| 1. |
- Becirovic, Ema, 1992-
(författare)
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Signal Processing Aspects of Massive MIMO
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Massive MIMO (multiple-input-multiple-output) is a technology that uses an antenna array with a massive number of antennas at the wireless base station. It has shown widespread benefit and has become an inescapable solution for the future of wireless communication. The mainstream literature focuses on cases when high data rates for a handful of devices are of priority. In reality, due to the diversity of applications, no solution is one-size-fits-all. This thesis provides signal-processing solutions for three challenging situations. The first challenging situation deals with the acquisition of channel estimates when the signal-to-noise-ratio (SNR) is low. The benefits of massive MIMO are unlocked by having good channel estimates. By the virtue of reciprocity in time-division duplex, the estimates are obtained by transmitting pilots on the uplink. However, if the uplink SNR is low, the quality of the channel estimates will suffer and consequently the spectral efficiency will also suffer. This thesis studies two cases where the channel estimates can be improved: one where the device is stationary such that the channel is constant over many coherence blocks and one where the device has access to accurate channel estimates such that it can design its pilots based on the knowledge of the channel. The thesis provides algorithms and methods that exploit the aforementioned structures which improve the spectral efficiency. Next, the thesis considers massive machine-type communications, where a large number of simple devices, such as sensors, are communicating with the base station. This thesis provides a quantitative study on which type of benefits massive MIMO can provide for this communication scenario — many devices can be spatially multiplexed and their battery life can be increased. Further, activity detection is also studied and it is shown that the channel hardening and favorable propagation properties of massive MIMO can be exploited to design efficient detection algorithms. The third part of the thesis studies a more specific application of massive MIMO, namely federated learning. In federated learning, the goal is for the devices to collectively train a machine learning model based on their local data by only transmitting model updates to the base station. Sum channel estimation has been advocated for blind over-the-air federated learning since fewer communication resources are required to obtain such estimates. On the contrary, this thesis shows that individually estimating each device's channel can save a huge number of resources owing to the fact that it allows for individual processing such as gradient sparsification which in turn saves a huge number of resources that compensates for the channel estimation overhead.
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| 2. |
- Kunnath Ganesan, Unnikrishnan, 1989-
(författare)
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Distributed Massive MIMO : Random Access, Extreme Multiplexing and Synchronization
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The data traffic in wireless networks has grown tremendously over the past few decades and is ever-increasing. Moreover, there is an enormous demand for speed as well. Future wireless networks need to support three generic heterogeneous services: enhanced mobile broadband(eMBB), ultra-reliable low latency communication (URLLC) and massive machine type communication (mMTC). Massive MIMO has shown to be a promising technology to meet the demands and is now an integral part of 5G networks. To get high data rates, ultra densification of the network by deploying more base stations in the same geographical area is considered. This led to an increase in inter-cell interference which limits the capacity of the network. To mitigate the inter-cell interference, distributed MIMO is advocated. Cell-free massive MIMO is a promising technology to improve the capacity of the network. It leverages all the benefits from ultra densification, massive MIMO, and distributed MIMO technologies and operates without cell boundaries. In this thesis, we study random access, extreme multiplexing capabilities, and synchronization aspects of distributed massive MIMO. In Paper A studies the activity detection in grant-free random access for mMTC in cell-free massive MIMO network. An algorithm is proposed for activity detection based on maximum likelihood detection and the 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. RadioWeaves technology is a new wireless infrastructure devised for indoor applications leveraging the benefits of massive MIMO and cell-free massive MIMO. In Paper B, we study the extreme multiplexing capabilities of RadioWeaves which can provide high data rates with very low power. We observe that the RadioWeaves deployment can spatially separate users much better than a conventional co-located deployment, which outweighs the losses caused by grating lobes and thus saves a lot on transmit power. Paper C studies the synchronization aspect of distributed massive MIMO. We propose a novel, over-the-air synchronization protocol, which we call as BeamSync, to synchronize all the different multi-antenna transmit panels. We also show that beamforming the synchronization signal in the dominant direction of the channel between the panels is optimal and the synchronization performance is significantly better than traditional beamforming techniques.
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| 3. |
- Shaik, Zakir Hussain
(författare)
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Cell-Free Massive MIMO: Distributed Signal Processing and Energy Efficiency
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this era of rapid wireless technological advancements, wireless connectivity between humans, humans with machines, and machines with machines is gradually becoming an absolute necessity. The initial motivation for wireless connectivity was to enable voice communication between humans over a geo-graphical area. Thanks to cellular communications advancements in the past decade, cellular wireless connectivity has become a global success, starting from 1G to the present generation 5G. However, the needs of humans often evolve with time, and now the world is witnessing an ever-growing demand for the internet with high data rates besides reliable voice communication. Current cellular networks suffer from non-uniform data rates across a cell, i.e., users at the cell center and the cell edges experience significant variations in signal-to-noise ratio, making the cellular technology less reliable to meet the future data demands. Moreover, cellular networks operating as cells, i.e., an access point (AP, the term we would use instead of base station) serving the users within its geographical location, cannot leverage the network’s total capacity without cooperation among APs of the neighboring cells. One potential solution is moving away from the cell to cell-free networks wherein all the APs will serve all the users within the geographical coverage area. Thus, there is a need for a paradigm shift in how cellular networks operate. Towards the goal mentioned above to fully leverage the network capacity, the Cell-Free Massive multiple-input-multiple-output (MIMO) technology is expected to be the next potential technology beyond 5G combining the benefits of Massive MIMO and cell-free distributed architectures. Distributed architectures require distributed signal processing algorithms, and also energy consumption of the network is crucial. Keeping in view the practical ease in deployment, we consider a sequentially connected Cell-Free Massive MIMO network called a “radio stripe”. In the first part of the thesis, we focus on developing an optimal sequential algorithm in the sense of mean-square-error (MSE) which has the same performance as that of centralized Cell-Free Massive MIMO implementation with the minimum MSE (MMSE) receiver. We also develop an optimal sequential algorithm that decentralizes the centralized bit LLR computation. Another attractive aspect of these proposed algorithms is that the fronthaul (number of real symbols required by the central processing unit (CPU) to decode the transmitted signal) is independent of the number of APs. On the contrary, centralized implementation fronthaul is dependent on the number of APs, causing scalability problems with the increase in APs. In the second part of the thesis, we develop an algorithm focused on maximizing the energy efficiency of the RadioWeave network in an underlay spectrum sharing. RadioWeave is a technology envisioned to combine Cell-Free Massive MIMO and possibly large intelligent surfaces. We first present the energy efficiency problem, which is non-convex in its original form. Then, a convex lower bound on the problem is provided with an iterative algorithm to solve the problem efficiently.
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| 4. |
- Özdogan, Özgecan, 1992-
(författare)
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Signal Processing Aspects of Massive MIMO and IRS-Aided Communications
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The data traffic in cellular networks has grown at an exponential pace for decades. This trend will most probably continue in the future, driven by new innovative applications. One of the key enablers of future cellular networks is the massive MIMO technology, and it has been started to be commercially deployed in many countries. A massive MIMO base station is equipped with a massive number (e.g., a hundred) of individually steerable antennas, which can be effectively used to serve tens of user equipments simultaneously on the same time-frequency resource. It can provide a notable enhancement of both spectral efficiency and energy efficiency in comparison with conventional MIMO. In the prior literature, the achievable spectral efficiencies of massive MIMO systems with a practical number of antennas have been rigorously characterized and optimized when the channels are subject to either spatially uncorrelated or correlated Rayleigh fading. Typically, in massive MIMO research, i.i.d. Rayleigh fading or less frequently free-space line-of-sight (LoS) channel models are assumed since they simplify the analysis. Massive MIMO technology is able to support both rich scattering and LoS scenarios. Practical channels can consist of a combination of an LoS path and a correlated small-scale fading component caused by a finite number of scattering clusters that can be modeled by spatially correlated Rician fading. In Paper \ref{PaperA}, we consider a multi-cell scenario with spatially correlated Rician fading channels and derive closed-form achievable spectral efficiency expressions for different signal processing techniques. Alternatively, a massive number of antennas can be spread over a large geographical area and this concept is called cell-free massive MIMO. In the canonical form of cell-free massive MIMO, the access points cooperate via a fronthaul network to spatially multiplex the users on the same time-frequency resource using network MIMO methods that only require locally obtained channel state information. Cell-free massive MIMO is a densely deployed system. Hence, the probability of having an LoS path between some access points and the users is quite high. In Paper B, we consider a practical scenario where the channels between the access points and the users are modeled with Rician fading. The main theory for massive MIMO has been developed for uni-polarized single-antenna users. Wireless signals are polarized electromagnetic waves, and there exist two orthogonal polarization dimensions. The practical base stations and user equipments typically utilize dual-polarized antennas (i.e., two co-located antennas that respond to orthogonal polarizations) to squeeze in twice the number of antennas in the same physical enclosure, as well as capturing signal components from both dimensions. In Paper C, we study a single-cell massive MIMO system with dual-polarized antennas at both the base station and users. The channel modeling for dual-polarized channels is substantially more complicated than for conventional uni-polarized channels. A channel model that takes into account several practical aspects that arise when utilizing dual-polarization, such as channel cross-polar discrimination (XPD) and cross-polar receive and transmit correlations (XPC) is considered. Another technology that has exciting prospects and is quickly gaining traction in wireless communications is intelligent reflecting surfaces (IRS). It is also known under the names reconfigurable intelligent surfaces and software-controlled metasurfaces. IRS is a thin two-dimensional metasurface that is used to aid communications. According to the application of interest, an IRS has the ability to control and transform electromagnetic waves that are impinging on it. In this thesis, we study different aspects of this technology such as pathloss modeling, channel estimation, and different technology use cases. In Paper D, we derive the pathloss model using physical optics techniques for an IRS that is configured to reflect an incoming wave from a far-field source towards a receiver in the far-field. In Paper E, we demonstrate how an IRS can be used to increase the rank of the channel matrix in LoS point-to-point MIMO communications by creating a controllable path that complements the uncontrollable paths. Bringing IRS technology into reality requires addressing many practical challenges. For instance, the proper configuration of an IRS critically depends on accurate channel state information. However, there are two main issues that complicate the channel acquisition with IRS. First, the IRS is not inherently equipped with transceiver chains. Therefore, it can not sense the pilot signals. Besides, introducing an IRS into an existing setup will increase the number of channel coefficients proportionally to the number of IRS elements. In Paper F, we present a deep learning-based approach for phase reconfiguration at an IRS in order to learn and make use of the local propagation environment.
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| 5. |
- Becirovic, Ema, 1992-, et al.
(författare)
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Combining Reciprocity and CSI Feedback in MIMO Systems
- 2022
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Ingår i: IEEE Transactions on Wireless Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1276 .- 1558-2248. ; 21:11, s. 10065-10080
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Tidskriftsartikel (refereegranskat)abstract
- Reciprocity-based time-division duplex (TDD) Massive MIMO (multiple-input multiple-output) systems utilize channel estimates obtained in the uplink to perform precoding in the downlink. However, this method has been criticized of breaking down, in the sense that the channel estimates are not good enough to spatially separate multiple user terminals, at low uplink reference signal signal-to-noise ratios, due to insufficient channel estimation quality. Instead, codebook-based downlink precoding has been advocated for as an alternative solution in order to bypass this problem. We analyze this problem by considering a "grid-of-beams world" with a finite number of possible downlink channel realizations. Assuming that the terminal accurately can detect the downlink channel, we show that in the case where reciprocity holds, carefully designing a mapping between the downlink channel and the uplink reference signals will perform better than both the conventional TDD Massive MIMO and frequency-division duplex (FDD) Massive MIMO approach. We derive elegant metrics for designing this mapping, and further, we propose algorithms that find good sequence mappings.
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| 6. |
- Kunnath Ganesan, Unnikrishnan, 1989-, et al.
(författare)
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Bridging the Digital Divide Using SuperCell Massive MIMO
- 2022
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Ingår i: 2022 IEEE 96TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-FALL). - London, United Kingdom : Institute of Electrical and Electronics Engineers (IEEE). - 9781665454681 - 9781665454698
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Konferensbidrag (refereegranskat)abstract
- Massive multiple input multiple output (MIMO) emerged as the leading technology for supporting fifth generation (5G) and beyond 5G cellular communication systems. Due to the tremendous increase in data traffic in urban areas and to meet such a significant demand, most studies consider macro/micro cell deployments in urban environments. Internet service providers (ISPs) are less interested in providing communication services in rural areas considering the relatively low profits compared to the deployment and maintenance costs. In this paper, we investigate the massive MIMO performance in rural scenarios. In particular, we investigate different aspects to consider while designing a long-range communication system. We propose to use elevated base station (BS) with sectorized antennas with unusually large aperture and implement a user scheduling algorithm at the BS to provide full digital coverage. We analyze the coverage range of a massive MIMO system to provide high-rate services. Furthermore, we also analyze the link budget requirements and the rates users can achieve in such a SuperCell massive MIMO network.
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