Sum Rate Maximization Problem Research Articles

Blocklength Allocation and Power Control in UAV-Assisted URLLC System via Multi-agent Deep Reinforcement Learning

Integration of unmanned aerial vehicles (UAVs) with ultra-reliable and low-latency communication (URLLC) systems can improve the real-time communication performance for various industrial internet of things (IIoT) applications. Designing an intelligent resource allocation system is one of the challenges posed by an energy-constrained UAV communication system. Therefore, we formulate a sum rate maximization problem, subject to the UAVs’ energy by optimizing the blocklength allocation and the power control jointly in the uplink UAV-assisted URLLC systems, in which the probabilistic channel model between UAV and users is adopted. The problem is difficult to solve due to the non-convex objective function and the energy constraints, and also challenging to make fast decision in the complex communication environment. Thus, we propose a deep reinforcement learning (DRL)-based scheme to optimize the blocklength allocation and power control jointly. First, transform the original problem into the multi-agent reinforcement learning process, where each subcarrier is regarded as the agent that optimizes its individual blocklength allocation and power control. Then, each agent makes the intelligent decision to obtain the maximum reward value depending on the weighted segmented reward function, which is related to the UAV energy consumption and user rates to improve the rate performance. Finally, the simulation results show that the proposed scheme outperforms the benchmark schemes and has the stable convergence in different settings, such as the learning rate, the error probability, the subcarrier spacing, and the number of users.

Sum-rate maximization for a distributed space-time block code-aided cooperative NOMA with energy harvesting

AbstractIn this paper, we exploit the spatial and transmission diversities in cooperative non-orthogonal multiple access (C-NOMA) networks to improve the system sum-rate. To achieve this, we propose a user-pairing scheme where near-field user pairs serve as relays for user pairs in the far-field region. Based on this pairing scheme, we incorporate a space-time block code transmission technique at the near-field user pairs to maximize the transmission diversity in the cooperative phase. Moreover, we consider a non-linear energy harvesting model at the near-field user pair to alleviate the problem of energy consumption during the cooperative transmission phase. Further to this, we formulate a sum-rate maximization problem that is addressed from the viewpoint of joint power allocation factor and power splitting ratio optimization. We develop a low-computational iterative algorithm based on the concepts of the Stackelberg game and the Nash bargaining solution. We benchmark our findings with different scenarios, such as energy harvesting C-NOMA with a fixed power allocation factor and power splitting ratio, energy harvesting C-NOMA without STBC, non-cooperative NOMA, and orthogonal multiple access. The results obtained via simulations outperform the benchmark schemes.

A sum‐rate maximization for IRS‐aided broadcast SWIPT with time‐switching structure and nonlinear energy harvester

AbstractThis study evaluates the sum‐rate gain of the intelligent reflecting surface (IRS) in a broadcast simultaneous wireless information and power transfer network. Users are equipped with a time‐switching (TS) structure for an alternating information decoder and a nonlinear energy harvester. The sum‐rate maximization (SRM) problem is formulated subject to the constraints of the energy‐harvesting threshold required by users and limited transmission power at the base station (BS). To address the SRM problem, we jointly designed a broadcast beamforming precoding vector at the BS, reflective phase shifters at the IRS, and TS factors at the users. Subsequently, after certain manipulations, we propose efficient iterative algorithms that combine alternating optimization, successive convex approximation, semidefinite relaxation, and rank‐one penalty function methods to obtain a suboptimal solution. Finally, the numerical results show the promising sum‐rate gain of the proposed scheme compared with the conventional schemes.

Radio resource management for OFDM-based dual-function radar-communication: sum-rate and fairness

AbstractThis paper focuses on radio resource management (RRM) in multi-user dual-function radar communication (DFRC) systems using orthogonal frequency division multiplexing (OFDM) waveforms. We propose two RRM schemes, one from the perspective of sum rate maximization and the other from the perspective of user fairness maximization. These optimization problems are non-convex due to the presence of mixed integer terms, making them difficult to solve. To address these challenges, we have employed a decomposition approach to transform these two complex problems into separate, more readily solvable ones. In addressing the sum rate maximization problem, we initially introduce a heuristic greedy algorithm to obtain a resource allocation scheme that satisfies radar performance requirements. Subsequently, we utilize a cyclic iterative method along with KKT conditions to solve the sum rate maximization problem for communication users. Concerning the fairness maximization problem for communication users, we similarly employ a heuristic greedy algorithm to obtain a resource allocation scheme that meets radar performance constraints. Then utilize the Lagrangian dual method to solve the multi-user fairness maximization problem for communication users. Our experimental results confirm the effectiveness of the proposed algorithms.

A sum rate maximization problem in uplink MIMO with RSMA systems

This study explores the problem of maximizing the sum rate in uplink multi-user Multiple-Input Multiple-Output (MIMO) using Rate-Splitting Multiple Access (RSMA) systems. The investigation revolves around the scenario where the Users (UEs) are single-antenna nodes transmitting data to a multi-antenna Base Station (BS) through the RSMA technique. The optimization process encompasses determining parameters such as UEs’ transmit powers, decoding order, and detection vector at the BS. An approach based on Deep Reinforcement Learning (DRL) is introduced to address this challenge. This DRL framework involves an action-refined stage and applies a Deep Deterministic Policy Gradient (DDPG)-based strategy. Simulation outcomes effectively demonstrate the convergence of the proposed DRL framework, where it converges after approximately 1,800 episodes. Also, the results prove the superior performance of the proposed method when compared to established benchmark strategies, where it is up to 45% and 86% higher than the local search and random schemes, respectively.

Joint Precoding Design for Sub-Connected Hybrid Beamforming System

Hybrid beamforming has been widely considered in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system, which can greatly reduce power consumption and hardware cost of data paths. Compared to the fully-connected hybrid beamforming architecture, the sub-connected architecture is more practical for its reduced complexity. However, optimal precoding design for the sub-connected architecture is not straightforward due to the specific block-diagonal structure of analog phase shifter network. Algorithms on fully-digital or fully-connected hybrid beamforming architecture cannot be directly applied to sub-connected architecture. Meanwhile, most existing precoding algorithms in such case can only solve the approximate problem, which results in significant performance loss. In this paper, we study the sum rate maximization problem in the sub-connected architecture. We first relax the objective function and derive a relaxed upper bound of the original problem. Then we propose an algorithm to solve the original problem with a local-optimal solution. Simulation results show that the proposed local-optimal algorithm outperforms the baseline algorithms with better sum rate and energy efficiency performance. Besides, the proposed algorithm also converges quickly and is robust.

Algorithm-Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks

The user-centric cell-free network has emerged as an appealing technology to improve the wireless communication’s capacity of the internet of things (IoT) networks thanks to its ability to eliminate inter-cell interference effectively. However, the cell-free network inevitably brings in higher hardware cost and backhaul overhead as a larger number of base stations (BSs) are deployed. Additionally, severe channel fading in high-frequency bands constitutes another crucial issue that limits the practical application of the cell-free network. In order to address the above challenges, we amalgamate the cell-free system with another emerging technology, namely reconfigurable intelligent surface (RIS), which can provide high spectrum and energy efficiency with low hardware cost by reshaping the wireless propagation environment intelligently. To this end, we formulate a weighted sum-rate (WSR) maximization problem for RIS-assisted cell-free systems by jointly optimizing the BS precoding matrix and the RIS reflection coefficient vector. Subsequently, we transform the complicated WSR problem to a tractable optimization problem and propose a distributed cooperative alternating direction method of multipliers (ADMM) to fully utilize parallel computing resources. Inspired by the model-based algorithm unrolling concept, we unroll our solver to a learning-based deep distributed ADMM (D-ADMM) network framework. To improve the efficiency of the D-ADMM in distributed BSs, we develop a monodirectional information exchange strategy with a small signaling overhead. In addition to benefiting from domain knowledge, D-ADMM adaptively learns hyper-parameters and non-convex solvers of the intractable RIS design problem through data-driven end-to-end training. Finally, numerical results demonstrate that the proposed D-ADMM achieve around 210% improvement in capacity compared with the distributed noncooperative algorithm and almost 96% compared with the centralized algorithm.

Power Allocation for NOMA With Cache-Aided D2D Communication

Communication networks are becoming increasingly content-centric, and reusable content like viral information and video on demand are often requested by multiple users. Caching at the user equipment enables device-to-device (D2D) communications to be used to deliver requested contents which will help reduce data traffic at base stations and also enhance the achievable data rates. In this paper, we propose a system whereby users are able to exchange valuable cached content with each other via a D2D link which underlays the reception of a downlink non-orthogonal multiple access (NOMA) signal. We formulated a sum rate maximization problem that is subject to minimum rate constraints and derived optimal solutions depending on which user is the D2D transmitter. Additionally, sub-optimal solutions based on a negligible self-interference assumption are also proposed. Simulation results demonstrate the significant performance gains of underlaid D2D communications as compared with optimal downlink cellular NOMA. Furthermore, results on the self-interference (SI) cancellation factor highlight that the sub-optimal power allocation solution offers sum rate performance close to the optimal case. The performance gains are further enhanced when SI cancellation is high.

Intelligent Reflecting Surface Backscatter Enabled Downlink Multi-Cell MIMO Networks

This paper proposes to leverage intelligent reflecting surface (IRS) backscatter to implement downlink communications for a multi-cell multiple-input multiple-output (MIMO) network, which represents a brand-new communication framework. In such a network, one active macro cell base station (MBS) is deployed for radiating energy signal, while each IRS acts as a small cell base station to realize its own information transmission by modulating and reflecting the signal from the MBS. Under this paradigm, we investigate two optimization problems, namely weighted sum rate maximization problem and max-min fairness problem, aiming at satisfying different communication requirements. To seek their optimal solutions, Lagrangian dual transform and alternate methods are employed to optimize the active beamforming vector at the MBS and the passive beamforming vectors at all the IRSs. Additionally, an element clustering scheme is developed to reduce computation and control complexity, in which each element cluster works like a unit and all clusters can collaborate to communicate with users. Extensive simulations are conducted to evaluate the achievable communication performance and to verify the feasibility of the proposed IRS backscatter enabled downlink multi-cell MIMO network.

Robust Beamforming Optimization Design for RIS-Aided MIMO Systems With Practical Phase Shift Model and Imperfect CSI

The ideal phase shift model and the acquisition of channel state information (CSI) are practically difficult to realize in reconfigurable intelligent surface (RIS) assisted communication systems. Therefore, these have greatly troubled the beamforming design of multiple-input multiple-output (MIMO) systems via RIS. This paper presents a robust beamforming design framework in the RIS-enhanced MIMO communication networks in the presence of considering practical phase shifts for the RIS and imperfect CSI of the cascade channels. More specifically, we propose an alternative optimization framework and adopt the distributed alternating direction method of multipliers (ADMM) method to solve the sum-rate maximization problem by combining the true environment model (TEM) and the mismatch environment model (MEM) with beamforming. In particular, the proposed scheme is also used for the MEM scenario. It shows that the sum rate of the ADMM scheme with MEM under low SNR is about 3 bps/Hz more than the BFP method, approximately 6 bps/Hz more than the APG method. Next, when the SNR is 5dB, the above three schemes are greater than SDR method. But, the above methods are smaller than SDR method in MEM. Then, the sum rate of the SDR method with the TEM is approximately 1.6 times of the MEM, while the CPU time of both is consistent with the computational complexity. Therefore, the differences between the two models affect the performance achieved by different methods. The proposed approach sheds a light to engineers working in wireless communication on enhancing the transmission robustness of existing wireless networks.

K-Receiver Wiretap Channel: Optimal Encoding Order and Signaling Design

The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -receiver wiretap channel is a channel model where a transmitter broadcasts <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> independent messages to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> intended receivers while keeping them secret from an eavesdropper. The capacity region of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -receiver multiple-input multiple-output (MIMO) wiretap channel has been characterized by using dirty-paper coding and stochastic encoding. However, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> factorial encoding orders may need to be enumerated to evaluate the capacity region, which makes the problem intractable. In addition, even though the capacity region is known, the optimal signaling to achieve the capacity region is unknown. In this paper, we determine one optimal encoding order to achieve every point on the capacity region, and thus reduce the encoding complexity <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> factorial times. We prove that the optimal decoding order for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -receiver MIMO wiretap channel is the same as that for the MIMO broadcast channel without secrecy. To be specific, the descending weight ordering in the weighted sum-rate (WSR) maximization problem determines the optimal encoding order. Next, to reach the border of the secrecy capacity region, we form a WSR maximization problem and apply the block successive maximization method to solve this nonconvex problem and find the input covariance matrices corresponding to each message. Numerical results are used to verify the optimality of the encoding order and to demonstrate the efficacy of the proposed signaling design.

Tunable SRS-Aware Robust Coordinated Beamforming Design With Channel Aging

Coordinated beamforming holds tremendous potential to maximize network capacity, whose effect largely depends on the period sounding reference signal (SRS)-based channel sounding. However, the huge discrepancy between the sounding channel and the real-time channel is inevitable and nonnegligible with channel aging, which results in severe degradation of coordinated beamforming gain. In this paper, the discrepancy is modeled as a random error term with the effect of channel aging included, and a robust coordinated beamforming design with channel aging is further proposed, exploiting the potential capacity gain through the channel statistical information, which provides the potential for tuning the SRS period. Specifically, the joint correlation properties of channels are modeled in the random error matrix with the static statistical information collected from practical channel samples. To cope with multiple random terms, the deterministic equivalent method is adopted to obtain the approximate closed-form expression of the ergodic capacity, aiming at deriving a robust beamformer that can achieve a stationary point of the expected weighted sum-rate maximization problem. In addition, by adopting the proposed beamforming design, the tuning guidance for the SRS period is provided for practical networks satisfying different capacity requirements and mobile scenarios. Simulation results show that the proposed design can extend the SRS period more than 1× at 10% capacity loss when user speed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$v=30\rm {km/h}$</tex-math></inline-formula> , compared with the traditional beamforming design.

Hybrid/Reflective Beamforming for IRS-Assisted Dual-Function Radar-Communication System

This paper investigates the joint design problem of the hybrid/reflective beamforming for the dual-function radar-communication (DRC) system with the help of an intelligent reconfigurable surface (IRS). The degrees of freedom for beamformer design are the analog and digital beamformers at the base station (BS), and the reflective beamformer at the IRS. To begin with, we formulate the beamformer design problem as a weighted sum-rate (WSR) maximization problem subject to the transmit power and similarity between the designed beamformer and the reference one with desired beampattern property. Then, utilizing equivalent reformulation of the preceding design problem, we conceive an alternating direction sequential programming (ADSP) based algorithm that iteratively updates the following low-complexity subproblems: 1) digital beamformer at the BS through the Powell-Hestenes-Rockafellar (PHR) method; 2) analog beamformer at the BS using the riemannian conjugate gradient method; and 3) reflective beamformer at the IRS by the successive convex approximation (SCA) technique. The convergence is ensured by analytically offering the non-decreasing and boundary of the WSR value. Finally, numerical results verify the effectiveness of the proposed algorithm, which achieves superior WSR performance while ensuring the radar requirement.

Rate Splitting Multiple Access for Next Generation Cognitive Radio Enabled LEO Satellite Networks

Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.

Cooperative Beamforming for RIS-Aided Cell-Free Massive MIMO Networks

The combination of cell-free massive multiple-input multiple-output (CF-mMIMO) and reconfigurable intelligent surface (RIS) is envisioned as a promising paradigm to improve network capacity and enhance coverage capability. However, to reap full benefits of RIS-aided CF-mMIMO, the main challenge is to efficiently design cooperative beamforming (CBF) at base stations (BSs), RISs, and users. Firstly, we investigate the fractional programing to convert the weighted sum-rate (WSR) maximization problem into a tractable optimization problem. Then, the alternating optimization framework is employed to decompose the transformed problem into a sequence of subproblems, i.e., hybrid BF (HBF) at BSs, passive BF at RISs, and combining at users. In particular, the alternating direction method of multipliers algorithm is utilized to solve the HBF subproblem at BSs. Concretely, the analog BF design with unit-modulus constraints is solved by the manifold optimization (MO) while we obtain a closed-form solution to the digital BF design that is essentially a convex least-square problem. Additionally, the passive BF at RISs and the analog combining at users are designed by primal-dual subgradient and MO methods. Moreover, considering heavy communication costs in conventional CF-mMIMO systems, we propose a partially-connected CF-mMIMO (P-CF-mMIMO) framework to decrease the number of connections among BSs and users. To better compromise WSR performance and network costs, we formulate the BS selection problem in the P-CF-mMIMO system as a binary integer quadratic programming (BIQP) problem, and develop a relaxed linear approximation algorithm to handle this BIQP problem. Finally, numerical results demonstrate superiorities of our proposed algorithms over baseline counterparts.

LMA-SLN beamforming optimization for sum-rate maximization in intelligent reflecting surface assisted NOMA Systems

Intelligent reflecting surfaces (IRS) can effectively improve the system performance of non-orthogonal multiple access (NOMA) systems. In this paper, we propose a Levenberg–Marquardt algorithm-based supervised learning network (LMA-SLN) to maximize the sum-rate of an IRS-assisted NOMA system. By decoupling the sum-rate maximization problem into the active and passive beamforming optimization sub-problems, we design an alternating optimization scheme to optimize the active and passive beamformings. Then, the LMA-SLN is trained with ideal channel state information (CSI) to obtain the optimized network parameters. Finally, the trained LMA-SLN is applied to optimize the active and passive beamformings without requiring CSI. The experimental results show that the proposed LMA-SLN scheme achieves the superior performance on improving the sum-rate.

Integrated Robotics Networks with Co-Optimization of Drone Placement and AirGround Communications

Terrestrial robots, i.e., unmanned ground vehicles (UGVs), and aerial robots, i.e., unmanned aerial vehicles (UAVs), operate in separate spaces. To exploit their complementary features (e.g., fields of views, communication links, computing capabilities), a promising paradigm termed integrated robotics network therefore emerges, which provides communications for cooperative UAVs-UGVs applications. However, how to efficiently deploy UAVs and schedule the UAVs-UGVs connections according to different UGV tasks become challenging. In this paper, we consider the sum-rate maximization problem, where UGVs plan their trajectories autonomously and are dynamically associated with UAVs according to their planned trajectories. Although this problem is a NP-hard mixed integer program, a fast polynomial time algorithm using alternating gradient descent and penalty-based binary relaxation, is devised. Simulation results demonstrate the effectiveness of the proposed algorithm.

RIS-Aided Cell-Free MIMO System: Perfect and Imperfect CSI Design for Energy Efficiency

Thanks to the development of the 6th generation mobile network that makes it possible for us to move towards an intelligent ubiquitous information society, among which some novel technologies represented by cell-free network has also attracted widespread academic attention. Cell-free network has brought distinguished gains to the network capacity with its strong ability against inter-cell interference. Unfortunately, further improvement demands more base stations (BSs) to be settled, which incurs steep cost increase. To address this issue, reconfigurable intelligent surface (RIS) with low cost and power consumption is introduced in this paper to replace some of the trivial BSs in the system, then, a RIS-aided cell-free network paradigm is formulated. Our objective is to solve the weighted sumrate (WSR) maximization problem by jointly optimizing the beamforming design at BSs and the phase shift of RISs. Due to the non-convexity of the formulated problem, this paper investigates a joint optimizing scheme based on block coordinate descent (BCD) method. Subsequently, on account of the majority of the precious work reposed perfect channel state information (CSI) setup for the ultimate performance, this paper also extends the proposed algorithm to the case wherein CSI is imperfect by utilizing successive convex approximation (SCA). Finally, simulation results demonstrate that the proposed scheme shows great performance and robustness in perfect CSI scenario as well as the imperfect ones.

Fundamental Limits of Intelligent Reflecting Surface Aided Multiuser Broadcast Channel

Intelligent reflecting surface (IRS) has recently received significant attention in wireless networks owing to its ability to smartly control the wireless propagation through passive reflection. Although prior works have employed the IRS to enhance the system performance under various setups, the fundamental capacity limits of an IRS aided multi-antenna multi-user system have not yet been characterized. Motivated by this, we investigate an IRS aided multiple-input single-output (MISO) broadcast channel by considering the capacity-achieving dirty paper coding (DPC) scheme and dynamic beamforming configurations. We first propose a bisection based framework to characterize its capacity region by optimally solving the sum-rate maximization problem under a set of rate constraints, which is also applicable to characterize the achievable rate region with the zero-forcing (ZF) scheme. Interestingly, it is rigorously proved that dynamic beamforming is able to enlarge the achievable rate region of ZF if the IRS phase-shifts cannot achieve fully orthogonal channels, whereas the attained gains become marginal due to the reduction of the channel correlations induced by smartly adjusting the IRS phase-shifts. The result implies that employing the IRS is able to reduce the demand for implementing dynamic beamforming. Finally, we analytically prove that the sum-rate achieved by the IRS aided ZF is capable of approaching that of the IRS aided DPC with a sufficiently large IRS in practice. Simulation results shed light on the impact of the IRS on transceiver designs and validate our theoretical findings, which provide useful guidelines to practical systems by indicating that replacing sophisticated schemes with easy-implementation schemes would only result in slight performance loss.

Optimization for Reflection and Transmission Dual-Functional Active RIS-Assisted Systems

Reconfigurable intelligent surface (RIS) has been deemed as one of potential components of future wireless communication systems because it can adaptively manipulate the wireless propagation environment with low-cost passive devices. However, due to the severe double path loss, the traditional passive RIS can provide sufficient gain only when receivers are very close to the RIS. Moreover, RIS cannot provide signal coverage for the receivers at the back side of it. To address these drawbacks in practical implementation, we introduce a novel reflection and transmission dual-functional active RIS (DF-ARIS) architecture in this paper, which can simultaneously realize reflection and transmission functionalities with active signal amplification to significantly extend signal coverage and enhance the quality-of-service (QoS) of all users. The problem of joint transmit beamforming and dual-functional active RIS design is investigated in RIS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both sum-rate maximization and power minimization problems are considered. To address their non-convexity, we develop efficient iterative algorithms to decompose them into several separate design problems, which are efficiently solved by exploiting fractional programming (FP) and Riemannian-manifold optimization techniques. Simulation results demonstrate the superiority of the proposed dual-functional active RIS architecture and the effectiveness of our proposed algorithms over various benchmark schemes.