We investigate resource allocation for a movable antenna (MA) enabled integrated sensing and communication (ISAC) system scanning a sector for sensing and simultaneously serving multiple communication users using multiple variable-length snapshots. To tackle the critical challenges of slow antenna movement speed, dynamic radar cross section (RCS) variation, imperfect channel state information (CSI), and finite precision antenna positioning encountered in practice, we propose a novel two-timescale (TTS) optimization framework. In particular, we jointly optimize the discrete MA positions, the communication and sensing beamforming vectors, and the snapshot durations for minimization of the average transmit power at the base station (BS) while guaranteeing a minimum sensing and communication quality of service (QoS) and accounting for imperfect CSI. To overcome the slow antenna movement speed, the MA positions are adjusted only once per scanning period whereas the beamforming vectors and snapshot durations are adapted in every snapshot. Furthermore, to manage the impact of varying RCSs, a novel chance constraint for the sensing QoS is introduced. To solve the resulting challenging highly non-convex mixed integer non-linear program (MINLP), an efficient iterative algorithm exploiting alternating optimization (AO) is developed and shown to yield a high-quality suboptimal solution. Our simulation results reveal that the proposed MA enabled ISAC system cannot only significantly reduce the BS transmit power compared to systems relying on fixed-position antennas and antenna selection but also exhibits a remarkable robustness to RCS fluctuations and imperfect CSI. Furthermore, the proposed TTS framework achieves a similar performance as a system adjusting the MA positions in every snapshot, while the TTS approach significantly reduces the time used for MA adjustment.

We investigate a novel integrated sensing and communication (ISAC) system supported by pinching antennas (PAs), which can be dynamically activated along a dielectric waveguide to collect spatially diverse observations. This capability allows different PAs to view the same target from different angles across time, thereby introducing target diversity, which is a key advantage over conventional fixed antenna arrays. To quantify the sensing reliability, we adopt the outage probability as a performance metric, capturing the likelihood that the accumulated radar echo signal power falls below a detection threshold. In contrast to traditional ISAC models that assume deterministic sensing channels, we explicitly account for the look-angle dependence of radar cross-section (RCS) by modeling it as a random variable. We ensure the long-term quality-of-service (QoS) for communication users by enforcing an accumulated data rate constraint over time. We derive an exact closed-form expression for the sensing outage probability based on the distribution of weighted sums of exponentially distributed random variables. Since the resulting expression is highly non-convex and intractable for optimization, we use a tractable upper bound based on the Chernoff inequality and formulate a PA activation optimization problem. A successive convex approximation (SCA) framework is proposed to efficiently solve the formulated problem. Numerical results show that dynamically activating different PAs across time slots significantly enhances sensing reliability compared to repeatedly activating the same PA at a fixed position and conventional antenna selection schemes, respectively. These findings highlight the benefits of integrating outage-based reliability metrics and target diversity into ISAC systems using PAs.

We investigate resource allocation in integrated sensing and communication (ISAC) systems exploiting movable antennas (MAs) to enhance system performance. Unlike the existing ISAC literature, we account for dynamic radar cross-section (RCS) variations. Chance constraints are introduced and integrated into the sensing quality of service (QoS) framework to precisely control the impact of the resulting RCS uncertainties. Taking into account the dynamic nature of the RCS, we jointly optimize the MA positions and the communication and sensing beam design for minimization of the total transmit power at the base station (BS) while ensuring the individual communication and sensing task QoS requirements. To tackle the resulting non-convex mixed integer non-linear program (MINLP), we develop an iterative algorithm to obtain a high quality suboptimal solution. Our numerical results reveal that the proposed MA-enhanced ISAC system cannot only significantly reduce the BS transmit power compared to systems relying on fixed antenna positions and antenna selection but also demonstrates remarkable robustness to RCS fluctuations, underscoring the multifaceted benefits of exploiting MAs in ISAC systems.

In this paper, we investigate the joint resource allocation and trajectory design for a multi-user, multi-target unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) system, where the link capacity between a ground base station (BS) and the UAV is limited. The UAV conducts target sensing and information transmission in orthogonal time slots to prevent interference. As is common in practical systems, sensing is performed while the UAV hovers, allowing the UAV to acquire high-quality sensing data. Subsequently, the acquired sensing data is offloaded to the ground BS for further processing. We jointly optimize the UAV trajectory, UAV velocity, beamforming for the communication users, power allocated to the sensing beam, and time of hovering for sensing to minimize the power consumption of the UAV while ensuring the communication quality of service (QoS) and successful sensing. Due to the prohibitively high complexity of the resulting non-convex mixed integer non-linear program (MINLP), we employ a series of transformations and optimization techniques, including semidefinite relaxation, big-M method, penalty approach, and successive convex approximation, to obtain a low-complexity suboptimal solution. Our simulation results reveal that 1) the proposed design achieves significant power savings compared to two baseline schemes; 2) stricter sensing requirements lead to longer sensing times, highlighting the challenge of efficiently managing both sensing accuracy and sensing time; 3) the optimized trajectory design ensures precise hovering directly above the targets during sensing, enhancing sensing quality and enabling the application of energy-focused beams; and 4) the proposed trajectory design balances the capacity of the backhaul link and the downlink rate of the communication users.

This paper considers the effective power in downlink a multi-antenna, multi-user single-cell network enabled with simultaneous wireless information and power transfer (SWIPT). The proposed power efficiency problem aims to maximize the harvested energy and minimize transmission power consumption simultaneously. Specifically, the beamforming and antenna selection procedures at the receivers are optimized under minimum data rate requirements. The underlying optimization problem is shown to be an intractable nonlinear programming problem. As a result, a joint beamforming design and antenna selection is performed based on the scheduling chosen for information decoding and energy harvesting. The main problem is decomposed into two subproblems: antenna selection and beamforming, which yields a locally optimal solution. The first subproblem is solved based on the maximum channel gain across all antennas. While the second subproblem is solved via a two-layer iterative structure based on the sum of ratio programming. Simulation results show that the proposed scheme not only improves power efficiency but also enhances energy efficiency. The results also unveil an interesting tradeoff between power and energy efficiency.

In this paper, we investigate joint resource allocation and trajectory design for multi-user multi-target unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC). To be compatible with practical UAV-based sensing systems, sensing is carried out while the UAV hovers. In particular, we jointly optimize the two-dimensional trajectory, the velocity, and the downlink information and sensing beamformers of a fixed-altitude UAV for minimization of the average power consumption, while ensuring the quality of service of the communication users and the sensing tasks. To tackle the resulting non-convex mixed integer non-linear program (MINLP), we exploit semidefinite relaxation, the big-M method, and successive convex approximation to develop an alternating optimization-based algorithm. Our simulation results demonstrate the significant power savings enabled by the proposed scheme compared to two baseline schemes employing heuristic trajectories.

In this paper, we design a new flexible smart software-defined radio access network (Soft-RAN) architecture with traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation model for the proposed smart soft-RAN model where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation procedures to intelligently perform decision-making. In this paper, our aim is to make the network more scalable and more flexible in terms of conflicting performance indicators such as achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, i.e, throughput-overhead-complexity (TOC), for the proposed machine learning-based algorithm, which supports a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL) which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic (SAC) method which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in recent state-of-the-art network designs.

This paper focuses on leveraging a mobile edge computing (MEC) server at an access point (AP) to address the delay and reliability sensitivity requirement of multi-user machine-type communication (MTC). By offloading tasks to the MEC server, latency for low-power MTC devices can be minimized. Meanwhile, intelligent reflecting surfaces (IRSs) are supported to facilitate robust offloading, enhance spectrum efficiency, and improve coverage by influencing incident radio-frequency wave propagation via modifying the phase shifts with passive reflecting components. Therefore, we investigate joint radio resource allocation and edge offloading decision optimization in a multi-user IRS-assisted MEC network, wherein a multi-antenna AP receives information symbols from a set of Internet of Things (IoT) users with short packet transmission. In particular, we minimize the system’s power utilization subject to offloading MTC-enabled IoT users’ quality of service (QoS) requirements, transmit power feasibility, capacity limitation, and IRS phase shift. The non-convex nature of the formulated problem poses a challenge to solving it effectively. To address this, we propose an efficient iterative algorithm based on successive convex approximation (SCA) and a penalty-based approach for handling unit-modulus constraints in the presence of passive reflecting elements at the IRS. Simulation results demonstrate the superior performance of our algorithm compared to other baseline schemes.

We consider a relay system empowered by an unmanned aerial vehicle (UAV) that facilitates downlink information delivery while adhering to finite blocklength requirements. The setup involves a remote controller transmitting information to both a UAV and an industrial Internet of Things (IIoT) or remote device, employing the non-orthogonal multiple access (NOMA) technique in the first phase. Subsequently, the UAV decodes and forwards this information to the remote device in the second phase. Our primary objective is to minimize the decoding error probability (DEP) at the remote device, which is influenced by the DEP at the UAV. To achieve this goal, we optimize the blocklength, transmission power, and location of the UAV. However, the underlying problem is highly non-convex and generally intractable to be solved directly. To overcome this challenge, we adopt an alternative optimization (AO) approach and decompose the original problem into three sub-problems. This approach leads to a sub-optimal solution, which effectively mitigates the non-convexity issue. In our simulations, we compare the performance of our proposed algorithm with baseline schemes. The results reveal that the proposed framework outperforms the baseline schemes, demonstrating its superiority in achieving lower DEP at the remote device. Furthermore, the simulation results illustrate the rapid convergence of our proposed algorithm, indicating its efficiency and effectiveness in solving the optimization problem.

In this paper, the downlink transmission of a two-tier heterogeneous network (HetNet) is considered in which a macro base station (MBS) serves the macro users using orthogonal frequency division multiple access (OFDMA) and small base stations (SBSs) serve the small-cell users through multi-carrier non-orthogonal multiple access (MC-NOMA) and joint transmission (JT). In particular, assuming the subcarriers are already allocated to macro users, the problem of scheduling (i.e., joint user association and subcarrier allocation) and power control is studied with the goal of maximizing the total users’ perceived quality-of -experience (QoE) for small-cell users, while a minimum data rate for macro users is guaranteed. To solve the joint optimization problem, a near-optimal and computationally efficient two-phase solution approach is proposed based on the tools from optimization and machine learning (ML). In the first phase, the optimization problem is solved to obtain the scheduling decisions and transmit power variables. In the second phase, the optimized scheduling decisions and transmit power variables serve as training samples for an AlexNet classifier and support vector regressor (SVR), respectively. Simulation results reveal that the integration of JT into MC-NOMA, outperforms the conventional MC-NOMA scheme by up to 24%, 19%, and 21% for the web, video and audio multimedia services, respectively. Compared to a conventional convolutional neural network, our results demonstrate that for the web, video, and audio-services, AlexNet increases the scheduling prediction accuracy up to 14%, 11%, and 17%, while SVR increases the power prediction accuracy up to 8%, 7%, and 12%, respectively.

Abstract This paper investigates the resource allocation design in device‐to‐device (D2D) communication underlying cellular networks, which is assisted by multiple intelligent reflecting surfaces (IRSs) deployed at the cell boundary to enhance desired signals and mitigate interference between D2D pairs and CUs. In this regard, a multi‐objective optimization problem (MOOP) framework is formulated to jointly maximize the downlink sum‐rate of the D2D pairs and cellular users (CUs). To doing so, the MOOP is first converted into a single‐objective optimization problem (SOOP) by invoking the weighted sum method. Next, to facilitate the high‐coupled non‐convex SOOP formulation, it is decomposed into two sub‐problems through the alternative optimization method. For the first sub‐problem, the inner approximation and successive convex approximations are adopted for jointly allocating D2D transmission power, subcarrier assignment, and transmit beamforming matrix at the BS. For the second sub‐problem, an iterative algorithm based on the penalty‐based method as well as Fenchel's duality approach is adopted to design the passive beamforming matrices at IRSs, which is guaranteed to rapidly converge to a stationary point. Simulation results reveal that a non‐trivial trade‐off between the total throughput of CUs and D2D pairs exist. Furthermore, the proposed framework significantly outperforms existing works addressed in the literature.

Abstract In this paper, a downlink multi‐user communication of an intelligent reflecting surface (IRS)‐assisted multiple‐input single‐output (MISO) power‐domain non‐orthogonal multiple access (NOMA) system is investigated. Considering multi‐carrier (MC) transmission and to enhance user fairness, two users are assigned to the same subcarrier. For such a system, the authors optimize active beamforming at the base station (BS), subcarrier allocation policy, and phase shifts at the IRS to maximize the system throughput. A semi‐definite relaxation (SDR) is applied to tackle the non‐convex optimization problem, and an alternating optimization (AO) algorithm is proposed to obtain a suboptimal solution. Numerical results illustrate the higher throughput of the proposed MC multi‐user IRS‐aided MISO‐NOMA system as compared to the conventional IRS‐assisted orthogonal multiple access (OMA) system.

This article proposes a novel approach to improve the energy efficiency (EE) of an energy-harvesting (EH)-enabled IoT network supported by simultaneous wireless information and power transfer (SWIPT). More specifically, the device-to-device (D2D) users harvest ambient energy throughout their communication with the time switching (TS) technique, while the Internet of Things (IoT) users harvest energy from the base station (BS) based on the power splitting (PS) method. We study the EE optimization problem that takes into account transmit power feasibility conditions for D2D users and IoT users, the minimum data rate requirements for D2D and IoT users, joint spectrum sharing block, and time allocation for the D2D links. The underlying problem is a highly nonconvex mixed-integer nonlinear problem (MINLP) and the global optimal solution is intractable. To handle it, we decompose the original problem into three subproblems: 1) joint subchannel allocation and PS; 2) power control; and 3) time allocation. Since it is difficult to find an exact state model approach in a dynamic environment with a large state space, we exploit a$Q$-learning method based on reinforcement learning (RL) to solve the first subproblem. To solve the second subproblem, we apply a conventional convex optimization technique based on the majorization–minimization (MM) approach and Dinkelbach method. Simulation results not only demonstrate the superiority of our proposed algorithm as compared to other methods in the literature but also confirm impressive EE gains through spectrum sharing and harvested energy from D2D and IoT users.

The roll-out of various emerging wireless services has triggered the need for the sixth-generation (6G) wireless networks to provide functions of target sensing, intelligent computing and information communication over the same radio spectrum. In this paper, we provide a unified framework integrating sensing, computing, and communication to optimize limited system resource for 6G wireless networks. In particular, two typical joint beamforming design algorithms are derived based on multi-objective optimization problems (MOOP) with the goals of the weighted overall performance maximization and the total transmit power minimization, respectively. Extensive simulation results validate the effectiveness of the proposed algorithms. Moreover, the impacts of key system parameters are revealed to provide useful insights for the design of integrated sensing, computing, and communication (ISCC).

This paper proposes a novel approach to improve the performance of a heterogeneous network (HetNet) supported by dual connectivity (DC) by adopting multiple unmanned aerial vehicles (UAVs) as passive relays that carry reconfigurable intelligent surfaces (RISs). More specifically, RISs are deployed under the UAVs termed as UAVs-RISs that operate over the micro-wave ($\mu \text{W}$) channel in the sky to sustain a strong line-of-sight (LoS) connection with the ground users. The macro-cell operates over the$\mu \text{W}$channel based on orthogonal multiple access (OMA), while small base stations (SBSs) operate over the millimeter-wave (mmW) channel based on non-orthogonal multiple access (NOMA). We study the problem of total transmit power minimization by jointly optimizing the trajectory/velocity of each UAV, RISs’ phase shifts, subcarrier allocations, and active beamformers at each BS. The underlying problem is highly non-convex and the global optimal solution is intractable. To handle it, we decompose the original problem into two subproblems, i.e., a subproblem which deals with the UAVs’ trajectories/velocities, RISs’ phase shifts, and subcarrier allocations for$\mu \text{W}$; and a subproblem for active beamforming design and subcarrier allocation for mmW. In particular, we solve the first subproblem via the dueling deep Q-Network (DQN) learning approach by developing a distributed algorithm which leads to a better policy evaluation. Then, we solve the active beamforming design and subcarrier allocation for the mmW via the successive convex approximation (SCA) method. Simulation results exhibit the effectiveness of the proposed resource allocation scheme compared to other baseline schemes. In particular, it is revealed that by deploying UAVs-RISs, the transmit power can be reduced by 6 dBm while maintaining similar guaranteed QoS.

In this paper, we develop a resource allocation framework to optimize the downlink transmission of a backhaul-aware multi-cell cognitive radio network (CRN) which is enabled with multi-carrier non-orthogonal multiple access (MC-NOMA). The considered CRN is composed of a single macro base station (MBS) and multiple small BSs (SBSs) that are referred to as the primary and secondary tiers, respectively. For the primary tier, we consider orthogonal frequency division multiple access (OFDMA) scheme and also Quality of Service (QoS) to evaluate the user satisfaction. On the other hand in secondary tier, MCNOMA is employed and the user satisfaction for web, video and audio as popular multimedia services is evaluated by Quality-of-Experience (QoE). Furthermore, each user in secondary tier can be served simultaneously by multiple SBSs over a subcarrier via Joint Transmission (JT). In particular, we formulate a joint optimization problem of power control and scheduling (i.e., user association and subcarrier allocation) in secondary tier to maximize total achievable QoE for the secondary users. An efficient resource allocation mechanism has been developed to handle the non-linear form interference and to overcome the non-convexity of QoE serving functions. The scheduling and power control policy leverage on Augmented Lagrangian Method (ALM). Simulation results reveal that proposed solution approach can control the interference and JT-NOMA improves total perceived QoE compared to the existing schemes.

In this paper, we formulate two multi-objective optimization problems (MOOPs) in orthogonal frequency-division multiple access (OFDMA)-based in-band full-duplex (IBFD) wireless communications. The aim of this study is to exploit the performance trade-off between uplink and downlink where a wireless radio simultaneously transmits and receives in the same frequency. We consider maximizing the system throughput as the first MOOP and minimizing the system aggregate power consumption as the second MOOP between uplink and downlink, while taking into account the impact of self-interference (SI) and quality of service provisioning. We study the throughput and the transmit power trade-off between uplink and downlink via solving these two problems. Each MOOP is a nonconvex mixed integer non-linear programming (MINLP) which is generally intractable. In order to circumvent this difficulty, a penalty function is introduced to reformulate the problem into a mathematically tractable form. Subsequently, each MOOP is transformed into a single-objective optimization problem (SOOP) via the weighted Tchebycheff method which is addressed by majorization-minimization (MM) approach. Simulation results demonstrate an interesting trade-off between the considered competing objectives.

In this paper, the problem of joint sub-channel assignment, antenna selection, and power control in the downlink (DL) of an orthogonal frequency division multiple access (OFDMA) femtocell network is investigated in order to maximize system throughput. This problem, due to its discrete nature and the interference, is modeled as a mixed integer non-linear programming (MINLP) optimization problem, which is complicated to solve. To deal with this complexity, the problem is decomposed into three subproblems: 1) sub-channel assignment, 2) antenna selection, and 3) power control. We then present an iterative algorithm for these subproblems resulting in an optimized network throughput. The superiority of our proposed method compared to other existing works is demonstrated through simulation results.

In this paper, we investigate the joint resource allocation and antenna selection algorithm design for uplink orthogonal frequency division multiple access (OFDMA) communication system. We propose a multi-objective optimization framework to strike a balance between spectral efficiency (SE) and energy efficiency (EE). The resource allocation design is formulated as a multi-objective optimization problem (MOOP), where the conflicting objective functions are linearly combined into a single objective function employing the weighted sum method. In order to develop an efficient solution, the majorization minimization (MM) approach is proposed where a surrogate function serves as a lower bound of the objective function. Then an iterative suboptimal algorithm is proposed to maximize the approximate objective function. Numerical results unveil an interesting tradeoff between the considered conflicting system design objectives and reveal the improved EE and SE facilitated by the proposed transmit antenna selection in OFDMA systems.

This paper aims at investigating the problem of energy efficiency (EE) maximization for uplink multi-cell networks via a joint design of sub-channel assignment, power control, and antenna selection. We study the problem under two practical scenarios. In the first scenario, known as conventional antenna selection (CAS), there is only one radio frequency (RF) chain available at the mobile user and all the sub-channels for each user can be assigned to one of the antennas. For the second scenario, known as generalized antenna selection (GAS), the number of RF chains is equal to the number of antennas and the messages of each user can transmit over its assigned sub-channels via different antennas. The resource allocation design is formulated as a multi-objective optimization problem (MOOP) and then converted into a single objective optimization problem (SOOP) via the weighted Tchebycheff method. The considered problem is a mixed integer nonlinear programming (MINLP) which is generally intractable. To address this problem, a penalty function is introduced to handle the binary variable constraints. In order to obtain a computationally efficient suboptimal solution, the majorization minimization (MM) approach is proposed where a surrogate function serves as the lower bound of the objective function. Furthermore, we propose another low-complexity practical algorithm to further reduce the computational cost. Simulation results demonstrate the superiority of the proposed method and unveil an interesting trade-off between EE and SE for two considered scenarios.

The problem of joint power and sub-channel allocation to maximize energy efficiency (EE) and spectral efficiency (SE) simultaneously in in-band full-duplex (IBFD) orthogonal frequency-division multiple access (OFDMA) network is addressed considering users' QoS in both uplink and downlink. The resulting optimization problem is a non-convex mixed integer non-linear program (MINLP) which is generally difficult to solve. In order to strike a balance between the EE and SE, we restate this problem as a multi-objective optimization problem (MOOP) which aims at maximizing system's throughput and minimizing system's power consumption, simultaneously. To this end, the ε-constraint method is adopted to transform the MOOP into single objective optimization problem (SOOP). The underlying problem is solved via an efficient solution based on the majorization minimization (MM) approach. Furthermore, in order to handle binary subchannel allocation variable constraints, a penalty function is introduced. Simulation results unveil interesting tradeoffs between EE and SE.