1,721,372 research outputs found
Collaborative beamforming aided fog radio access networks
No full textThe success of fog radio access networks (F-RANs) is critically dependent on the potential quality of service (QoS) that they can offer to users in the face of capacity-constrained fronthaul links and limited caches at their remote radio heads (RRHs). In this context, the collaborative beamforming design is very challenging, since it constitutes a large-dimensional nonlinearly constrained optimization problem. The paper develops a new technique for tackling these critical challenges in fog computing. We show that all the associated constraints can be efficiently dealt with maximizing the geometric mean (GM) of the user throughputs (GM-throughput) subject to the affordable total transmit power constraints. To elaborate, the GM-throughput maximization judiciously exploits the fronthaul links and the RRHs' caches by relying on our novel algorithm, which evaluates low-complexity closed-form expressions in each of its iterations. The problem of F-RAN energy-efficiency is also addressed while maintaining the target throughput. Numerical examples are provided for quantifying the efficiency of the proposed algorithms
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
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
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
Rate-fairness-aware low resolution RIS-aided multi-User OFDM beamforming
No full textThis paper investigates reconfigurable intelligent surface (RIS)-aided OFDM network, where a multiple-antenna aided base station (BS) transmits its downlink (DL) signals to multiple single-antenna users via an RIS, which consists of a considerable amount of low-resolution programmable reflecting elements (PREs). Explicitly, we propose the joint design of the multi-user (MU) beamformers and the RIS’s PREs for quality-of-service target in terms of the individual users’ rates. In the face of dispersive channels, we demonstrate that this poses a large-scale mixed discrete continuous optimization problem of intractable nature. We then tackle this challenge by developing low-complexity iterative procedures, which invoke light-weight closed-form expressions at each iteration, are developed for its computational solution. The simulations demonstrate their computational efficiency and additionally, reveal the deficiencies of the conventional MU OFDM beamforming design based on sum-rate maximization
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
Low-complexity pareto-optimal 3D beamforming for the full-dimensional multi-user massive MIMO downlink
No full textFull-dimensional (FD) multi-user massive multiple-input multiple output (m-MIMO) systems employ large two-dimensional (2D) rectangular antenna arrays to control both the azimuth and elevation angles of signal transmission. We introduce the sum of two outer products of the azimuth and elevation beamforming vectors having moderate dimensions as a new class of FD beamforming. We show that this low-complexity class is capable of outperforming 2D beamforming relying on the single outer product of the azimuth and elevation beamforming vectors. It is also capable of performing close to its FD counterpart of massive dimensions in terms of either the users' minimum rate or their geometric mean rate (GM-rate), or sum rate (SR). Furthermore, we also show that even FD beamforming may be outperformed by our outer product-based improper Gaussian signaling solution. Explicitly, our design is based on low-complexity algorithms relying on convex problems of moderate dimensions for max-min rate optimization or on closed-form expressions for GM-rate and SR maximization
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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