1,721,016 research outputs found
Regularized zero-forcing aided hybrid beamforming for millimeter-wave multi-user MIMO systems
No full textThis paper considers hybrid beamforming consisting of analog beamforming (ABF) coupled with digital baseband beamforming (DBF) which is designed for multi-user (MU) multiple input multiple output (MIMO) millimeter-wave (mmWave) communications. ABF uses a limited number of radio frequency (RF) chains and finite-resolution phase-shifters to alleviate the power consumption at the base station (BS), while DBF uses either zero-forcing beamforming (ZFB) or regularized zero forcing beamforming (RZFB) to restrain MU interference. The joint design of ABF and DBF constitutes a computationally challenging mixed discrete continuous optimization problem. The paper develops efficient algorithms for its solution, which iterate scalable-complex expressions. Furthermore, we conceive a new class of MU RZFB for attaining higher rates. Simulations are provided to demonstrate the viability of the proposed algorithms and the advantages of the conceived RZFB
Widely linear processing improves the throughput of nonorthogonal user access
Full text linkThe quality-of-service (QoS) provided by wireless communication networks can be upgraded by the technique of non-orthogonal multiple-access (NOMA) in tandem with widely linear beamforming (WLB), which uses a pair of beamformers for each information symbol. Conventionally, rate-fairness among the users is achieved by maximizing the users’ minimal throughput (max-min throughput optimization). However, this is computationally challenging, as each iteration requires solving a high-dimensional convex optimization problem, even for small networks. We circumvent this by maximizing the geometric mean (GM) of the users’ throughput (GM-throughput maximization) and design novel algorithms based on iterating closed-form expressions are developed, which are shown to be hundreds of times more computationally efficient than the existing algorithms that are based on convex-solvers. The proposed algorithms are developed for both conventional wireless networks and networks requiring ultra-reliable and low-latency communications (URLLC)
Active RIS-Assisted Multi-User Multi-Stream Transmit Precoding Relying on Scalable-Complexity Iterations
No full textThis is the first investigation focused on delivering multi-stream information to multiple multi-antenna users employing an active reconfigurable intelligent surface (aRIS)-assisted system. We conceive the joint design of the transmit precoders and of the aRIS’s power-amplified reconfigurable elements (APRES) to enhance the log-det rate objective functions for all users, which poses large-scale mixed discrete continuous problems. We develop a max-min log-det solver, which iterates quadratic-solvers of cubic complexity to maximize the nonsmooth function representing the minimum of the users’ log-det rate functions. To mitigate the computational burden associated with cubically escalating complexity in large-scale scenarios, we introduce a pair of alternative problems aimed at maximizing the smooth functions representing the sum of the users’ log-det rate function (sum log-det) and the soft minimum of the users’ log-det rate function (soft min log-det). We develop sum log-det and soft max-min solvers, leveraging closed-form expressions of scalable (linear) complexity for efficient computation. This approach ensures practicality in addressing large-scale scenarios. Furthermore, the soft min log-det enables us to enhance the log-det rates for all users and their sum, ultimately improving the quality of delivering multi-user multi-stream information
Holographic multi-user multi-stream beamforming maintaining rate-fairness
No full textWe present the first investigation into the transmission of multi-stream information from a base station equipped with reconfigurable holographic surfaces (RHS) to multiple users with the aid of multi-antenna arrays. Building upon this, we propose the joint design of RHS and baseband beamformers that enables multi-stream delivery at fair rates across all users. Specifically, we first introduce a max-min rate optimization approach, which aims for maximizing the minimum rate for all users through iterative solutions of quadratic problems. To reduce complexity, we then propose a surrogate-based optimization approach that offers a low-complexity design alternative relying on closed-form updates. Our simulations show that the surrogate-based approach achieves nearly the same minimum rate as max-min optimization, while delivering sum-rates comparable to those of sum-rate maximization, overcoming the rate-fairness deficiency typical of the latter
Active-RIS enhances the multi-user rate of multi-carrier communications
No full textThis paper explores a multi-user multi-carrier system leveraging an active reconfigurable intelligent surface (RIS), where the joint design of the RIS’s programmable reflecting elements and the subcarrier-wise beamformers at the base station is investigated. To overcome the limitation of the conventional design, which aims solely at sum-rate maximization resulting in zero rates for some users across all sub-carriers and thus failing to boost all users rates, we propose the two alternative designs: one maximizing the geometric mean of the users’ rates (GM-rate maximization) and the other maximizing the soft users’ minimum rate (soft max-min rate optimization). However, they pose challenges as large-scale nonconvex problems, rendering convex-solver computational approaches impractical. To tackle this, we develop iterative computational procedures based on closed-form expressions of scalable complexity. Extensive simulations demonstrate the substantial benefits of these novel designs in significantly enhancing multi-user rates. Notably, under the same power budget, the active-RIS-assisted multi-carrier system achieves approximately twice the minimum user-rate or sum rate compared to RIS-less or passive-RIS-assisted counterparts
Max-min rate optimization of low-complexity hybrid multi-user beamforming maintaining rate-fairness
No full textA wireless network serving multiple users in the millimeter-wave or the sub-terahertz band by a base station is considered. High-throughput multi-user hybrid-transmit beamforming is conceived by maximizing the minimum rate of the users. For the sake of energy-efficient signal transmission, the array-of-subarrays structure is used for analog beamforming relying on low-resolution phase shifters. We develop a convexsolver based algorithm, which iteratively invokes a convex problem of the same beamformer size for its solution. We then introduce the soft max-min rate objective function and develop a scalable algorithm for its optimization. Our simulation results demonstrate the striking fact that soft max-min rate optimization not only approaches the minimum user rate obtained by max-min rate optimization but it also achieves a sum rate similar to that of sum-rate maximization. Thus, the soft max-min rate optimization based beamforming design conceived offers a new technique of simultaneously achieving a high individual quality-of-service for all users and a high total network throughput
Finite-blocklength RIS-aided transmit beamforming
No full textThis paper considers the downlink of an ultra-reliable low-latency communication (URLLC) system in which a base station (BS) serves multiple single-antenna users in the short (finite) blocklength (FBL) regime with the assistance of a reconfigurable intelligent surface (RIS). In the FBL regime, the users' achievable rates are complex functions of the beamforming vectors and of the RIS's programmable reflecting elements (PREs). We propose the joint design of the transmit beamformers and PREs to maximize the geometric mean (GM) of these rates (GM-rate) and show that this approach provides fair rate distribution and thus reliable links to all users. A novel computational algorithm is developed, which is based on closed forms to generate improved feasible points. Simulations show the merit of our solution.</p
Signal detection in fractional Gaussian noise and an RKHS approach to robust detection and estimation
No full textThis thesis is divided into two parts. In the first part, the problem of signal detection in fractional Gaussian noise is considered. To facilitate the study of this problem, several results related to the reproducing kernel Hilbert space of fractional Brownian motion are presented. In particular, this reproducing kernel Hilbert space is characterized completely and an alternative characterization for the restriction of this class of functions to a compact interval (0,T) is given. Infinite-interval whitening filters for fractional Brownian motion are also developed. Application of these results to the signal detection problem yields necessary and sufficient conditions for a deterministic or stochastic signal to produce a nonsingular shift when embedded in additive fractional Gaussian noise. Also, a formula for the likelihood ratio corresponding to any deterministic nonsingular shift is developed. Finally, some results concerning detector performance in the presence of additive fractional Gaussian noise are presented.In the second part of the thesis, the application of reproducing kernel Hilbert space theory to the problems of robust detection and estimation is investigated. It is shown that this approach provides a general and unified framework in which to analyze the problems of L\sp2 estimation, matched filtering, and quadratic detection in the presence of uncertainties regarding the second-order structure of the random processes involved. Minimax robust solutions to these problems are characterized completely, and some results concerning existence of robust solutions are presented.Made available in DSpace on 2011-05-07T14:14:57Z (GMT). No. of bitstreams: 2
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A new class of analog precoding for multi-antenna multi-user communications over high-frequency bands
No full textA network relying on a large antenna-array-aided base station is designed for delivering multiple information streams to multi-antenna users over high-frequency bands such as the millimeter-wave and sub-Terahertz bands. The state-of-the-art analog precoder (AP) dissipates excessive circuit power due to its reliance on a large number of phase shifters. To mitigate the power consumption, we propose a novel AP relying on a controlled number of phase shifters. Within this new AP framework, we design a hybrid precoder (HP) for maximizing the users’ minimum throughput, which poses a computationally challenging problem of large-scale, nonsmooth mixed discrete-continuous log-determinant optimization. To tackle this challenge, we develop an algorithm which iterates through solving convex problems to generate a sequence of HPs that converges to the max-min solution. We also introduce a new framework of smooth optimization termed soft max-min throughput optimization. Additionally, we develop another algorithm, which iterates by evaluating closed-form expressions to generate a sequence of HPs that converges to the soft max-min solution. Simulation results reveal that the HP soft max-min solution approaches the Pareto-optimal solution constructed for simultaneously optimizing both the minimum throughput and sum-throughput. Explicitly, it achieves a minimum throughput similar to directly maximizing the users’ minimum throughput and it also attains a sum-throughput similar to directly maximizing the sum-throughput
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