230 research outputs found
Sum Throughput Maximization in Multi-Tag Backscattering to Multiantenna Reader
Backscatter communication (BSC) is being realized as the core technology for
pervasive sustainable Internet-of-Things applications. However, owing to the
resource-limitations of passive tags, the efficient usage of multiple antennas
at the reader is essential for both downlink excitation and uplink detection.
This work targets at maximizing the achievable sum-backscattered-throughput by
jointly optimizing the transceiver (TRX) design at the reader and
backscattering coefficients (BC) at the tags. Since, this joint problem is
nonconvex, we first present individually-optimal designs for the TRX and BC. We
show that with precoder and {combiner} designs at the reader respectively
targeting downlink energy beamforming and uplink Wiener filtering operations,
the BC optimization at tags can be reduced to a binary power control problem.
Next, the asymptotically-optimal joint-TRX-BC designs are proposed for both low
and high signal-to-noise-ratio regimes. Based on these developments, an
iterative low-complexity algorithm is proposed to yield an efficient
jointly-suboptimal design. Thereafter, we discuss the practical utility of the
proposed designs to other application settings like wireless powered
communication networks and BSC with imperfect channel state information.
Lastly, selected numerical results, validating the analysis and shedding novel
insights, demonstrate that the proposed designs can yield significant
enhancement in the sum-backscattered throughput over existing benchmarks.Comment: 17 pages, 5 figures, accepted for publication in IEEE Transactions on
Communication
Integer-forcing in multiterminal coding: uplink-downlink duality and source-channel duality
Interference is considered to be a major obstacle to wireless communication. Popular approaches, such as the zero-forcing receiver in MIMO (multiple-input and multiple-output) multiple-access channel (MAC) and zero-forcing (ZF) beamforming in MIMO broadcast channel (BC), eliminate the interference first and decode each codeword separately using a conventional single-user decoder. Recently, a transceiver architecture called integer-forcing (IF) has been proposed in the context of the MIMO Gaussian multiple-access channel to exploit integer-linear combinations of the codewords. Instead of treating other codewords as interference, the integer-forcing approach decodes linear combinations of the codewords from different users and solves for desired codewords. Integer-forcing can closely approach the performance of the optimal joint maximum likelihood decoder. An advanced version called successive integer-forcing can achieve the sum capacity of the MIMO MAC channel. Several extensions of integer-forcing have been developed in various scenarios, such as integer-forcing for the Gaussian MIMO broadcast channel, integer-forcing for Gaussian distributed source coding and integer-forcing interference alignment for the Gaussian interference channel.
This dissertation demonstrates duality relationships for integer-forcing among three different channel models. We explore in detail two distinct duality types in this thesis: uplink-downlink duality and source-channel duality. Uplink-downlink duality is established for integer-forcing between the Gaussian MIMO multiple-access channel and its dual Gaussian MIMO broadcast channel. We show that under a total power constraint, integer-forcing can achieve the same sum rate in both cases. We further develop a dirty-paper integer-forcing scheme for the Gaussian MIMO BC and show an uplink-downlink duality with successive integer-forcing for the Gaussian MIMO MAC. The source-channel duality is established for integer-forcing between the Gaussian MIMO multiple-access channel and its dual Gaussian distributed source coding problem. We extend previous results for integer-forcing source coding to allow for successive cancellation. For integer-forcing without successive cancellation in both channel coding and source coding, we show the rates in two scenarios lie within a constant gap of one another. We further show that there exists a successive cancellation scheme such that both integer-forcing channel coding and integer-forcing source coding achieve the same rate tuple
Advances in Resource Allocation Optimization for Multiuser Wireless Systems with Joint Energy and Information Transfer
Ph.DDOCTOR OF PHILOSOPH
Multi-static Parameter Estimation in the Near/Far Field Beam Space for Integrated Sensing and Communication Applications
This work proposes a maximum likelihood (ML)-based parameter estimation
framework for a millimeter wave (mmWave) integrated sensing and communication
(ISAC) system in a multi-static configuration using energy-efficient hybrid
digital-analog arrays. Due to the typically large arrays deployed in the higher
frequency bands to mitigate isotropic path loss, such arrays may operate in the
near-field regime. The proposed parameter estimation in this work consists of a
two-stage estimation process, where the first stage is based on far-field
assumptions, and is used to obtain a first estimate of the target parameters.
In cases where the target is determined to be in the near-field of the arrays,
a second estimation based on near-field assumptions is carried out to obtain
more accurate estimates. In particular, we select beamfocusing array weights
designed to achieve a constant gain over an extended spatial region and
re-estimate the target parameters at the receivers. We evaluate the
effectiveness of the proposed framework in numerous scenarios through numerical
simulations and demonstrate the impact of the custom-designed flat-gain
beamfocusing codewords in increasing the communication performance of the
system.Comment: 16 page
Energy Efficient Massive MIMO and Beamforming for 5G Communications
Massive multiple-input multiple-output (MIMO) has been a key technique
in the next generation of wireless communications for its potential to achieve
higher capacity and data rates. However, the exponential growth of data
traffic has led to a significant increase in the power consumption and system
complexity. Therefore, we propose and study wireless technologies to improve the trade-off between system performance and power consumption of wireless communications.
This Thesis firstly proposes a strategy with partial channel state information
(CSI) acquisition to reduce the power consumption and hardware complexity of massive MIMO base stations. In this context, the employment of partial CSI is proposed in correlated communication channels with user mobility. By exploiting both the spatial correlation and temporal correlation of the channel, our analytical results demonstrate significant gains in the energy efficiency of the massive MIMO base station.
Moreover, relay-aided communications have experienced raising interest; especially, two-way relaying systems can improve spectral efficiency with short required operating time. Therefore, this Thesis focuses on an uncorrelated massive MIMO two-way relaying system and studies power
scaling laws to investigate how the transmit powers can be scaled to improve the energy efficiency up to several times the energy efficiency without power scaling while approximately maintaining the system performance.
In a similar line, large antenna arrays deployed at the space-constrained relay would give rise to the spatial correlation. For this reason, this Thesis presents an incomplete CSI scheme to evaluate the trade-off between the spatial correlation and system performance. In addition, the advantages of linear processing methods and the effects of channel aging are investigated to further improve the relay-aided system performance.
Similarly, large antenna arrays are required in millimeter-wave communications to achieve narrow beams with higher power gain. This poses the problem that locating the best beam direction requires high power and complexity consumption. Therefore, this Thesis presents several low-complexity beam alignment methods with respect to the state-of-the-art to evaluate the trade-off between complexity and system performance.
Overall, extensive analytical and numerical results show an improved performance and validate the effectiveness of the proposed techniques
Analysis and Design of Algorithms for the Improvement of Non-coherent Massive MIMO based on DMPSK for beyond 5G systems
Mención Internacional en el título de doctorNowadays, it is nearly impossible to think of a service that does not rely on wireless communications.
By the end of 2022, mobile internet represented a 60% of the total global online traffic.
There is an increasing trend both in the number of subscribers and in the traffic handled by each
subscriber. Larger data rates, smaller extreme-to-extreme (E2E) delays and greater number of
devices are current interests for the development of mobile communications. Furthermore, it
is foreseen that these demands should also be fulfilled in scenarios with stringent conditions,
such as very fast varying wireless communications channels (either in time or frequency) or
scenarios with power constraints, mainly found when the equipment is battery powered.
Since most of the wireless communications techniques and standards rely on the fact that the
wireless channel is somehow characterized or estimated to be pre or post-compensated in transmission
(TX) or reception (RX), there is a clear problem when the channels vary rapidly or the
available power is constrained. To estimate the wireless channel and obtain the so-called channel
state information (CSI), some of the available resources (either in time, frequency or any
other dimension), are utilized by including known signals in the TX and RX typically known as
pilots, thus avoiding their use for data transmission. If the channels vary rapidly, they must be
estimated many times, which results in a very low data efficiency of the communications link.
Also, in case the power is limited or the wireless link distance is large, the resulting signal-tointerference-
plus-noise ratio (SINR) will be low, which is a parameter that is directly related to
the quality of the channel estimation and the performance of the data reception. This problem
is aggravated in massive multiple-input multiple-output (massive MIMO), which is a promising
technique for future wireless communications since it can increase the data rates, increase the
reliability and cope with a larger number of simultaneous devices. In massive MIMO, the base
station (BS) is typically equipped with a large number of antennas that are coordinated. In these
scenarios, the channels must be estimated for each antenna (or at least for each user), and thus,
the aforementioned problem of channel estimation aggravates. In this context, algorithms and
techniques for massive MIMO without CSI are of interest.
This thesis main topic is non-coherent massive multiple-input multiple-output (NC-mMIMO)
which relies on the use of differential M-ary phase shift keying (DMPSK) and the spatial
diversity of the antenna arrays to be able to detect the useful transmitted data without CSI knowledge. On the one hand, hybrid schemes that combine the coherent and non-coherent
schemes allowing to get the best of both worlds are proposed. These schemes are based on
distributing the resources between non-coherent (NC) and coherent data, utilizing the NC data
to estimate the channel without using pilots and use the estimated channel for the coherent
data. On the other hand, new constellations and user allocation strategies for the multi-user
scenario of NC-mMIMO are proposed. The new constellations are better than the ones in the
literature and obtained using artificial intelligence techniques, more concretely evolutionary
computation.This work has received funding from the European Union Horizon 2020 research and innovation
programme under the Marie Skłodowska-Curie ETN TeamUp5G, grant agreement No.
813391. The PhD student was the Early Stage Researcher (ESR) number 2 of the project.
This work has also received funding from the Spanish National Project IRENE-EARTH
(PID2020-115323RB-C33) (MINECO/AEI/FEDER, UE), which funded the work of some coauthors.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Luis Castedo Ribas.- Secretario: Matilde Pilar Sánchez Fernández.- Vocal: Eva Lagunas Targaron
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