Transmit Power Budget Research Articles

Optimized design for integrated sensing and communication in secure MIMO SWIPT systems

This paper investigates a secure multiple-input multiple-output integrated sensing and communication (MIMO ISAC)-enabled untrusted simultaneous wireless information and power transfer (SWIPT) system in which a base station is concurrently responsible for three different functionalities of communicating with users, sensing targets, and powering energy harvesting (EH) devices. These EH devices are assumed to be untrusted and potentially wiretap the information intended for legitimate users. This paper considers three different system design strategies: (i) communication-centric design, (ii) sensing-centric design, and (iii) EH-centric design. In each strategy, we aim to jointly design the transmit communication precoders and sensing precoder (which also acts as both the energy beam for EH and the artificial noise for securing communication) subject to requirements corresponding to the transmit power budget, minimum achievable secrecy rate, minimum amount of harvested energy, and maximum beampattern similarity constraint. The optimization design problems are highly non-convex, and thus it is mathematically challenging to directly find the optimal solutions. To overcome this issue, we first derive convex inner approximations, and then utilize the sequential convex programming (SCP) approach to develop efficient iterative algorithms which converge at least to a locally optimal solution. Extensive numerical results are simulated to verify the optimality of developed algorithms and highlight the impact of sensing on the system secrecy rate and harvested energy.

Trajectory Optimization and Power Allocation Scheme for a UAV Relay-aided Network in the Presence of an Eavesdropper

The information theoretical security for a cellular network in the presence of an eavesdropper is investigated in this research. The network is single-input-single-output (SISO) in nature. A small unmanned aerial vehicle (UAV) is aiding the network as a relay that follows the decode-and-forward (DF) protocol. The relay decodes the transmitted signal and retransmits it to the destination while repositioning itself if required. The allotted power of the UAV may not be enough for long-distance and long-duration travel. This article deals with the power needed for the data transmission so that the UAV can operate as a relay with less transmit power. However, the confidential data transmission between a base station and a mobile device is being intercepted by a passive eavesdropper. The security issue affects the transmit power and the outage situation. The theory of physical layer security is employed to ensure a secure wireless transmission. The secrecy parameters, namely, the secrecy capacity and the secrecy outage probability are investigated via mathematical derivations and computer programming. Additionally, optimizing the trajectory and allocation of the transmit power budget of the UAV will increase the network’s reliability. Our results show that the UAV relay can handle a secure transmission with its limited resources if a budget power allocation can be achieved along with an optimized trajectory.

Secure Active Intelligent Reflecting Surface Communication against Colluding Eavesdroppers

An active intelligent reflecting surface (IRS)-assisted, secure, multiple-input–single-output communication method is proposed in this paper. In this proposed scheme, a practical and unfavorable propagation environment is considered by assuming that multiple colluding eavesdroppers (Eves) coexist. In this case, we jointly optimize the beamformers of the base station (BS) and the active IRS for the formulated sum secrecy rate (SSR) maximization problem. Because the formulated problem is not convex, we apply the alternating optimization method to optimize the beamformers for maximizing the SSR. Specifically, we use the semi-definite relaxation method to solve the sub-problem of the beamforming vector of the BS, and we use the successive convex approximation method to solve the sub-problem of the power amplification matrix of the active IRS. Based on the solutions obtained using these stated methods, numerical results show that deploying an active IRS is superior compared to the cases of a passive IRS and a non-IRS for improving the physical layer security of wireless communication with multiple colluding Eves under different settings, such as the numbers of users, Eves, reflecting elements, and BS antennas as well as the maximum transmit power budget at the BS.

Energy-Efficient Resource Optimization for IRS-Assisted VLC-Enabled Offshore Communication System

In this paper, a downlink energy efficiency maximization problem is investigated in an intelligent reflective surface (IRS)-assisted visible light communication system. In order to extend wireless communication coverage of the onshore base station, an IRS mounted on a unmanned aerial vehicle (UAV) is introduced to assist an onshore lighthouse with simultaneously providing remote ship users wireless communication services and illumination. Aiming to maximizing the energy efficiency of the proposed system, a resource allocation problem is formulated as the ratio of the achievable system sum rate to the total power consumption under the constraints of the user’s data requirement and transmit power budget. Due to the non-convexity of the proposed problem, the Dinkelbach method and mean-square error (MSE) method are adopted to turn the non-convex origin problem into two equivalent problems, namely transmit beamforming and reflected phase shifting. The Lagrangian method and semidefinite relaxation technique are used to obtain the closed-form solutions of these two subproblems. Accordingly, an alternative optimization-based resource allocation scheme is proposed to obtain the optimal system energy efficiency. The simulation results show that the proposed scheme performs better in terms of energy efficiency over benchmark schemes.

Low-power timely random access: Packet-based or connection-based?

This paper studies low-power random access protocols for timely status update systems with information freshness requirements, measured by age of information (AoI). A fundamental challenge for such networks is to schedule a large number of transmitters to access the wireless channel in a way that achieves a low network-wide AoI at low power consumption. Conventional packet-based random access protocols involve transmitters contending for the channel by sending their entire data packets. When packets are of long duration, the time and energy wasted due to packet collisions is considerable. In contrast, connection-based random access protocols establish connections with the receiver before transmitting data packets. From an information freshness perspective, there should be conditions that favor one approach over the other. Therefore, we conduct a comparative study of the average AoI of packet-based and connection-based random access protocols. Specifically, we consider frame slotted Aloha (FSA) as a representative of packet-based random access and design a request-then-access (RTA) protocol for connection-based random access. Our analyses indicate that the choice between packet-based and connection-based protocols depends mainly on the payload size of update packets and the transmit power budget. In particular, RTA significantly reduces AoI and saves power, especially when the payload size is large. Overall, our investigation offers insights into the practical design of random access protocols for low-power timely status update systems.

Joint Trajectory Design and Resource Optimization in UAV-Assisted Caching-Enabled Networks with Finite Blocklength Transmissions

In this study, we design and analyze a reliability-oriented downlink wireless network assisted by unmanned aerial vehicles (UAVs). This network employs non-orthogonal multiple access (NOMA) transmission and finite blocklength (FBL) codes. In the network, ground user equipments (GUEs) request content from a remote base station (BS), and there are no direct connections between the BS and the GUEs. To address this, we employ a UAV with a limited caching capacity to assist the BS in completing the communication. The UAV can either request uncached content from the BS and then serve the GUEs or directly transmit cached content to the GUEs. In this paper, we first introduce the decoding error rate within the FBL regime and explore caching policies for the UAV. Subsequently, we formulate an optimization problem aimed at minimizing the average maximum end-to-end decoding error rate across all GUEs while considering the coding length and maximum UAV transmission power constraints. We propose a two-step alternating optimization scheme embedded within a deep deterministic policy gradient (DDPG) algorithm to jointly determine the UAV trajectory and transmission power allocations, as well as blocklength of downloading phase, and our numerical results show that the combined learning-optimization algorithm efficiently addresses the considered problem. In particular, it is shown that a well-designed UAV trajectory, relaxing the FBL constraint, increasing the cache size, and providing a higher UAV transmission power budget all lead to improved performance.

Neighbor-based joint spatial division and multiplexing in massive MIMO: user scheduling and dynamic beam allocation

AbstractTwo-stage precoding schemes have been developed to reduce the channel estimation overhead in FDD systems. By integrating user scheduling into these schemes, it becomes possible to meet the quality-of-service requirements of high-density wireless communication systems, despite the limitations on spatial resources and transmit power budget. In this paper, we propose a user scheduling and dynamic beam allocation method for neighbor-based joint spatial division multiplexing (N-JSDM) transmission. The user scheduling problem is formulated as a 0–1 quadratic programming problem to maximize effective spectral efficiency (ESE) using directional channel properties. To address the complexity issue, convex relaxation and linearization methods are employed to transform the problem into a 0–1 mixed integer linear programming, and a dimensionality reduction method is introduced. The proposed user scheduling-aided N-JSDM scheme reduces downlink training length and feedback of channel state information. Additionally, a dynamic configuration form is used for pre-beamforming matrix design based on user distribution, outperforming conventional approaches. Simulation results demonstrate higher ESE achieved by the proposed scheme compared to previous methods.

UAV Relay Energy Consumption Minimization in an MEC-Assisted Marine Data Collection System

Recently, unmanned aerial vehicle (UAV)-assisted maritime communication systems have drawn considerable attention due to their potential for broadband maritime communication applications. However, their limited energy resources remains a critical issue in providing long-term data transmission support for maritime applications. In this study, an integrated sea–air–terrestrial communication system was constructed for marine data collection, where several unmanned surface vessels (USVs) are deployed to collect marine data from underwater sensors (UWSs), and a UAV hovers above these USVs as a relay node, transmitting marine data from USVs to an onshore base station (BS). To prolong the lifetime of the UAV relay, mobile edge computing technology is applied in USVs for partial data computing, which reduces the to-be-relayed data volume from UAVs to USVs to onshore BS as well as the relay energy consumption of UAV. A parallel data computing and transmission scheme was developed for simultaneous local data computing and relaying in the proposed system. Accordingly, a UAV energy consumption minimization problem was formulated with constraints on the USV’s computational ability, the USV’s transmission power budget, the UAV transmission power budget, and the maximum system latency. To effectively solve this nonconvex optimal problem, an energy optimal partial data computing and relaying strategy was constructed by successively optimizing the data partial computational offloading ratio, USV transmit power allocation, and UAV transmit power. Numerical simulations were used to verify the effectiveness of the proposed strategy.

Energy Efficiency Optimization for a V2X Network with NOMA-CoMP Enabled

With the increase in user density, it is a challenging problem to achieve higher energy efficiency in vehicle-to-everything (V2X) networks, where frequent handovers occur as users continuously pass through dense cell boundaries. In this paper, we investigate this energy efficiency problem in a highly dense V2X network. To alleviate inter-cell interference and reduce unnecessary handovers, we propose a user-centric network architecture and utilize the coordinated multiple point (CoMP) transmission scheme to enable multiple roadside access points (APs) simultaneously to service one mobile vehicle. Moreover, non-orthogonal multiple access (NOMA) is adopted to enable multiple users to multiplex within one sub-channel and improve system spectrum reuse. Accordingly, the energy efficiency optimization problem is formulated as system achievable rate per joule, with the constraints of the AP transmit power budget, vehicle minimum rate requirements, and the maximum number of CoMP APs serving each vehicle. To solve the optimization problem, a semi-dynamic CoMP AP clustering algorithm is proposed to select a group of CoMP APs to serve a vehicle user by considering the vehicle–AP channel coefficients and the vehicle user’s achievable energy efficiency. Then, with the selected CoMP AP clusters, a bidirectional selection algorithm is applied via NOMA to select multiple vehicle users multiplexing within one sub-channel and perform sub-channel power allocation. Numerical simulations are presented to verify the effectiveness of the proposed algorithms, and the results show the improvements offered by the proposed algorithms compared to current benchmarks.

Robust Beamforming for IRS-Aided SWIPT in Cognitive Satellite and Terrestrial Networks

This paper proposes a robust beamforming (BF) scheme for intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) in a cognitive satellite and terrestrial network (CSTN). The satellite network serves multiple earth stations through the multicast transmission, while the terrestrial network operating at the same spectrum implements the SWIPT through IRS-aided multicast technology. Assuming that the imperfect channel state information (CSI) is available, we formulate an optimization problem to maximize the minimum achievable rate of the information receivers (IRs), subject to the transmit power budget, achievable rate and harvesting energy requirements. To address this nonconvex problem, we propose a tight bound robust BF algorithm based on Lagrange duality and alternating optimization (AO) to jointly optimize the active and passive beamformers for the base station, satellite and IRS, respectively. Simulation results confirm the robustness and superiority of our proposed BF scheme over other related works.

Robust Downlink Transmission Design in IRS-Assisted Cognitive Satellite and Terrestrial Networks

Cognitive satellite and terrestrial network (CSTN) is considered as a promising technology to provide ubiquitous connectivity for various users within wide-coverage. This paper proposes a robust downlink transmission scheme for multiple intelligent reflecting surfaces (IRSs) assisted CSTN. Here, the satellite network adopts multigroup multicast transmission scheme to serve many earth stations, while the terrestrial network exploits space division multiple access and multi-IRS-enhanced non-orthogonal multiple access technology to communicate with many terrestrial users. By assuming that these two networks share the same frequency band having only the angular information based imperfect channel state information of each user, we formulate an optimization problem to minimize the total transmit power subject to the constraints of quality-of-service requirement for each user, per-antenna transmit power budgets of satellite and BS, and unit-modulus requirement for each reflecting element. To tackle this mathematically intractable problem, we then employ angular discretization together with the successive convex approximation method to obtain the active beamforming (BF) vectors of satellite and BS, the passive BF vector of IRS, and the power allocation coefficients. Moreover, we propose a generalized zero forcing BF and alternative optimization to obtain the suboptimal solutions of the optimization problem with low computational complexity. Finally, simulation results are given to demonstrate the effectiveness and superiority of the proposed two schemes over the benchmarks.

Polar Coded Integrated Data and Energy Networking: A Deep Neural Network Assisted End-to-End Design

Wireless sensors are everywhere. To address their energy supply, we proposed an end-to-end design for polar-coded integrated data and energy networking (IDEN), where the conventional signal processing modules, such as modulation/demodulation and channel decoding, are replaced by deep neural networks (DNNs). Moreover, the input-output relationship of an energy harvester (EH) is also modelled by a DNN. By jointly optimising both the transmitter and the receiver as an autoencoder (AE), we minimize the bit-error-rate (BER) and maximize the harvested energy of the IDEN system, while satisfying the transmit power budget constraint determined by the normalization layer in the transmitter. Our simulation results demonstrate that the DNN aided end-to-end design conceived outperforms its conventional model-based counterpart both in terms of the harvested energy and the BER.

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.

DRL-Based Resource Allocation in Remote State Estimation

Remote state estimation where sensors send their measurements of distributed dynamic plants to a remote estimator over shared wireless resources is essential for mission-critical applications of Industry 4.0. Existing algorithms on dynamic radio resource allocation for remote estimation systems assumed oversimplified wireless communications models and can only work for small-scale settings. In this work, we consider remote estimation systems with practical wireless models over the orthogonal multiple-access and non-orthogonal multiple-access schemes. We derive necessary and sufficient conditions under which remote estimation systems can be stabilized. The conditions are described in terms of the transmission power budget, channel statistics, and plants’ parameters. For each multiple-access scheme, we formulate a novel dynamic resource allocation problem as a decision-making problem for achieving the minimum overall long-term average estimation mean-square error. Both the estimation quality and the channel quality states are taken into account for decision making. We systematically investigated the problems under different multiple-access schemes with large discrete, hybrid discrete-and-continuous, and continuous action spaces, respectively. We propose novel action-space compression methods and develop advanced deep reinforcement learning algorithms to solve the problems. Numerical results show that our algorithms solve the resource allocation problems effectively and provide much better scalability than the literature.

QoS-Aware User Scheduling in Crowded XL-MIMO Systems Under Non-Stationary Multi-State LoS/NLoS Channels

Ensuring that the quality-of-service (QoS) requirements are satisfied in wireless communications systems with high user density is challenging due to the limitations on the transmit power budget and the number of resource blocks. In this paper, we propose a QoS-aware joint user scheduling and power allocation technique to enhance the number of served users in the downlink of crowded extra-large scale massive multiple-input multiple-output (XL-MIMO) with minimum QoS requirements guarantee. The proposed technique is constituted by two sequential procedures: the clique search-based scheduling (CBS) algorithm for user scheduling followed by optimal power allocation with transmit power budget and minimum achievable rate per user constraints.We propose a generalized non-stationary multi-state channel model based on spherical wave propagation assuming that users under line-of-sight (LoS) and non-LoS (NLoS) transmission coexist in the same communication cell. This is done to accurately evaluate the proposed technique in realistic XL-MIMO scenarios. Numerical results reveal that the proposed CBS algorithm provides fair coverage over the whole cell area, achieving remarkable numbers of scheduled users with satisfied QoS requirements when users under the LoS and NLoS channel states coexist in the communication cell.

Deep Reinforcement Learning for Practical Phase-Shift Optimization in RIS-Aided MISO URLLC Systems

We study the joint active/passive beamforming and channel blocklength (CBL) allocation in a non-ideal reconfigurable intelligent surface (RIS)-aided ultra-reliable and low-latency communication (URLLC) system. The considered scenario is a finite blocklength (FBL) regime and the problem is solved by leveraging a deep reinforcement learning (DRL) algorithm named twin-delayed deep deterministic policy gradient (TD3). First, assuming an industrial automation system, the signal-to-interference-plus-noise ratio and achievable rate in the FBL regime are identified for each actuator. Next, the joint active/passive beamforming and CBL optimization problem is formulated where the objective is to maximize the total achievable FBL rate in all actuators, subject to non-linear amplitude response at the RIS elements, BS transmit power budget and total available CBL. Since the formulated problem is highly non-convex and non-linear, we resort to employing an actor-critic policy gradient DRL algorithm based on TD3. The considered method relies on interacting RIS with the industrial automation environment by taking actions which are the phase shifts at the RIS elements, CBL variables, and BS beamforming to maximize the expected observed reward, i.e., the total FBL rate. We assess the performance loss of the system when the RIS is non-ideal, i.e., with non-linear amplitude response, and compare it with ideal RIS without impairments. The numerical results show that optimizing the RIS phase shifts, BS beamforming, and CBL variables via the TD3 method with deterministic policy outperforms conventional methods and it is highly beneficial for improving the network total FBL rate considering finite CBL size.

Energy Efficient Train-Ground mmWave Mobile Relay System for High Speed Railways

The rapid development of high-speed railways (HSRs) puts forward high requirements on the corresponding communication system. Millimeter wave (mmWave) can be a promising solution due to its wide bandwidth, narrow beams, and rich spectrum resources. However, with the large number of antenna elements employed, energy-efficient solutions at mmWave frequencies are in great demand. Based on a mmWave HSR communication system with multiple mobile relays (MRs) on top of the train, a dynamic power-control scheme for train-ground communications is proposed. The scheme follows the regular movement characteristics of high-speed trains and considers three phases of train movement: the train enters the cell, all MRs are covered in the cell, and the train leaves the cell. The transmit power is further refined according to the number of MRs in the cell and the distance between the train and the remote radio head. By minimizing energy consumption under the constraints of the transmitted data and transmit power budget, the transmit power is allocated to multiple MRs through the multiplier punitive function-based algorithm. Comprehensive simulation results, where the velocity estimation error is taken into account, are provided to demonstrate the effectiveness of the proposed scheme over several baseline schemes.

Model Compression via Pattern Shared Sparsification in Analog Federated Learning Under Communication Constraints

Recently, it has been shown that analog transmission based federated learning enables more efficient usage of communication resources compared to the conventional digital transmission. In this paper, we propose an effective model compression strategy enabling analog FL under constrained communication bandwidth. To this end, the proposed approach is based on pattern shared sparsification by setting the same sparsification pattern of parameter vectors uploaded by edge devices, as opposed to each edge device independently applying sparsification. In particular, we propose specific schemes for determining the sparsification pattern and characterize the convergence of analog FL leveraging these proposed sparsification strategies, by deriving a closed-form upper boun d of convergence rate and residual error. The closed-form expression allows to capture the effect of communication bandwidth and power budget to the performance of analog FL. In terms of convergence analysis, the model parameter obtained with the proposed schemes is proven to converge to the optimum of model parameter. Numerical results show that leveraging the proposed pattern shared sparsification consistently improves the performance of analog FL in various settings of system parameters. The improvement in performance is more significant under scarce communication bandwidth and limited transmit power budget.

Energy-Efficiency Optimization for D2D Communications Underlaying UAV-Assisted Industrial IoT Networks With SWIPT

The Industrial Internet of Things (IIoT) has been viewed as a typical application for the fifth generation (5G) mobile networks. This article investigates the energy efficiency (EE) optimization problem for the Device-to-Device (D2D) communications underlaying unmanned aerial vehicles (UAVs)-assisted IIoT networks with simultaneous wireless information and power transfer (SWIPT). We aim to maximize the EE of the system while satisfying the constraints of transmission rate and transmission power budget. However, the designed EE optimization problem is nonconvex involving joint optimization of the UAV’s location, beam pattern, power control, and time scheduling, which is difficult to tackle directly. To solve this problem, we present a joint UAV location and resource allocation algorithm to decouple the original problem into several subproblems and solve them sequentially. Specifically, we first apply the Dinkelbach method to transform the fraction problem to a subtractive-form one and propose a mulitiobjective evolutionary algorithm based on decomposition (MOEA/D)-based algorithm to optimize the beam pattern. We then optimize UAV’s location and power control using the successive convex optimization techniques. Finally, after solving the above variables, the original problem can be transformed into a single-variable problem with respect to the charging time, which is linear and can be tackled directly. Numerical results verify that significant EE gain can be obtained by our proposed algorithm as compared to the benchmark schemes.

IRS Empowered Robust Secure Transmission for Integrated Satellite-Terrestrial Networks

In this letter, we investigate an intelligent reflecting surface (IRS) empowered robust secure transmission approach for an integrated satellite and terrestrial network (ISTN) with multiple eavesdroppers. The satellite transmits multicast signals to earth stations (ESs) within its coverage, while the base station (BS) serves many terrestrial users (TUs) with the aid of IRS. To improve the security of the satellite system, by assuming that only imperfect channel state information is available, we formulate a weighted sum rate maximization problem subject to the intercept probability constraints, the quality-of-service requirement of each ES and TU, and the transmit power budget of the satellite platform and BS. To tackle this non-convex problem, we jointly employ the Charnes-Cooper approach, the second order Taylor expansion and Bernstein-type inequality, and propose a successive convex approximation based algorithm to obtain the satellite beamforming weight vector, the BS power allocation and the IRS phase shifts. Finally, simulation results demonstrate that our proposed scheme can improve the spectrum efficiency and secrecy performance of ISTNs compared to the benchmarks.