. Numerical examples and analysis results demonstrate that the proposed scheme either outperforms the existing approaches in the lit-erature or has a competitive performance with lower complexity. We then adapt our solution to the scenarios with residual hardware impairments (HWIs) at the BS and the user equipment sides by developing a hardware-impairment aware LMMSE solution for channel estimation using the HWI statistics and observe that the newly proposed solution improves the energy efficiency and increases the number of supported active users. Finally, we study on-off division multiple access in the context of URA where each active user utilizes a small fraction of the transmission frame and show that the new approach is superior to the existing ones in terms of performance or complexity.
Open Access
Estimation theoretic analyses of location secrecy and ris-aided localization under hardware impairments
(2022-06) Öztürk, Cüneyd
In this thesis, we present estimation theoretic analyses of location secrecy and reconfigurable intelligent surface (RIS) aided localization under hardware impairments. First, we consider a wireless source localization network in which a target node emits localization signals that are used by anchor nodes to estimate the target node position. In addition to target and anchor nodes, there can also exist eavesdropper nodes and jammer nodes which aim to estimate the position of the target node and to degrade the accuracy of localization, respectively. We propose the problem of eavesdropper selection with the goal of optimally placing a given number of eavesdropper nodes to a subset of possible positions in the network to estimate the target node position as accurately as possible. As the performance metric, the Cramér-Rao lower bound (CRLB) related to the estimation of the target node position by eavesdropper nodes is derived, and its convexity and monotonicity properties are investigated. By relaxing the integer constraints, the eavesdropper selection problem is approximated by a convex optimization problem and algorithms are proposed for eavesdropper selection. Moreover, in the presence of parameter uncertainty, a robust version of the eavesdropper selection problem is developed. Then, the problem of jammer selection is proposed where the aim is to optimally place a given number of jammer nodes to a subset of possible positions for degrading the localization accuracy of the network as much as possible. A CRLB expression from the literature is used as the performance metric, and its concavity and monotonicity properties are derived. Also, a convex optimization problem and its robust version are derived after relaxation. Moreover, the joint eavesdropper and jammer selection problem is proposed with the goal of placing certain numbers of eavesdropper and jammer nodes to a subset of possible positions. Simulation results are presented to illustrate performance of the proposed algorithms. Second, a wireless source localization network consisting of synchronized target and anchor nodes is considered. An anchor placement problem is formulated to minimize the CRLB on estimation of target node positions by anchor nodes. It is shown that the anchor placement problem can be approximated as a minimization problem of the ratio of two supermodular functions. Due to the lack of a polynomial time algorithm for such problems, an anchor selection problem is proposed to solve the anchor placement problem. Via relaxation of integer constraints, the anchor selection problem is approximated by a convex optimization problem, which is used to propose two algorithms for anchor selection. Furthermore, extensions to quasi-synchronous wireless localization networks are discussed. To examine the performance of the proposed algorithms, various simulation results are presented. Third, we investigate the problem of RIS-aided near-field localization of a user equipment (UE) served by a base station (BS) under phase-dependent amplitude variations at each RIS element. Through a misspecified Cramér -Rao bound (MCRB) analysis and a resulting lower bound (LB) on localization, we show that when the UE is unaware of amplitude variations (i.e., assumes unit-amplitude responses), severe performance penalties can arise, especially at high signal-to-noise ratios (SNRs). Leveraging Jacobi-Anger expansion to decouple range-azimuth-elevation dimensions, we develop a low-complexity approximated mismatched maximum likelihood (AMML) estimator, which is asymptotically tight to the LB. To mitigate performance loss due to model mismatch, we propose to jointly estimate the UE location and the RIS amplitude model parameters. The corresponding Cramér -Rao bound (CRB) is derived, as well as an iterative refinement algorithm, which employs the AMML method as a subroutine and alternatingly updates individual parameters of the RIS amplitude model. Simulation results indicate fast convergence and performance close to the CRB. The proposed method can successfully recover the performance loss of the AMML under a wide range of RIS parameters and effectively calibrate the RIS amplitude model online with the help of a user that has an a-priori unknown location. Fourth, we consider RIS-aided localization scenarios with RIS pixel failures, where individual RIS elements can become faulty due to hardware imperfections. We explore the impact of such failures on the localization performance. To that aim, an MCRB analysis is conducted and numerical results indicate that performance loss for estimating the UE position can be significant in the presence of pixel failures. To remedy this issue, we develop two different diagnosis strategies to determine which pixels are failing, and design robust methods to perform localization in the presence of faulty elements. One strategy is based on the l_1-regularization method, the second one employs a successive approach. Both methods significantly reduce the performance loss due to pixel failures. The successive one performs very close to the theoretical bounds at high SNRs even though it has a higher computational cost than the l_1-regularization based method. In the final part of the dissertation, the optimal encoding strategy of a scalar parameter is performed in the presence of jamming based on an estimation theoretic criterion. Namely, the aim is to obtain the optimal encoding function at the transmitter that minimizes the expectation of the conditional Cramér -Rao bound (ECRB) at the receiver when the jammer has access to the parameter and alters the received signal by sending an encoded version of the parameter. Via calculus of variations, the optimal encoding function at the transmitter is characterized explicitly, and an algorithm is proposed to calculate it. Numerical examples demonstrate benefits of the proposed optimal encoding approach.
Open Access
On the Impact of hardware impairments on RIS-aided localization
(IEEE, 2022-05) Öztürk, Cüneyd; Keskin, M. F.; Wymeersch, H.; Gezici, Sinan
We investigate a reconfigurable intelligent surface (RIS)-aided near-field localization system with single-antenna user equipment (UE) and base station (BS) under hardware impairments by considering a practical phase-dependent RIS amplitude variations model. To analyze the localization performance under the mismatch between the practical model and the ideal model with unit-amplitude RIS elements, we employ the misspecified Cramér-Rao bound (MCRB). Based on the MCRB derivation, the lower bound (LB) on the mean-squared error for estimation of UE position is evaluated and shown to converge to the MCRB at low signal-to-noise ratios (SNRs). Simulation results indicate more severe performance degradation due to the model misspecification with increasing SNR. In addition, the mismatched maximum likelihood (MML) estimator is derived and found to be tight to the LB in the high SNR regime. Finally, we observe that the model mismatch can lead to an order-of-magnitude localization performance loss at high SNRs. © 2022 IEEE.
Open Access
RIS-aided near-field localization under phase-dependent amplitude variations
(Institute of Electrical and Electronics Engineers , 2023-08-14) Ozturk, C.; Keskin, M. F.; Wymeersch, H.; Gezici, Sinan
We investigate the problem of reconfigurable intelligent surface (RIS)-aided near-field localization of a user equipment (UE) served by a base station (BS) under phase-dependent amplitude variations at each RIS element. Through a misspecified Cramér-Rao bound (MCRB) analysis and a resulting lower bound (LB) on localization, we show that when the UE is unaware of amplitude variations (i.e., assumes unit-amplitude responses), severe performance penalties can arise, especially at high signal-to-noise ratios (SNRs). Leveraging Jacobi-Anger expansion to decouple range-azimuth-elevation dimensions, we develop a low-complexity approximated mismatched maximum likelihood (AMML) estimator, which is asymptotically tight to the LB. To mitigate performance loss due to model mismatch, we propose to jointly estimate the UE location and the RIS amplitude model parameters. The corresponding Cramér-Rao bound (CRB) is derived, as well as an iterative refinement algorithm, which employs the AMML method as a subroutine and alternatingly updates individual parameters of the RIS amplitude model. Simulation results indicate fast convergence and performance close to the CRB. The proposed method can successfully recover the performance loss of the AMML under a wide range of RIS parameters and effectively calibrate the RIS amplitude model online with the help of a user that has an a-priori unknown location.