| 1. |
- Ghazanfari, Amin, 1983-
(författare)
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Multi-Cell Massive MIMO: Power Control and Channel Estimation
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cellular network operators have witnessed significant growth in data traffic in the past few decades. This growth occurs due to the increase in the number of connected mobile devices, and further, the emerging mobile applications developed for rendering video-based on-demand services. As the available frequency bandwidth for cellular communication is limited, significant efforts are dedicated to improving the utilization of available spectrum and increasing the system performance with the aid of new technologies. Third-generation (3G) and fourth-generation (4G) mobile communication networks were designed to facilitate high data traffic in cellular networks in past decades. Nevertheless, there is still a requirement for new cellular network technologies to accommodate the ever-growing data traffic demand. The fifth-generation (5G) is the latest generation of mobile communication systems deployed and implemented around the world. Its objective is to meet the tremendous ongoing increase in the data traffic requirements in cellular networks. Massive MIMO (multiple-input-multi-output) is one of the backbone technologies in 5G networks. Massive MIMO originated from the concept of multi-user MIMO. It consists of base stations (BSs) implemented with a large number of antennas to increase the signal strengths via adaptive beamforming and concurrently serving many users on the same time-frequency blocks. With Massive MIMO technology, there is a notable enhancement of both sum spectral efficiency (SE) and energy efficiency (EE) in comparison with conventional MIMO-based cellular networks. Resource allocation is an imperative factor to exploit the specified gains of Massive MIMO. It corresponds to efficiently allocating resources in the time, frequency, space, and power domains for cellular communication. Power control is one of the resource allocation methods of Massive MIMO networks to deliver high spectral and energy efficiency. Power control refers to a scheme that allocates transmit powers to the data transmitters such that the system maximizes some desirable performance metric. The first part of this thesis investigates reusing a Massive MIMO network's resources for direct communication of some specific user pairs known as device-to-device (D2D) underlay communication. D2D underlay can conceivably increase the SE of traditional Massive MIMO networks by enabling more simultaneous transmissions on the same frequencies. Nevertheless, it adds additional mutual interference to the network. Consequently, power control is even more essential in this scenario than the conventional Massive MIMO networks to limit the interference caused by the cellular network and the D2D communication to enable their coexistence. We propose a novel pilot transmission scheme for D2D users to limit the interference on the channel estimation phase of cellular users compared with sharing pilot sequences for cellular and D2D users. We also introduce a novel pilot and data power control scheme for D2D underlaid Massive MIMO networks. This method aims to assure that the D2D communication enhances the SE of the network compared to conventional Massive MIMO networks. In the second part of this thesis, we propose a novel power control approach for multi-cell Massive MIMO networks. The proposed power control approach solves the scalability issue of two well-known power control schemes frequently used in the Massive MIMO literature, based on the network-wide max-min and proportional fairness performance metrics. We first identify the scalability issue of these existing approaches. Additionally, we provide mathematical proof for the scalability of our proposed method. Our scheme aims at maximizing the geometric mean of the per-cell max-min SE. To solve the optimization problem, we prove that it can be rewritten in a convex form and is solved using standard optimization solvers. The final part of this thesis focuses on downlink channel estimation in a Massive MIMO network. In Massive MIMO networks, to fully benefit from large antennas at the BSs and perform resource allocation, the BS must have access to high-quality channel estimates that can be acquired via the uplink pilot transmission phase. Time-division duplex (TDD) based Massive MIMO relies on channel reciprocity for the downlink transmission. Thanks to the channel hardening in the Massive MIMO networks with ideal propagation conditions, users rely on the statistical knowledge of channels for decoding the data in the downlink. However, when the channel hardening level is low, using only the channel statistics causes fluctuations in the performance. We investigate how to improve the performance by empowering the user to estimate the downlink channel from downlink data transmissions utilizing a model-based and a data-driven approach instead of relying only on channel statistics. Furthermore, the performance of the proposed method is compared with solely relying on statistical knowledge.
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| 2. |
- Gülgün, Ziya, 1992-
(författare)
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Physical Layer Security Issues in Massive MIMO and GNSS
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Wireless communication technology has evolved rapidly during the last 20 years. Nowadays, there are huge networks providing communication infrastructures to not only people but also to machines, such as unmanned air and ground vehicles, cars, household appliances and so on. There is no doubt that new wireless communication technologies must be developed, that support the data traffic in these emerging, large networks. While developing these technologies, it is also important to investigate the vulnerability of these technologies to different malicious attacks. In particular, spoofing and jamming attacks should be investigated and new countermeasure techniques should be developed. In this context, spoofing refers to the situation in which a receiver identifies falsified signals, that are transmitted by the spoofers, as legitimate or trustable signals. Jamming, on the other hand, refers to the transmission of radio signals that disrupt communications by decreasing the signal-to-interference-and-noise ratio (SINR) on the receiver side. In this thesis, we analyze the effects of spoofing and jamming both on global navigation satellite system (GNSS) and on massive multiple-input multiple-output (MIMO) communications. GNSS is everywhere and used to provide location information. Massive MIMO is one of the cornerstone technologies in 5G. We also propose countermeasure techniques to the studied spoofing and jamming attacks. More specifically, in paper A we analyze the effects of distributed jammers on massive MIMO and answer the following questions: Is massive MIMO more robust to distributed jammers compared with previous generation’s cellular networks? Which jamming attack strategies are the best from the jammer’s perspective, and can the jamming power be spread over space to achieve more harmful attacks? In paper B, we propose a detector for GNSS receivers that is able to detect multiple spoofers without having any prior information about the attack strategy or the number of spoofers in the environment.
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| 3. |
- Chen, Zheng, 1990-, et al.
(författare)
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Optimizing Information Freshness in a Multiple Access Channel With Heterogeneous Devices
- 2021
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Ingår i: IEEE Open Journal of the Communications Society. - : IEEE. - 2644-125X. ; 2, s. 456-470
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Tidskriftsartikel (refereegranskat)abstract
- In this work, we study age-optimal scheduling with stability constraints in a multiple access channel with two heterogeneous source nodes transmitting to a common destination. The first node is connected to a power grid and it has randomly arriving data packets. Another energy harvesting (EH) sensor monitors a stochastic process and sends status updates to the destination. We formulate an optimization problem that aims at minimizing the average age of information (AoI) of the EH node subject to the queue stability condition of the grid-connected node. First, we consider a Probabilistic Random Access (PRA) policy where both nodes make independent transmission decisions based on some fixed probability distributions. We show that with this policy, the average AoI is equal to the average peak AoI, if the EH node only sends freshly generated samples. In addition, we derive the optimal solution in closed form, which reveals some interesting properties of the considered system. Furthermore, we consider a Drift-Plus-Penalty (DPP) policy and develop AoI-optimal and peak-AoI-optimal scheduling algorithms using the Lyapunov optimization theory. Simulation results show that the DPP policy outperforms the PRA policy in various scenarios, especially when the destination node has low multi-packet reception capabilities.
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| 4. |
- Ghazanfari, Amin, 1983-, et al.
(författare)
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Learning to perform downlink channel estimation in massive MIMO systems
- 2021
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Ingår i: 2021 17th International Symposium on Wireless Communication Systems (ISWCS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728174327 - 9781728174334
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Konferensbidrag (refereegranskat)abstract
- We study downlink (DL) channel estimation in a multi-cell Massive multiple-input multiple-output (MIMO) system operating in a time-division duplex. The users must know their effective channel gains to decode their received DL data signals. A common approach is to use the mean value as the estimate, motivated by channel hardening, but this is associated with a substantial performance loss in non-isotropic scattering environments. We propose two novel estimation methods. The first method is model-aided and utilizes asymptotic arguments to identify a connection between the effective channel gain and the average received power during a coherence block. The second one is a deep-learning-based approach that uses a neural network to identify a mapping between the available information and the effective channel gain. We compare the proposed methods against other benchmarks in terms of normalized mean-squared error and spectral efficiency (SE). The proposed methods provide substantial improvements, with the learning-based solution being the best of the considered estimators.
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| 5. |
- Gülgün, Ziya, 1992-, et al.
(författare)
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Is Massive MIMO Robust Against Distributed Jammers?
- 2021
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Ingår i: IEEE Transactions on Communications. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0090-6778 .- 1558-0857. ; 69:1, s. 457-469
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we evaluate the uplink spectral efficiency (SE) of a single-cell massive multiple-input-multiple-output (MIMO) system with distributed jammers. We define four different attack scenarios and compare their impact on the massive MIMO system as well as on a conventional single-input-multiple-output (SIMO) system. More specifically, the jammers attack the base station (BS) during both the uplink training phase and data phase. The BS uses either least squares (LS) or linear minimum mean square error (LMMSE) estimators for channel estimation and utilizes either maximum-ratio-combining (MRC) or zero-forcing (ZF) decoding vectors. We show that ZF gives higher SE than MRC but, interestingly, the performance is unaffected by the choice of the estimators. The simulation results show that the performance loss percentage of massive MIMO is less than that of the SIMO system. Moreover, we consider two types of power control algorithms: jamming-aware and jamming-ignorant. In both cases, we consider the max-min and proportional fairness criteria to increase the uplink SE of massive MIMO systems. We notice numerically that max-min fairness is not a good option because if one user is strongly affected by the jamming, it will degrade the other users' SE as well. On the other hand, proportional fairness improves the sum SE of the system compared with the full power transmission scenario.
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| 6. |
- Kunnath Ganesan, Unnikrishnan, 1989-, et al.
(författare)
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Clustering-Based Activity Detection Algorithms for Grant-Free Random Access in Cell-Free Massive MIMO
- 2021
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Ingår i: IEEE Transactions on Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 0090-6778 .- 1558-0857. ; 69:11, s. 7520-7530
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Tidskriftsartikel (refereegranskat)abstract
- Future wireless networks need to support massive machine type communication (mMTC) where a massive number of devices accesses the network and massive MIMO is a promising enabling technology. Massive access schemes have been studied for co-located massive MIMO arrays. In this paper, we investigate the activity detection in grant-free random access for mMTC in cell-free massive MIMO networks using distributed arrays. Each active device transmits a non-orthogonal pilot sequence to the access points (APs) and the APs send the received signals to a central processing unit (CPU) for joint activity detection. The maximum likelihood device activity detection problem is formulated and algorithms for activity detection in cell-free massive MIMO are provided to solve it. The simulation results show that the macro diversity gain provided by the cell-free architecture improves the activity detection performance compared to co-located architecture when the coverage area is large.
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| 7. |
- Shaik, Zakir Hussain, et al.
(författare)
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Distributed Computation of A Posteriori Bit Likelihood Ratios in Cell-Free Massive MIMO
- 2021
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Ingår i: 2021 29th European Signal Processing Conference (EUSIPCO). - : European Signal Processing Conference, EUSIPCO. - 9789082797060 ; , s. 935-939
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel strategy to decentralize the soft detection procedure in an uplink cell-free massive multiple-input-multiple-output network. We propose efficient approaches to compute the a posteriori probability-per-bit, exactly or approximately, when having a sequential fronthaul. More precisely, each access point (AP) in the network computes partial sufficient statistics locally, fuses it with received partial statistics from another AP, and then forward the result to the next AP. Once the sufficient statistics reach the central processing unit, it performs the soft demodulation by computing the log-likelihood ratio (LLR) per bit, and then a channel decoding algorithm (e.g., a Turbo decoder) is utilized to decode the bits. We derive the distributed computation of LLR analytically.
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