Minimum Total Transmission Power Research Articles

Multi-Objective Robust Beamforming for Integrated Satellite and Aerial Networks Supporting Heterogeneous Services

An integrated satellite and aerial network (ISAN) is considered a promising candidate to provide seamless connectivity for future wireless communication systems. In this paper, we propose a multi-objective based robust beamforming (BF) scheme for an ISAN to support heterogeneous services with high flexibility, where the satellite network serves various heterogeneous satellite terminals through multicast non-orthogonal multiple access (MC-NOMA), while the aerial network offers services to many internet of things devices using layered division multiplexing (LDM). Specifically, we first formulate a multi-objective optimization problem (MOOP) to achieve a good trade-off between sum rate maximization and total transmit power minimization. To tackle this mathematically intractable problem, we exploit the weighted Tchebycheff approach to transform the MOOP into a single-objective problem. Since only the angular information based channel state information is available, we exploit the angular discretization method and sequential convex approximation to design a robust BF algorithm to obtain the Pareto optimal solutions. Finally, simulation results demonstrated that our proposed scheme can achieve a optimal trade-off between multiple performance metrics with high spectrum and energy efficiency, so as to support heterogeneous services in the ISAN and fill the gap of only single type of serivce in the existing ISAN works.

Energy-Efficient Resource Allocation for NOMA HetNets in Millimeter Wave Communications

The integration of millimeter-wave (mmWave) and non-orthogonal multiple access (NOMA) has attracted academic and industrial interest as a promising enabling approach due to their massive bandwidth support and greater spectral efficiency. In this paper, we consider NOMA heterogeneous network (HetNet) in mmWave communications, consisting of the macro-cell and small cell tiers connected through the wireless backhaul. Following this, we propose a user grouping algorithm to significantly simplify the user clustering process where each cluster consists of highly correlated users to suppress the inter-cluster interference. We then propose a hybrid analog/digital precoding scheme at the macro base station. The goal is to maximize the system’s total energy-efficiency (EE) in NOMA HetNet by jointly optimizing the transmission of hybrid precoding, power allocation, and bandwidth partition. Next, we formulate an optimization problem within the proposed framework under the minimum data rate and total maximum transmission power constraints. Due to the non-convexity nature of the formulated problem, it is challenging to obtain the optimal solution. As a result, we transform the original problem into a quasi-convex equivalent by analyzing its structure. Then we propose energy-efficient distributed power allocation algorithms to optimize the resource allocation of macro-cell and small cells. Finally, we exhibit the simulation results to demonstrate the proposed algorithm’s performance gains. The simulation results reveal that the proposed algorithmic approach surpasses state-of-the-art and traditional mmWave orthogonal multiple access systems in both capacity and EE.

Robust Distributed Hybrid Beamforming in Coordinated Multi-User Multi-Cell mmWave MIMO Systems Relying on Imperfect CSI

Novel hybrid beamformer designs are conceived for a multi-user multi-cell (MUMC) mmWave system relying on base station (BS) coordination and total transmit power minimization subject to realistic signal-to-interference-plus-noise ratio (SINR) constraints at each mobile station (MS). Initially, a semidefinite relaxation (SDR)-based approach is developed for a centralized MUMC system to determine the fully digital beamformer having perfect CSI. Subsequently, a Bayesian learning (BL) technique is harnessed for decomposing the fully-digital (FD) solution into its analog and digital components for constructing a hybrid transceiver. Next, an alternating direction method of multipliers (ADMM) based distributed hybrid beamformer is designed for the same system, which requires only local CSI and limited information exchange among the BSs, thus avoiding the excessive signalling overheads required by the centralized approach. Then we further extend both the centralized and the above distributed hybrid designs to construct robust beamformers that minimize the worst-case transmit power with imperfect CSI. Our robust beamforming techniques leverage the S-lemma, which is eminently suitable for the infinitely many constraints arising from the associated CSI uncertainty. Finally, our simulation results demonstrate the improved performance of the proposed centralized and distributed methods over the system having no coordination.

Relay-Assisted Uplink Transmission Design of URLLC Packets

With the emergence and development of mission-critical applications, the design of uplink URLLC transmission has become one of the issues of concern. In this article, we consider the ultrareliable uplink transmission design between multiple robots and a central controller in a smart factory with stringent delay requirements while multiple relays are deployed for cooperative transmission. Subject to the throughput and reliability requirements, a relay-assisted two-phase transmission protocol is proposed aiming at total transmit power minimization by jointly optimizing the decoding error probability, relay selection, resource block (RB) assignment, and transmit power allocation. To support the mission-critical applications in Internet of Things (IoT) with diverse throughput targets and make full use of RBs, we investigate the optimization problem under the single-RB allocation and multi-RB allocation schemes. The problems in these two cases have an analogous structure and can be solved with a unified framework. In the solution framework, we present the optimal design solved by the nonconvex penalty (NCP)- and quadratic penalty (QP)-based successive convex approximation (SCA) algorithms and the corresponding low-complexity design with near-optimal settings of decoding error probabilities. Numerical results demonstrate the effectiveness of the proposed penalty-based iterative algorithms and confirm that the near-optimal design only causes minor power performance loss compared to the optimal design. The power performance gap under these two allocation schemes and the impact of system parameters are revealed and analyzed.

Integrating Sensing, Computing, and Communication in 6G Wireless Networks: Design and Optimization

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).

Outage Constrained Robust Beamforming Optimization for Multiuser IRS-Assisted Anti-Jamming Communications With Incomplete Information

Malicious jamming attacks have been regarded as a serious threat to Internet of Things (IoT) networks, which can significantly degrade the Quality of Service (QoS) of users. This article utilizes an intelligent reflecting surface (IRS) to enhance anti-jamming performance due to its capability in reconfiguring the wireless propagation environment via dynamically adjusting each IRS reflecting elements. To enhance the communication performance against jamming attacks, a robust beamforming optimization problem is formulated in a multiuser IRS-assisted anti-jamming communications scenario with or without imperfect jammer’s channel state information (CSI). In addition, we further consider the fact that the jammer’s transmit beamforming can not be known at BS. Specifically, with no knowledge of jammers transmit beamforming, the total transmit power minimization problems are formulated subject to the outage probability requirements of legitimate users with the jammer’s statistical CSI, and signal-to-interference-plus-noise ratio requirements of legitimate users without the jammer’s CSI, respectively. By applying the decomposition-based large deviation inequality, Bernstein-type inequality, Cauchy–Schwarz inequality, and penalty nonsmooth optimization method, we efficiently solve the initial intractable and nonconvex problems. Numerical simulations demonstrate that the proposed anti-jamming approaches achieve superior anti-jamming performance and lower power-consumption compared to the non-IRS scheme and reveal the impact of key parameters on the achievable system performance.

Resource Management for Transmit Power Minimization in UAV-Assisted RIS HetNets Supported by Dual Connectivity

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 (<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>) channel in the sky to sustain a strong line-of-sight (LoS) connection with the ground users. The macro-cell operates over the <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> 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&#x2019; 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&#x2019; trajectories/velocities, RISs&#x2019; phase shifts, and subcarrier allocations for <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>; 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.

Multi-objective robust secure beamforming for cognitive satellite and UAV networks

A multi-objective optimization based robust beam-forming (BF) scheme is proposed to realize secure transmission in a cognitive satellite and unmanned aerial vehicle (UAV) network. Since the satellite network coexists with the UAV network, we first consider both achievable secrecy rate maximization and total transmit power minimization, and formulate a multi-objective optimization problem (MOOP) using the weighted Tchebycheff approach. Then, by supposing that only imperfect channel state information based on the angular information is available, we propose a method combining angular discretization with Taylor approximation to transform the non-convex objective function and constraints to the convex ones. Next, we adopt semi-definite programming together with randomization technology to solve the original MOOP and obtain the BF weight vector. Finally, simulation results illustrate that the Pareto optimal trade-off can be achieved, and the superiority of our proposed scheme is confirmed by comparing with the existing BF schemes.

Joint design of transmit beamforming, IRS platform, and power splitting SWIPT receivers for downlink cellular multiuser MISO

A multiple antenna base station (BS) with an intelligent reflecting surface (IRS) platform, and several single antenna users are considered in the downlink mode. Simultaneous wireless information and power transfer (SWIPT) is utilized by the BS via transmit beamforming to convey information and power to all devices. Each device applies power splitting (PS) to dedicate separate parts of received power to information decoding and energy harvesting. We formulate a total transmit power minimization problem to jointly design the BS beamforming vectors, IRS phase shifts, and PS ratios at the receivers subject to minimum rate and harvested energy quality of service (QoS) constraints at all the receivers. First, we develop a block coordinate descent algorithm, also known as alternating optimization that can decrease the objective function in every iteration with guaranteed convergence. Afterwards, two low-complexity sub-optimal algorithms that rely on well-known maximum ratio transmission and zero-forcing beamforming techniques are introduced. These algorithms are beneficial when the number of BS antennas and/or number of users is large, or coherence times of channels are small. Simulations corroborate the expectation that by deploying a passive IRS, BS transmit power can be reduced by 10–20 dBw while maintaining similar guaranteed QoS. Furthermore, even the proposed sub-optimal algorithms outperform the globally optimal SWIPT solution without IRS for a modest number of IRS elements.

Power allocation technique with soft performance guarantees in hybrid OFDMA–NOMA cognitive radio systems: Modeling and simulation

The integration between orthogonal frequency division multiple access (OFDMA), non-orthogonal multiple access (NOMA), and cognitive radio (CR) technology has been recently configured as promising candidate to support the massive high-speed connectivity requirements of beyond fifth generation (B5G) systems. This combination is referred to in the literature as a hybrid OFDMA–NOMA CR network. In such a system, each primary user’s (PU) idle channel, i.e., sub-band, is opportunistically utilized to serve multiple secondary users (SUs) by exploiting power-domain NOMA. This study soughs to explore the resource allocation technique for a hybrid OFDMA–NOMA CR system to further elaborate the potential capabilities of this system. Specifically, a power minimization resource allocation framework is developed, in which the objective is to serve the contending CR users, i.e., SUs, with minimum possible total transmission power, while achieving a set of relevant NOMA and CR constraints. These constraints include the dynamic nature of PU activities, rate demand, probability of success and imposed CR power mask constraints. However, the developed power minimization framework is non-convex in nature. Therefore, we employ the sequential convex approximation (SCA) along with a second-order cone approach to deal with the non-convexity issues of the developed optimization problem, and thus evaluating the corresponding optimization parameters. Compared to the conventional orthogonal resource allocation techniques, simulation results reveal that our proposed resource allocation technique for the hybrid OFDMA–NOMA CR system has a considerable performance enhancement in terms of the overall network throughput and total required transmit power.

MISO NOMA downlink beamforming optimization with per-antenna power constraints

Consider a multiuser downlink beamforming optimization problem for the non-orthogonal multiple access (NOMA) transmission in a multiple-input single-output (MISO) system with a general number of users. The total transmission power minimization problem is formulated subject to both quality-of-service (QoS) constraints under the NOMA principal and per-antenna power constraints, which are more realistic since each antenna has its own power amplifier and is limited individually by the linearity of the power amplifier. The problem is a nonconvex quadratically constrained quadratic program, and the conventional semidefinite program (SDP) relaxation is not tight. In order to tackle the non-convex problem, we construct second-order cone program (SOCP) approximation for each signal-to-interference-plus-noise ratio (SINR) constraint and form an iterative algorithm, in which a sequence of SOCPs are solved. The optimal values of SOCPs in the sequence are proved to be non-increasing, and converging to a locally optimal value. In particular, we show that the SDP relaxation is tight for two-user case if one of the SINR constraints is strict (non-binding) at the optimality. Detailed simulation results are presented to demonstrate the performance gains of the NOMA downlink beamforming with per-antenna power constraints through the proposed algorithm, which is compared with some state-of-the-art methods.

Power Efficient Secure Full-Duplex SWIPT Using NOMA and D2D with Imperfect CSI.

The secure full-duplex (FD) simultaneous wireless information and power transfer (SWIPT) system and non-orthogonal multiple access (NOMA) have been deemed two promising technologies for the next generation of wireless communication. In this paper, the network is combined with device-to-device (D2D) and a practical bounded channel state information (CSI) estimation scheme. A system total transmit power minimization problem is studied and formulated as a multi-objective optimization (MOO) problem via the weighted Tchebycheff approach. A set of linear matrix inequalities (LMI) is used to transform the non-convex form of constraints into the convex form. Considering the imperfect CSI of the potential eavesdropper for robust power allocation, a bounded transmission beamforming vector design along with artificial noise (AN) is used, while satisfying the requirements from the secrecy rates as well as the energy harvesting (EH) task. Numerical simulation results validate the convergence performance and the trade-off between the uplink (UL) and downlink (DL) data transmit power. It is also shown that by FD and NOMA, the performance of the proposed algorithm is higher than that of half-duplex (HD) and orthogonal multiple access (OMA).

Dynamic resource allocation for opportunistic software-defined IoT networks: stochastic optimization framework

Several wireless technologies have recently emerged to enable efficient and scalable internet-of-things (IoT) networking. Cognitive radio (CR) technology, enabled by software-defined radios, is considered one of the main IoT-enabling technologies that can provide opportunistic wireless access to a large number of connected IoT devices. An important challenge in this domain is how to dynamically enable IoT transmissions while achieving efficient spectrum usage with a minimum total power consumption under interference and traffic demand uncertainty. Toward this end, we propose a dynamic bandwidth/channel/power allocation algorithm that aims at maximizing the overall network’s throughput while selecting the set of power resulting in the minimum total transmission power. This problem can be formulated as a two-stage binary linear stochastic programming. Because the interference over different channels is a continuous random variable and noting that the interference statistics are highly correlated, a suboptimal sampling solution is proposed. Our proposed algorithm is an adaptive algorithm that is to be periodically conducted over time to consider the changes of the channel and interference conditions. Numerical results indicate that our proposed algorithm significantly increases the number of simultaneous IoT transmissions compared to a typical algorithm, and hence, the achieved throughput is improved.

Total transmit power minimization with physical layer security in multiuser peer-to-peer two-way relay networks

In this paper, we investigate a multiuser peer-to-peer (MUP2P) two-way relay network (TWRN) in the presence of an eavesdropper. In this network, only the k∗th pair of users demand secure communication. Also, a multi-antenna eavesdropper aim to wiretap the confidential data of the k∗th pair in both cooperative phases. Our goal is to minimize the total transmit power provided that the secrecy rate for two secure users and the quality of service constraints of each user are met. For simplifying the problem and also omitting the confidential information leakage in the second phase, a null space beamforming (NSBF) scheme is employed. Despite this, the problem is still non-convex. We divided the problem into two subproblems to solve beamforming and users’ power formulated in two iterations, respectively. Three different methods are proposed to solve the first subproblem, semi-definite programming (SDP), second-order cone programming (SOCP), and sequential quadratic programming (SQP). We employ a sequential parametric convex approximation (SPCA) method to convert the users’ power subproblem into inequality form a linear programming (LP) problem to solve it. In the following, we evaluate the computational complexity of these methods. Simulation results confirmed the validity of the proposed methods.

Resource Allocation for Secure URLLC in Mission-Critical IoT Scenarios

Ultra-reliable low latency communication (URLLC) is one of three primary use cases in the fifth-generation (5G) networks, and its research is still in its infancy due to its stringent and conflicting requirements in terms of extremely high reliability and low latency. To reduce latency, the channel blocklength for packet transmission is finite, which incurs transmission rate degradation and higher decoding error probability. In this case, conventional resource allocation based on Shannon capacity achieved with infinite blocklength codes is not optimal. Security is another critical issue in mission-critical internet of things (IoT) communications, and physical-layer security is a promising technique that can ensure the confidentiality for wireless communications as no additional channel uses are needed for the key exchange as in the conventional upper-layer cryptography method. This paper is the first work to study the resource allocation for a secure mission-critical IoT communication system with URLLC. Specifically, we adopt the security capacity formula under finite blocklength and consider two optimization problems: weighted throughput maximization problem and total transmit power minimization problem. Each optimization problem is non-convex and challenging to solve, and we develop efficient methods to solve each optimization problem. Simulation results confirm the fast convergence speed of our proposed algorithm and demonstrate the performance advantages over the existing benchmark algorithms.

On Optimal Beamforming Design for Downlink MISO NOMA Systems

This work focuses on the beamforming design for downlink multiple-input single-output (MISO) nonorthogonal multiple access (NOMA) systems. The beamforming vectors are designed by solving a total transmission power minimization (TPM) problem with quality-of-service (QoS) constraints. In order to solve the proposed nonconvex optimization problem, we provide an efficient method using semidefinite relaxation. Moreover, for the first time, we characterize the optimal beamforming in a closed form with quasi-degradation condition, which is proven to achieve the same performance as dirty-paper coding (DPC). For the special case with two users, we further show that the original nonconvex TPM problem can be equivalently transferred into a convex optimization problem and easily solved via standard optimization tools. In addition, the optimal beamforming is also characterized in a closed form and we show that it achieves the same performance as the DPC. In the simulation, we show that our proposed optimal NOMA beamforming outperforms OMA schemes and can even achieve the same performance as DPC. Our solutions dramatically simplifies the problem of beamforming design in the downlink MISO NOMA systems and improve the system performance.

Multiobjective Optimization of Uplink NOMA-Enabled Vehicle-to-Infrastructure Communication

Vehicular communication is gradually evolving from the rudimentary data exchange techniques to innovative, efficient, and large-scale communication platform. The future vehicular networks aim to offer new services and achieve massive connectivity with the aid of advanced wireless communication techniques. In this regard, one of the emerging paradigms is non-orthogonal multiple access (NOMA) which holds the promise to meet the resource-intensive demands of future networks. Due to this reason, the applications of NOMA are being investigated by the researcher of academia and industry alike. This article aims to build on the state-of-the-art by providing a novel multiobjective optimization framework for vehicle-to-infrastructure (V2I) communications. Specifically, this work aims to improve upon the sum-rate and energy efficiency of uplink NOMA-enabled V2I communications. The formulated problem is based on the tradeoff between sum-rate and total transmit power while maintaining the quality of service (QoS) requirements of the network. To provide a reliable solution, a multiobjective optimization framework has been proposed by applying the weighted-sum method and handle joint metrics of sum-rate maximization and total transmit power minimization. Extensive simulations have been performed to validate the superiority of the proposed framework against conventional benchmark techniques. The results clearly indicate that the proposed framework is feasible for NOMA-enabled V2I communication and can be scaled up for practical implementation.

Refining channel and power allocation for green device-to-device communications

Most of the existing researches for D2D communications try to achieve the minimum total transmission power of D2D user devices (DUEs) by performing channel assignment first, and then performing power control according to the QoS requirement of each DUE. However, after power control, the transmission powers and interferences of DUEs have dropped and the qualities of channels have changed, hence there is room to further reduce the transmission power of DUEs. To improve the existing works, the channel and power reallocations are performed for several rounds in the proposed schemes until the dropping transmission power is less than a threshold. Note that, the channel and power are not really allocated to the DUEs until the final refining allocation results are calculated by the base station. The trade-off among transmission power, throughput, and computation cost are discussed through extensive simulations. Simulation results justify the energy efficiency of the proposed refining schemes.

Refining channel and power allocation for green device-to-device communications

Most of the existing researches for D2D communications try to achieve the minimum total transmission power of D2D user devices (DUEs) by performing channel assignment first, and then performing power control according to the QoS requirement of each DUE. However, after power control, the transmission powers and interferences of DUEs have dropped and the qualities of channels have changed, hence there is room to further reduce the transmission power of DUEs. To improve the existing works, the channel and power reallocations are performed for several rounds in the proposed schemes until the dropping transmission power is less than a threshold. Note that, the channel and power are not really allocated to the DUEs until the final refining allocation results are calculated by the base station. The trade-off among transmission power, throughput, and computation cost are discussed through extensive simulations. Simulation results justify the energy efficiency of the proposed refining schemes.

Robust Multi-Objective Beamforming for Integrated Satellite and High Altitude Platform Network With Imperfect Channel State Information

In this paper, we propose a robust beamforming (BF) scheme for an integrated satellite and high altitude platform network, where a multibeam satellite system shares the millimeter wave spectrum with a high altitude platform system. Specifically, we first exploit the weighted Tchebycheff method and formulate a multi-objective optimization problem to obtain the Pareto optimal trade-off between two conflicting yet desirable objectives, namely, sum rate maximization and total transmit power minimization, while satisfying the quality-of-service constraints of both earth stations and mobile terminals and per-antenna transmit power budget. Then, by using the angular information based imperfect channel state information, we propose a low-complexity discretization method to transform the non-convex objective function and constraints to the convex ones. Furthermore, a monotonic optimization scheme combined with iterative penalty function algorithm is presented to obtain the BF weight vectors with low computational complexity and fast convergence rate. Finally, numerical results are provided to confirm the effectiveness and superiority of the proposed approach in comparison to the existing related works.