Total Power Constraint Research Articles

Precoder Design for Network Massive MIMO Optical Wireless Communications.

Precoding is a technique employed to enhance transmission rates in various communication systems, including massive multiple-input multiple-output (MIMO) and optical wireless communication (OWC). In this study, we focus on network massive MIMO OWC (NM-MIMO-OWC) systems and investigate the precoder design to enhance the sum rate and improve the system performance. We present the network's massive MIMO OWC framework. By utilizing this framework, we are able to calculate the achievable sum rate. Subsequently, we consider the precoding design for maximizing the sum rate while adhering to the total power constraint. To solve this optimization problem, we provide a necessary condition of the optimal solution based on the Karush-Kuhn-Tucker (KKT) conditions, and propose a low-complexity algorithm to further enhance the efficiency of the proposed precoding technique. The numerical results demonstrate that the proposed precoder design significantly improves the transmission rate and effectively maximizes the sum rate.

Statistical spatial information based power optimization algorithm for multi-user massive MIMO systems

Power optimization has a significant role in multi-user massive multiple-input and multiple-output (mMIMO) wireless communication systems, whereas the acquisition of instantaneous channel state information (I-CSI) is a serious challenge in practical scenarios. The statistical spatial information (SPI), which contains angle-of-departures (AoDs) and corresponding large-scale fading factors, is slowly varying. The power optimization based on SPI to improve system performance needs to be explored for multi-user mMIMO systems. In this paper, two novel algorithms for SPI-based power optimization are proposed to maximize the ergodic sum-rate (ESR) under total power constraint for the system. Specifically, the minorization-maximization-based power optimization (MM-PO) algorithm and the closed-form power optimization (CF-PO) algorithm are proposed utilizing the minorization-maximization (MM) method and the fractional programming (FP) method, respectively. Moreover, the ESR is expressed based on the extracted SPI. The approximate expression of the ESR is derived. The complexity of the algorithms depends on the number of users, and the algorithms are scalable in the system. The proposed algorithms can effectively achieve the advantages of SPI while being robust to the uncertainty of I-CSI. Simulation results verify the ESR of our two proposed algorithms is 1.47 and 1.4 times higher than that of the equal power allocation algorithm at a high SNR when the number of antenna and user are equal to 64 and 20, respectively.

UAV-Assisted Cooperative NOMA and OFDM Communication Systems: Analysis and Optimization

Utilizing unmanned aerial vehicles (UAVs) to facilitate wireless communication has emerged as a viable and promising strategy to enhance current and prospective wireless systems. This approach offers many advantages by establishing line-of-sight connections, optimizing operational efficiency, and enabling flexible deployment capabilities in various terrains. Thus, in this paper, we investigate UAV communication in a relaying network in which a UAV helps communication between a source and two destination users while flying to a location. To have a complete view of our proposed system, we consider both orthogonal multiple access, such as OFDMs and non-orthogonal multiple access (NOMA) scenarios. Moreover, we apply successive convex optimization (SCO) and the block-coordinate gradient descent (BCGD) for the sum-rate optimization problems to improve the system performance under constraints of total bandwidth and total power at the ground base station and UAV. The experimental results validate that the achievable secrecy rates are notably enhanced using our proposed algorithms and show optimal trends for critical parameters, such as transmit powers, the flight trajectory and speed of the UAV, and resource allocation of OFDM and NOMA.

Ergodic rate analysis and power allocation schemes for a novel active simultaneous transmission and reflection reconfigurable intelligent surface‐aided wireless network

SummaryIn this work, we propose the ergodic rate analysis and power allocation schemes for a novel active simultaneous transmits and reflects reconfigurable intelligent surface (STAR‐RIS) aided downlink wireless network. Here, the system model comprises of a single antenna source node (S) that uses the ES protocol to broadcast information to two users (T user and R user) via active STAR‐RIS. Each active STAR‐RIS element can function simultaneously in transmission and reflection modes owing to this ES protocol. We first derive the closed‐form analytical expressions for the system's ergodic rate. An optimization problem is formulated with a total power constraint to enhance the ergodic rate of the proposed system. A computationally inexpensive particle swarm optimization‐based power allocation (PSO‐PA) scheme and the Lagrange multiplier method (LMM) are adopted to solve the described optimization problem and to find the optimized power allocation factors for the source node and the active STAR‐RIS that can enhance the ergodic rate. Additionally, a comparison of the suggested PSO‐PA scheme, the LMM approach, and the equal power allocation (EPA) scheme is shown in terms of ergodic rate. Our results unveil that the proposed active STAR‐RIS system outperforms the passive STAR‐RIS and conventional RIS (transmitting/reflecting) aided systems. Further, the ergodic rate can be significantly improved with the proposed PSO‐PA and LMM schemes over the EPA scheme.

Hybrid RIS-Assisted Communications: Optimal Allocation under A Total Power Constraint

Hybrid RIS-Assisted Communications: Optimal Allocation under A Total Power Constraint

Joint optimization scheme for the reconfigurable intelligent surface-assisted untrusted relay networks

To further improve the secrecy rate, a joint optimization scheme for the reconfigurable intelligent surface (RIS) phase shift and the power allocation is proposed in the untrusted relay (UR) networks assisted by the RIS. The eavesdropping on the UR is interfered by a source-based jamming strategy. Under the constraints of unit modulus and total power, the RIS phase shift, the power allocation between the confidential signal and the jamming signal, and the power allocation between the source node and the UR are jointly optimized to maximize the secrecy rate. The complex multivariable coupling problem is decomposed into three sub-problems, and the non-convexity of the objective function and the constraints is solved with semi-definite relaxation. Simulation results indicate that the secrecy rate is remarkably enhanced with the proposed scheme compared with the equal power allocation scheme, the random phase shift scheme, and the no-RIS scheme.

Reliability-Oriented Resource Allocation for Wireless Powered Short Packet Communications With Multiple WPT Sources

We study a multi-source wireless power transfer (WPT) enabled network supporting multi-sensor transmissions. Activated by the energy harvesting from multiple WPT sources, the sensors transmit short packets to a destination with finite blocklength (FBL) codes. This work <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">for the first time</i> characterizes the FBL reliability for such multi-source WPT enabled network and accordingly provides reliability-oriented resource allocation designs, while a practical nonlinear EH model (including the effects of mutual interference among multiple RF signals) is considered. For the scenario with a fixed frame structure, we aim to maximize the FBL reliability via optimally allocating the transmit power among the multiple WPT sources. In particular, we investigate the relationship between the overall error probability and the transmit power of multiple WPT sources, based on which a power allocation problem is formulated. To solve the formulated non-convex problem, we first introduce auxiliary variables to make the problem analytically tractable, based on which an iterative algorithm is proposed while applying successive convex approximation (SCA) technique to the non-convex components of the problem. Then, we extend our design into a dynamic frame structure scenario, i.e., the blocklength allocated for WPT phase and short-packet transmission phase are adjustable, which introduces more flexibility and new challenges. In particular, we provide a joint power and blocklength allocation design maximizing the overall reliability under total power and blocklength constraints. A problem with high-dimension variables is formulated, which suffers from the complex and non-convex relationship among system reliability, multiple source power and blocklength. To tackle the difficulties, auxiliary variables introduction, multiple variable substitutions along with SCA technique utilization are exploited to reformulate and efficiently solve the problem. Finally, through numerical results, we validate our analytical model and evaluate the system performance, where a set of guidelines for practical system design are concluded.

Double-Active-IRS Aided Wireless Communication With Total Amplification Power Constraint

Double-Active-IRS Aided Wireless Communication With Total Amplification Power Constraint

Integrated sensing and communication waveform design in the Internet of Vehicles

We design an integrated sensing and communication (ISAC) transmission waveform with total transmission power, waveform similarity, and peak-to-average power ratio (PAPR) constraints in the Internet of Vehicles (IoV). The integrated waveform can complete radar target detection while communicating with downlink cellular users. The design of IoV with ISAC can not only improve the spectrum efficiency, but also further promote the integration of communication and sensing equipment. In this paper, the IoV with ISAC is formally modeled from the communication view and sensing view respectively. On this basis, taking the joint optimization of multi-user interference (MUI) total energy and Cramér Rao Bound (CRB) as the objective function, the transmission waveforms are designed with total transmission power, waveform similarity, and PAPR constraints respectively. To obtain the global optimal solution, we adopt a low complexity algorithm to solve the optimization problem under the total transmit power constraint and the optimization problem based on waveform similarity and PAPR constraints is further transformed into a nonconvex quadratically constrained quadratic programming (QCQP) problem, which is solved by using the semidefinite relaxation (SDR) technique. By ranking one approximation of the constrained optimization problem, it can be transformed into the solution of semidefinite programming (SDP) problems. The numerical results show that the designed ISAC transmission waveform can achieve an efficient tradeoff between sensing and communication performance, and each KPI is superior to one of the only communication and only sensing waveform design schemes. And it can improve the real-time information interaction ability of the IoV.

Reliability analysis for NOMA‐based UAV assisted short packet communication

AbstractThis paper introduces the short packet transmission in non‐orthogonal multiple access (NOMA)‐based unmanned aerial vehicle (UAV) assisted relay communication system. Short packet transmission has considerable potential to decrease the transmission latency of UAV communication, and NOMA can effectively enhance the spectrum efficiency and fairness. To improve the reliability of the system, an effective packet error rate (PER) minimization problem within short packet transmission is proposed by jointly optimizing the packet length, UAV placement, and power allocation under the reliability requirement and total power constraints. To address the intricate PER minimization problem, the optimization problem is firstly decomposed into three sub‐problems, and the corresponding monotonicity and convexity are analyzed, respectively. Then, an overall iterative optimization algorithm for PER minimization based on alternating direction method of multipliers algorithm and optimal solution algorithm is formulated by solving the three sub‐problems in an iterative manner. Simulation results validate the effectiveness and convergence of the proposed NOMA scheme and overall iterative optimization algorithm, respectively.

RSMA Precoding Design Based on Interference Nulling and Sum Rate Upper Bound

Rate splitting multiple access (RSMA), a recent generalization and merging of spatial division multiple access (SDMA) and superposition coding has exponential complexity. Hierarchical streams that transmit a subset of possible streams of encoded messages are proposed for rate splitting and analyzed. To reduce precoding complexity, RSMA with interference nulling (RSMA-IN), that nulls the portion of interference that is not decoded is investigated by formulating a sum rate maximization problem subject to a total power constraint. To solve this non-convex problem, a method that combines interference nulling and stream SNR maximization is proposed. A convex upper bound problem is formulated for the sum rate to compare existing algorithms, and conditions under which the upper bound problem is tight are determined. Taking advantage of the tight cases, we propose an upper bound aided (UBA) enhancement of existing precoding designs by exploiting optimal solutions in the tight cases. It is also shown that the existing augmented weighted mean square error (AWMSE) algorithms cannot converge to a local optimum in sum rate maximization. Simulation results indicate that the proposed approaches enhance performance over existing ones as well as approach their upper bounds in certain instances.

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.

Energy-Efficient Resource Allocation Algorithm for CR-WSN-Based Smart Irrigation System under Realistic Scenarios

Cognitive radio wireless sensor networks (CR-WSNs) are a type of WSNs that use cognitive radio technology to enhance the spectrum utilization and energy efficiency. This paper proposes an energy-efficient resource allocation algorithm (EERAA) to prolong the lifetime of a WSN-based smart irrigation system under realistic scenarios. In the proposed algorithm, power allocation and subcarrier assignment are performed consecutively. Considering the impact of the intercarrier interference (ICI) caused by timing offset, the problem of maximizing network-averaged capacity is formulated considering power and interference constraints in realistic scenarios. The obtained results reveal that the proposed algorithm attempts to maximize the averaged capacity of the CR-WSN subject to the total power constraint and tolerable interference. Numerically, the proposed algorithm can reduce the network energy consumption by up to 30%, compared with conventional approaches, while maintaining a high level of system performance in terms of secondary users’ (SUs) averaged capacity.

Waveform Design for MIMO Dual-Functional Radar-Communication System Using MUI Energy Minimization With PAPR and CRB Constraints

In this letter, we design a multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) waveform which can simultaneously detect multiple MIMO radar targets and serve downlink users. A nonconvex optimization problem is established to minimize the downlink multi-user interference (MUI) energy under the constraints of total power, peak average power ratio (PAPR) and Cramér-Rao bound (CRB) of direction-of-arrival (DOA) estimation. We propose a multivariable-iteration-based alternating direction method of multipliers (MI-ADMM) algorithm to solve such a nonconvex problem by transforming it into several convex subproblems. Thus, we only need to solve each convex subproblem alternately during each iteration. Additionally, we analyze the complexity of the proposed algorithm. Numerical simulations show the impacts of PAPR and CRB constraints on the performance of the DFRC system. The results demonstrate that the DFRC waveform can achieve a great trade-off between the performance of MIMO radar and communication.

Multigroup Multicast Beamforming for High Throughput GEO Satellite Communications Under Power-Consumption Outage Constraints

The heat dissipation of high throughput geostationary earth orbit (GEO) satellites poses great challenges to current satellite communications, which may cause performance degradation due to the power-consumption outage (PCO). Motivated by this, we investigate the multigroup multicast beamforming design for high throughput GEO satellite communications as an energy efficiency maximization problem which considers the PCO and total power constraints in this work. To cope with this nonconvex problem, we propose a low-complexity algorithmic approach. Numerical results demonstrate that our proposed approach outperforms the conventional one.

Symbol Error Probability Optimization of OFDM Bidirectional AF Relaying Systems

This paper considers that two transceivers exchange orthogonal frequency division multiplexing (OFDM) signals with the aid of multiple bidirectional amplify and forward (AF) relays. Unlike most researchers who care about sum rate maximization issues to improve transmission efficiency, we formulate a symbol error probability (SEP) minimization problem under total power constraint because high data rate may degrade SEP performance. A joint algorithm of power allocation and relay selection is proposed to study their impact on the SEP performance. The closed-form solution of power allocation is not a form of water filling widely used in the sum rate maximum problem. Both analytical and simulation results clearly demonstrate the superiority of our algorithm.

Total and Minimum Energy Efficiency Tradeoff in Robust Multigroup Multicast Satellite Communications

Satellite communication is an indispensable part of future wireless communications given its global coverage and long-distance propagation. In satellite communication systems, channel acquisition and energy consumption are two critical issues. To this end, we investigate the tradeoff between the total energy efficiency (TEE) and minimum EE (MEE) for robust multigroup multicast satellite communication systems in this paper. Specifically, under the total power constraint, we investigate the robust beamforming aimed at balancing the TEE-MEE, so as to achieve the balance between the fairness and total performance on the system EE. For this optimization problem, we first model the balancing problem as a nonconvex problem while deriving its approximate closed-form average user rate. Then, the nonconvex problem is handled by solving convex programs sequentially with the help of the semidefinite relaxation and the concave-convex procedure. In addition, depending on the solution rank value, Gaussian randomization and eigenvalue decomposition method are applied to generate the feasible solutions. Finally, simulation results illustrate that the proposed approach can effectively achieve the balance between the TEE and MEE, thus realizing a tradeoff between fairness and system EE performance. It is also indicated that the proposed robust approach outperforms the conventional baselines in terms of EE performance.

Covert Beamforming Design for Integrated Radar Sensing and Communication Systems

We propose covert beamforming design frameworks for integrated radar sensing and communication (IRSC) systems, where the radar can covertly communicate with legitimate users under the cover of the probing waveforms without being detected by the eavesdropper. Specifically, by jointly designing the target detection beamformer and communication beamformer, we aim to maximize the radar detection mutual information (MI) (or the communication rate) subject to the covert constraint, the communication rate constraint (or the radar detection MI constraint), and the total power constraint. For the perfect eavesdropper’s channel state information (CSI) scenario, we transform the covert beamforming design problems into a series of convex subproblems, by exploiting semidefinite relaxation, which can be solved via the bisection search method. Considering the high complexity of iterative optimization, we further propose a single-iterative covert beamformer design scheme based on the zero-forcing criterion. For the imperfect eavesdropper’s CSI scenario, we develop a relaxation and restriction method to tackle the robust covert beamforming design problems. Simulation results demonstrate the effectiveness of the proposed covert beamforming schemes for perfect and imperfect CSI scenarios.

Accelerated IoT Anti-Jamming: A Game Theoretic Power Allocation Strategy

A jamming combating power allocation strategy is proposed to secure the data communication in IoT networks. The proposed strategy aims to minimize the worst case jamming effect on the intended transmission under multi channel fading and total power constraints by modelling the problem as a Colonel Blotto game Nash Equibrium (NE). Both Logistic Regression as well as a specifically designed algorithm are used to iteratively and rapidly obtain the equilibrium strategy. The conducted theoretical derivations and Monte Carlo simulations confirm that the proposed approach can secure the IoT network with a limited amount of power and with a number of iterations that is much reduced compared to state-of-the-art techniques.

Distributed Multiantenna Frequency-Selective Energy Beamforming With Joint Total and Individual Power Constraints

We analyze distributed multi-antenna energy beamforming over frequency selective fading channels in wireless power transfer (WPT) systems with joint total and individual transmit power constraints. The constraints allow the WPT system to limit energy consumption for cost or environmental factors, and prevent individual coordinated energy transmitters (CETs) from overdriving their high-powered amplifiers due to hardware limitations. The harvested power at the energy receiver is modeled as an arbitrary non-linear, continuous, and non-decreasing function of the received radio frequency (RF) power. We show that the optimal frequency selective energy beamforming solution for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> CETs with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> antennas each is equivalent to maximizing the total RF power coherently combined at the energy receiver over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> shared subchannels. We obtained the optimal frequency selective energy beamforming strategy with the following insightful properties: First, for any subchannel, either all antennas of all <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> CETs allocate zero power to this subchannel (i.e., inactive), or all antennas of all <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> CETs allocate non-zero power to this subchannel (i.e., active). Second, for the active subchannels, we show that the optimal power allocation obeys a proportionality principle for the powers from all antennas of all <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> CETs, which reveals that higher powers are not necessarily allocated to subchannels with higher channel gains. Third, we prove that optimally no more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${T}\,\,+$ </tex-math></inline-formula> 1 subchannels are selected for energy beamforming, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula> (less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> ) is the number of CETs transmitting at their maximum individual powers due to the total power constraint. Based on the above properties, we show that the energy beamforming solution can be efficiently achieved via solving a low-complexity dual problem in simpler form with only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K} +1$ </tex-math></inline-formula> variables compared to the original primal problem with NMK variables. Numerical examples verify our theoretical findings and show the impact of the joint total and individual power constraints on the average harvested power.