Multi-antenna Base Station Research Articles

Joint Wideband Spectrum Sensing and Carrier Frequency Estimation in the Multi-Path Propagation Environment Based on Sub-Nyquist Sampling

We consider the wideband spectrum sensing within a multi-path propagation environment, where a multi-antenna base station (BS) is tasked with identifying the frequency positions of multiple narrowband transmissions distributed across a broad range of frequencies. To tackle this, we propose a sub-Nyquist sampling structure that incorporates a phased array system. Specifically, each antenna is connected to two separate sampling channels, i.e., one for direct sampling and another for delayed sampling, with the latter incorporating a specified time delay factor. The cross-correlation matrices associated with the samples, which are characterized by different time lags, are calculated. These matrices are represented in tensor form, and the factor matrices are extracted through CANDECOMP/PARAFAC (CP) decomposition. By these factor matrices, the carrier frequencies and the power spectra of the far-field signals of interest are estimated. Numerical simulations are conducted to evaluate the performance of the proposed method, and the results reveal the feasibility and effectiveness of the approach, demonstrating its potential for accurate and efficient wideband spectrum sensing in complex multi-path propagation environments.

Transmit Precoding via Block Diagonalization with Approximately Optimized Distance Measures for Limited Feedback in Dense Cellular Networks with Multiantenna Base Stations

Transmit Precoding via Block Diagonalization with Approximately Optimized Distance Measures for Limited Feedback in Dense Cellular Networks with Multiantenna Base Stations

UAV-mounted IRS assisted wireless powered mobile edge computing systems: Joint beamforming design, resource allocation and position optimization

Intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have been recently used in wireless-powered mobile edge computing (MEC) systems to enhance the computation bits and energy harvesting performance. However, in the conventional IRS- and UAV-aided MEC systems, the IRS is installed at fixed locations on a building, which restricts the computation performance. UAV-mounted IRS (UAV-IRS), as a promising technology, combines the advantages of UAV and IRS. Hence, in this work, we study a UAV-IRS wireless-powered MEC system, where multiple UAV-IRSs are considered between Internet of Things (IoT) devices and the base station to improve the computation bits and energy harvesting. The multi-antenna base station first charges the IoT devices via radio frequency signals, and then IoT devices offload their computation tasks to the base station via UAV-IRSs. We formulate a computation bits maximization problem for all IoT devices by jointly determining detection beamforming at IoT devices, active energy beamforming at the base station, power allocation, time slot assignment, CPU frequency, the phase shifts design in the wireless energy transfer (WET) and task offloading, and UAV-IRSs positions. A block coordinate descent (BCD) algorithm by decomposing the introduced problem into four blocks is proposed, while the detection beamforming, active energy beamforming, transmit power, time slot assignment, CPU frequency, and the phase shifts design in the task offloading are derived in closed-form results. Also, the successive convex approximation and semidefinite relaxation (SDR) are adopted to obtain the UAV-IRS positions and the phase shifts in the WET, respectively. The simulation results verify the effectiveness of the presented BCD method compared with the different benchmark schemes.

A Joint Communication and Computation Design for Probabilistic Semantic Communications.

In this paper, the problem of joint transmission and computation resource allocation for a multi-user probabilistic semantic communication (PSC) network is investigated. In the considered model, users employ semantic information extraction techniques to compress their large-sized data before transmitting them to a multi-antenna base station (BS). Our model represents large-sized data through substantial knowledge graphs, utilizing shared probability graphs between the users and the BS for efficient semantic compression. The resource allocation problem is formulated as an optimization problem with the objective of maximizing the sum of the equivalent rate of all users, considering the total power budget and semantic resource limit constraints. The computation load considered in the PSC network is formulated as a non-smooth piecewise function with respect to the semantic compression ratio. To tackle this non-convex non-smooth optimization challenge, a three-stage algorithm is proposed, where the solutions for the received beamforming matrix of the BS, the transmit power of each user, and the semantic compression ratio of each user are obtained stage by stage. The numerical results validate the effectiveness of our proposed scheme.

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.

Multi-Agent-Deep-Reinforcement-Learning-Enabled Offloading Scheme for Energy Minimization in Vehicle-to-Everything Communication Systems

Offloading computation-intensive tasks to mobile edge computing (MEC) servers, such as road-side units (RSUs) and a base station (BS), can enhance the computation capacities of the vehicle-to-everything (V2X) communication system. In this work, we study an MEC-assisted multi-vehicle V2X communication system in which multi-antenna RSUs with liner receivers and a multi-antenna BS with a zero-forcing (ZF) receiver work as MEC servers jointly to offload the tasks of the vehicles. To control the energy consumption and ensure the delay requirement of the V2X communication system, an energy consumption minimization problem under a delay constraint is formulated. The multi-agent deep reinforcement learning (MADRL) algorithm is proposed to solve the non-convex energy optimization problem, which can train vehicles to select the beneficial server association, transmit power and offloading ratio intelligently according to the reward function related to the delay and energy consumption. The improved K-nearest neighbors (KNN) algorithm is proposed to assign vehicles to the specific RSU, which can reduce the action space dimensions and the complexity of the MADRL algorithm. Numerical simulation results show that the proposed scheme can decrease energy consumption while satisfying the delay constraint. When the RSUs adopt the indirect transmission mode and are equipped with matched-filter (MF) receivers, the proposed joint optimization scheme can decrease the energy consumption by 56.90% and 65.52% compared to the maximum transmit power and full offloading schemes, respectively. When the RSUs are equipped with ZF receivers, the proposed scheme can decrease the energy consumption by 36.8% compared to the MF receivers.

Joint Design of Long-Term Base Station Activation and Short-Term Beamforming for Green Wireless Networks

Base station (BS) activation is a widely-used approach to alleviate the system power cost for device maintenance. However, frequently switching on/off BSs may also introduce extra power and signaling costs, which motivates us to limit the BS switching frequency when performing BS activation. To address this, we study a problem of joint long-term BS activation and short-term beamforming (J-LTBA-STBF) in a network where multiple multi-antenna BSs cooperatively serve multiple single-antenna users. LTBA means that each BS’s active/inactive switching frequency is properly controlled to yield relatively stable BS-user association and limited switching power, while STBF lies in that due to the low cost of refreshing beamformers, the transmit beamformers are allowed to be updated frequently to maximally lower the transmit power. Following the idea, two efficient algorithms are designed, according to the data amount they request to start running, to optimize the active BSs and the BS transmit beamformers in multiple adjacent time slices. Consequently, the transmit, maintenance and switching powers can be well balanced to facilitate power-efficient communications. Numerical results validate the efficacies of the algorithms.

Multi-User Tracking in Reconfigurable Intelligent Surface Aided Near-Field Wireless Communications System

An uplink multi-user tracking problem aided by multiple passive reconfigurable intelligent surfaces (RISs) is addressed in this work. Under a near-field circumstance, a multi-antenna base station (BS) localizes multiple moving single-antenna users by processing the received signals transmitted by users and reflected by RISs. Considering the users’ mobility and the potential obstruction of line-of-sight paths, a multi-user tracking system based on the extended Kalman filter (EKF) which fully exploits the temporal correlations between each user’s coordinate changes is designed. Then, the Bayesian Cramér–Rao bound (BCRB) of tracking errors is derived in a pattern consistent with the EKF process. Subsequently, an optimization scheme for passive phase shift design at the RISs is devised by minimizing the derived BCRB and is solved using the Gradient Descent method. Numerical results indicate that the accuracy of our tracking algorithm can approach the BCRB. With abundant RISs deployed and optimized, high-precision multi-user tracking via a single BS can be realized even in harsh localization environments.

Outage Analysis of IRS-Assisted RF Powered Networks for Energy-Constrained IoT Devices

This paper studies the performance of an intelligent reflecting surface (IRS)-assisted radio frequency (RF)-powered wireless network. In the considered network, a multi-antenna base station (BS) communicates with its users (UEs) through downlink transmission and energy-constrained internet-of-things (IoT) devices, which are randomly deployed around a cluster head (CH), utilize the resources of BS for energy harvesting and information transmission. IRS, deployed randomly around CH, reflects the RF signals transmitted by BS to the IoT devices, providing additional energy to harvest. For the considered network, average distances between BS, CH, IRS, and an IoT device have been computed, statistically. Furthermore, the average harvested energy at an IoT device is computed. Instead of considering a fixed transmit power (FTP) of an IoT device, we consider that its transmit power varies randomly depending on the stored energy. Using the Markov chain model, we obtain transmission probability and state-dependent transmit power (SDTP). Analytical expressions for outage probability and throughput of an IoT device are obtained. To manifest the effectiveness of derived expressions, numerical results are compared with Monte-Carlo simulation results. Our results reveal that the proposed SDTP scheme has a performance gain over the FTP scheme, especially in scenarios where harvested energy is in abundance.

RIS-Assisted Full-Duplex Integrated Sensing and Communication

In this paper, we explore the application of reconfigurable intelligent surface (RIS) in the integrated sensing and communication network, where a full-duplex multi-antenna base station (BS) concurrently detects a target and communicates with a user equipment (UE). Our objective is to maximize the UE’s transmission rate by jointly optimizing the BS’s transmit beamforming, UE’s transmit power, and RIS’s phase shifts, while satisfying the condition on the minimum required sensing power. We develop a block coordinate ascend-based iterative algorithm to solve the formulated problem, which guarantees the convergence to at least a local optimum. Numerical results show the efficiency of the proposed solution as well as the trade-off between the UE’s transmission rate and the required sensing power, along with the efficiency of employing RIS.

Robust Transmit Beamforming for Secure Integrated Sensing and Communication

This paper studies a downlink secure integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) transmits confidential messages to a single-antenna communication user (CU) while performing sensing on targets that may act as suspicious eavesdroppers. To ensure the quality of target sensing while preventing their potential eavesdropping, the BS combines the transmit confidential information signals with additional dedicated sensing signals, which play a dual role of artificial noise (AN) for degrading the qualities of eavesdropping channels. Under this setup, we jointly design the transmit information and sensing beamforming, with the objective of minimizing the weighted sum of beampattern matching errors and cross-correlation patterns for sensing subject to secure communication constraints. The robust design takes into account the channel state information (CSI) imperfectness of the eavesdroppers in two practical CSI error scenarios. First, we consider the scenario with bounded CSI errors of eavesdroppers, in which the worst-case secrecy rate constraint is adopted to ensure secure communication performance. In this scenario, we present the optimal solution to the worst-case secrecy rate constrained sensing beampattern optimization problem, by adopting the techniques of S-procedure, semi-definite relaxation (SDR), and a one-dimensional (1D) search, for which the tightness of the SDR is rigorously proved. Next, we consider the scenario with Gaussian CSI errors of eavesdroppers, in which the secrecy outage probability constraint is adopted. In this scenario, we present an efficient algorithm to solve the more challenging secrecy outage-constrained sensing beampattern optimization problem, by exploiting the convex restriction technique based on the Bernstein-type inequality, together with the SDR and 1D search. Finally, numerical results show that the proposed designs can properly adjust the information and sensing beams to balance the tradeoffs among communicating with CU, sensing targets, and confusing eavesdroppers, so as to achieve desirable sensing transmit beampatterns while ensuring the CU’s secrecy requirements for the two scenarios.

On the Capacity and State Estimation Error of “Beam-Pointing” Channels: The Binary Case

Motivated by beamformed millimeter-wave (mmWave) communication, we consider the optimal tradeoff between reliable communication rate and state estimation error for a new state-dependent channel model with in-block memory referred to as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">binary beam-pointing</i> (BBP) channel. The multiantenna base station (BS) uses a finite beamforming codebook (i.e., discrete beam directions) and associates the target user to the most favorable beam, i.e., the beam directed along the strongest propagation path from BS to the user in 5G and IEEE 802.11ad mmWave communication systems. We model the target user’s Angle-of-Departure (AoD) as the state of a state-dependent channel. Since the AoD remains almost constant over several consecutive time slots, the channel has memory. In addition, we assume the BS receives implicit causal feedback (e.g., modeling a backscatter signal as in radar) and consider a joint communication and sensing (JCAS) problem where the BS is also interested in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">explicitly</i> estimate the channel state (quantized AoD). We derive a closed-form solution to the capacity of the BBP channel model under the peak input cost constraint. Under the average constraint, we present an implicit capacity result, where the capacity is given as the solution of an optimization problem, and we provide an algorithm for its numerical computation. Finally, for the JCAS problem at hand, we provide the minimum distortion under the peak input constraint and show that this coincides with that obtained by the capacity-achieving strategy. This <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">completely characterizes the capacity-distortion tradeoff</i> for the BBP channel under peak input constraint. For the average constraint case, numerical results show that our capacity-achieving strategy yields a state estimation error very close to the theoretical minimum, showing the near-optimality of the proposed strategy for the JCAS problem under the average cost constraint.

IRS-Driven Cybersecurity of Healthcare Cyber Physical Systems

Intelligent reflective surface (IRS) as one of the most potential 6G key technologies injects new vitality into the smart medical and health system by intelligently configuring the wireless communication environment and spawns the construction of a modern medical information system. Medical data security is a sensitive issue that hospitals are generally concerned about in recent years. There are great hidden dangers to information security in the environment of “cloud storage”. The IRS provides a new solution for enhancing the privacy and security of “cloud storage” medical file data, assisting in the efficient transmission and interconnection of information, and ensuring highly secure and uninterrupted services in healthcare-based cyber physical systems (CPS). Specifically, the modelling in this paper considers the situation where communication over multiple-input single-output (MISO) channel is eavesdropped by a patient spy holding a single antenna receiver, and additionally the patient spy is obscured by an obstacle. One of the multi-antenna base stations (BS) transmits independent data streams to doctors and keeps them secret from patient spies. By jointly optimizing the beamforming at the BS and the phase shift of the IRS reflection elements, the simulation analysis verifies that the introduction of IRS can effectively improve the privacy security of medical data.

Energy-Efficient Base Station Switching-Off With Guaranteed Cooperative Profit Gain of Mobile Network Operators

This paper studies the energy-efficient cooperative base station (BS) switching-off in multi-input single-output (MISO) cellular networks, where the roaming-cost-based cooperation and the cooperative beamforming among multiple mobile network operators (MNOs) are jointly designed. To make the cooperation among MNOs practical, we introduce a new constraint to make sure the cooperative profit gain of MNOs is non-negative and also formulate an optimization problem to maximize the system energy efficiency (EE) by jointly considering multiple factors, including the operation modes of BSs, the connection states between users and BSs, the beamforming vectors of multi-antenna BSs under constraints of users’ quality of service, BSs’s power budget, and the non-negative profit gain of MNOs, etc. To solve the non-convex problem, we propose an energy-efficient beamforming-aware BS switching off method (EBBSOM), in which we first design a roaming-cost-based BS switching-off algorithm (EE-BSOA) to search the feasible inactive BSs and then propose a successive convex approximation (SCA) and Dinkelbach’s method-based EE maximization beamforming algorithm (EE-BFA) to optimize beamforming vectors. In order to keep the cooperative profit gain non-negative for MNOs and also to balance the load and power consumption of BS from the profit perspective, we present a profit-wise BS switching-off priority method. We theoretically prove that the proposed EBBSOM is able to resolve the inconsistency between BS switching-off and EE maximization. Simulations show that the proposed EBBSOM enhances the system EE without degrading the economic profit gain of MNOs. Also, the proposed profit-wise BS switching-off priority enables the EBBSOM to achieve higher EE than traditional load-wise one. Furthermore, it is also observed that there exists a feasible rate price region, which is able to clearly characterize network EE and the average profit gain of MNOs simultaneously.

Optimized Design for IRS-Assisted Integrated Sensing and Communication Systems in Clutter Environments

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system design in a clutter environment. Assisted by an IRS equipped with a uniform planar array (UPA), a multi-antenna base station (BS) is targeted for simultaneously sensing multiple targets in the non-light-of-sight (NLoS) region and communicating with multiple communication users (CUs). We consider the joint IRS-assisted ISAC design in the case with Type-I or Type-II CUs, where each Type-I CU and Type-II CU can and cannot cancel the interference from sensing signals, respectively. Under the perfect communication/sensing channel state information assumption, we aim to maximize the minimum sensing beampattern gain among multiple targets, where the sensing beampattern gain qualifies the achieved illumination signal power at the given location of the target of interest. We jointly optimize the BS’s communication-sensing beamformers and the IRS’s phase shifting matrix, subject to the signal-to-interference-plus-noise ratio (SINR) constraint for each Type-I/Type-II CU, the interference power constraint per clutter, the transmission power constraint at the BS, and the cross-correlation pattern constraint. Due to the design variable coupling, the joint IRS-assisted ISAC design problem is shown to be non-convex in the case with Type-I or Type-II CUs. To circumvent the non-convexity dilemma, we propose semidefinite relaxation (SDR) based alternating optimization algorithms in both cases, for which the computational complexity and convergency behavior are analyzed. In the case with Type-I CUs, we show that the dedicated sensing signal at the BS can help enhance the sensing performance gain. By contrast, the dedicated sensing signal at the BS is not required for the IRS-assisted ISAC designs in the case with Type-II CUs. Numerical results are provided to show that the proposed IRS-assisted ISAC design schemes achieve a significant gain over the existing benchmark schemes.

Intelligent Reflecting Surface Backscatter Enabled Uplink Coordinated Multi-Cell MIMO Network

This paper proposes an intelligent reflecting surface (IRS) backscatter based uplink coordinated transmission strategy for a radio cellular network, where IRS serves as a transmitter enabling uplink transmission from each user to the associated base station (BS). To be specific, the considered network is made up of multiple cells, each of which consists of one multi-antenna BS and its served users. While one multi-antenna power beacon (PB) is deployed to radiate energy-bearing electromagnetic wave, the radio signal received by each IRS is modulated to send its connective user’s information to the associated BS. Based on such a network frame, this paper aims to maximize the weighted sum rate (WSR) under the constraints of total transmit power and reflecting phase shifts by joint optimization of active beamforming at the PB, passive beamforming at the IRSs and uplink user scheduling. To address this challenging problem, fractional programming (FP), alternative optimization and weighted bipartite matching are employed to convert the logarithm objective function into a more tractable form and to handle the optimization variables. The simulation results demonstrate the achievable WSR of the considered network.

Multi-Active/Passive-IRS Enabled Wireless Information and Power Transfer: Active IRS Deployment and Performance Analysis

Intelligent reflecting surfaces (IRSs), active and/or passive, can be densely deployed in complex environments to significantly enhance wireless network coverage for both wireless information transfer (WIT) and wireless power transfer (WPT). In this letter, we study the downlink WIT/WPT from a multi-antenna base station to a single-antenna user over a multi-active/passive IRS (AIRS/PIRS)-enabled wireless link. In particular, we derive the location of the AIRS with those of the other PIRSs being fixed to maximize the received signal-to-noise ratio (SNR) and signal power at the user in the cases of WIT and WPT, respectively. The derived solutions reveal that the optimal AIRS deployment is generally different for WIT versus WPT due to the different roles of AIRS-induced amplification noise. Furthermore, both analytical and numerical results are provided to show the conditions under which the proposed AIRS deployment strategy yields superior performance to other baseline deployment strategies as well as the conventional all-PIRS enabled WIT/WPT.

Integrating Intelligent Reflecting Surface Into Base Station: Architecture, Channel Model, and Passive Reflection Design

Intelligent reflecting surface (IRS) has emerged as a cost-efficient technique to improve the wireless network’s capacity and performance. Existing works on IRS have mainly considered IRS being deployed in the environment to dynamically control the wireless channels between the base station (BS) and its served users in favor of their communications. In contrast, we propose in this paper a new integrated IRS-BS architecture by deploying IRSs inside the BS’s antenna radome to directly reconfigure the signal radiation to/from the BS’s antennas. In other words, the IRSs can be considered as auxiliary passive arrays with real-time reconfigurability equipped at the BS to enhance its communication performance cost-effectively. Since the distance between the integrated IRSs and BS’s antenna array is practically small (in the order of several to tens of wavelengths), the path loss among them is significantly reduced as compared to conventional IRS deployed much farther away from the BS, while the real-time control of the IRS’s reflection by the BS becomes easier to implement. However, the resultant near-field channel model also becomes drastically different from its far-field counterpart for conventional far-away IRSs in the literature. Thus, we propose an element-wise channel model for IRS to characterize the channel vector between each single-antenna user and the antenna array of the BS, which includes the direct (without any IRS’s reflection) as well as the single and double IRS-reflection channel components. Based on this channel model, we formulate a problem to optimize the reflection coefficients of all IRS reflecting elements for maximizing the uplink sum-rate of the users. By considering two typical cases with/without perfect channel state information (CSI) at the BS, the formulated problem is solved efficiently by adopting the successive refinement method and iterative random phase algorithm (IRPA), respectively. Numerical results validate the substantial capacity gain of the integrated IRS-BS architecture over the conventional multi-antenna BS without integrated IRS. Moreover, the proposed algorithms significantly outperform other benchmark schemes in terms of sum-rate, and the IRPA without CSI can approach the performance upper bound with perfect CSI as the training overhead increases.

Analysis and Optimization of a Double-IRS Cooperatively Assisted System With a Quasi-Static Phase Shift Design

The analysis and optimization of single intelligent reflecting surface (IRS)-assisted systems have been extensively studied, whereas little is known regarding multiple-IRS-assisted systems. This paper investigates the analysis and optimization of a double-IRS cooperatively assisted downlink system (D-IRS-C), where a multi-antenna base station (BS) serves a single-antenna user with the help of two multi-element IRSs, connected by an inter-IRS channel. The channel between any two nodes is modeled with Rician fading. The BS adopts the instantaneous CSI-adaptive maximum-ratio transmission (MRT) beamformer, and the two IRSs adopt a cooperative quasi-static phase shift design. The goal is to maximize the average achievable rate, which can be reflected by the average channel power of the equivalent channel between the BS and user at low channel estimation and phase adjustment costs and computational complexity. First, we obtain tractable expressions of the average channel power of the equivalent channel in the general (Rician factor), pure line of sight (LoS), and pure non-line of sight (NLoS) regimes, respectively. Then, we jointly optimize the phase shifts of the two IRSs to maximize the average channel power of the equivalent channel in these regimes. The optimization problems are challenging non-convex problems. We obtain globally optimal closed-form solutions for some cases and propose computationally efficient iterative algorithms to obtain stationary points for the other cases. Next, we compare the computational complexity for optimizing the phase shifts and the optimal average channel power of D-IRS-C with those of a counterpart double-IRS non-cooperatively assisted system (D-IRS-NC) and a counterpart single-IRS-assisted system (S-IRS) at a large number of reflecting elements in the three regimes. Finally, we numerically demonstrate notable gains of the proposed solutions over the existing solutions at different system parameters. To our knowledge, this is the first work that optimizes the quasi-static phase shift design of D-IRS-C and characterizes its advantages over the optimal quasi-static phase shift design of the counterpart D-IRS-NC and S-IRS.

RIS-Aided Integrated Sensing and Communication: Joint Beamforming and Reflection Design

Integrated sensing and communication (ISAC) has been envisioned as a promising technique to alleviate the spectrum congestion problem. Inspired by the applications of reconfigurable intelligent surface (RIS) in dynamically manipulating wireless propagation environment, in this paper, we investigate to deploy a RIS in an ISAC system to pursue performance improvement. Particularly, we consider a RIS-assisted ISAC system where a multi-antenna base station (BS) performs multi-target detection and multi-user communication with the assistance of a RIS. Our goal is maximizing the weighted summation of target detection signal-to-noise ratios (SNRs) by jointly optimizing the transmit beamforming and the RIS reflection coefficients, while satisfying the communication quality-of-service (QoS) requirement, the total transmit power budget, and the restriction of RIS phase-shift. An efficient alternating optimization algorithm combining the majorization-minimization (MM), penalty-based, and manifold optimization methods is developed to solve the resulting complicated non-convex optimization problem. Simulation results illustrate the advantages of deploying RIS in ISAC systems and the effectiveness of our proposed algorithm.