Signal-to-interference-plus-noise Ratio Constraints Research Articles

Transmit Power Optimization and Precoding Design in Multiuser Satellite MIMO Downlink With SINR Constraints

In this paper, a multi-user (MU) satellite Multiple-Input Multiple-Output (MIMO) downlink including a multi-beam GEO satellite and multiple terrestrial users is considered. In order to meet the signal to interference plus noise ratio (SINR) requirements of different users while minimizing the satellite transmit power, the onboard precoding problem is investigated. Zero forcing (ZF) and regularized zero-forcing (RZF) precoding are first adopted and it is found that they can satisfy the SINR constraints by multiplying by a power allocation matrix. The required power allocation matrix is divided into two types: equal power factor schemes and unequal power factor schemes, and they are derived separately, where Karush-Kuhn-Tucker (KKT) conditions are used in RZF unequal power factor scheme and a heuristic algorithm is proposed to optimize the regularization factor in RZF schemes to further reduce the transmit power. Then, a semi-definite programming beamforming (SDPBF) is proposed to jointly optimize precoding and power allocation matrices. The closed-form expressions of the transmit power of the above five precoding schemes are derived and their performance is compared in the simulation part. The research results show that under the same conditions, the performance of RZF is better than that of ZF, the unequal power factor scheme is better than the equal power factor scheme, and SDPBF has the best performance. Besides, it is also found that in addition to changing the precoding method, activating more beams will further reduce the transmit power.

Robust Downlink Multi-Antenna Beamforming With Heterogenous CSI: Enabling eMBB and URLLC Coexistence

Two of the main problems to achieve ultra-reliable low-latency communications (URLLC) are related to instantaneous channel state information (I-CSI) acquisition and the coexistence with other service modes such as enhanced mobile broadband (eMBB). The former comes from the non-negligible time required for accurate I-CSI acquisition, while the latter, from the heterogeneous and conflicting requirements of different nodes sharing the same network resources. In this paper, we leverage the I-CSI of multiple eMBB links and the channel measurement’s history of a URLLC user for multi-antenna beamforming design. Specifically, we propose a precoding design that minimizes the transmit power of a base station (BS) providing eMBB and URLLC services with signal-to-interference-plus-noise ratio (SINR) and outage constraints, respectively, by modifying existing I-CSI-based precoding schemes to account for URLLC channel history information. Moreover, we illustrate and validate the proposed method by adopting zero-forcing (ZF) and the transmit power minimization (TPM) precoding with SINR constraints. We show that the ZF implementation outperforms TPM in adverse channel conditions as in Rayleigh fading, while the situation is rapidly reversed as the channel experiences some line-of-sight (LOS). Finally, we determine the confidence levels at which the target outage probabilities are reached. For instance, we show that outage probabilities below 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> are achievable with more than 99% confidence for both precoding schemes under favorable LOS conditions with 16 transmit antennas and 500 samples of URLLC channel history.

Double-Phase-Shifter Based Hybrid Beamforming for mmWave DFRC in the Presence of Extended Target and Clutters

In millimeter-wave (mmWave) dual-function radar-communication (DFRC) systems, hybrid beamforming (HBF) is recognized as a promising technique utilizing a limited number of radio frequency chains. In this work, in the presence of extended target and clutters, a HBF design based on the subarray connection architecture is proposed for a multiple-input multiple-output (MIMO) DFRC system. In this HBF, the double-phase-shifter (DPS) structure is embedded to further increase the design flexibility. We derive the communication spectral efficiency (SE) and radar signal-to-interference-plus-noise-ratio (SINR) with respect to the transmit HBF and radar receiver, and formulate the HBF design problem as the SE maximization subjecting to the radar SINR and power constraints. To solve the formulated nonconvex problem, the joinT Hybrid bRamforming and Radar rEceiver OptimizatioN (THEREON) is proposed, in which the radar receiver is optimized via the generalized eigenvalue decomposition, and the transmit HBF is updated with low complexity in a parallel manner using the consensus alternating direction method of multipliers (consensus-ADMM). Furthermore, we extend the proposed method to the multi-user multiple-input single-output (MU-MISO) scenario. Numerical simulations demonstrate the efficacy of the proposed algorithm and show that the solution provides a good trade-off between number of phase shifters and performance gain of the DPS HBF.

Optimization of pre-equalized time reversal security transmission systems assisted with artificial noise

Time reversal (TR) transmission technology can focus the power of a signal in both time and space domains and reduce signal energy leakage to unintended receivers, making it suitable for physical layer security systems. By adding a pre-equalizer to the transmitter and optimizing it, the performance of multi-antenna TR transmission system can be improved obviously with an acceptable optimization complexity. In this paper, the pre-equalizer and artificial noise (AN) are jointly optimized to improve the security performance of pre-equalized TR (ETR) systems when eavesdropping channel state information (ECSI) is unknown or known. When ECSI is unknown, null-space AN is adopted, and the pre-equalizer is optimized to minimize the signal power under the constraint of the minimum signal-to-interference-plus-noise ratio (SINR) of the legitimate receiver, so the AN power and the interference with the eavesdropper are maximized. The optimization problem is transformed into the problem of finding the maximum generalized eigenvalue of a matrix pencil. When ECSI is known, under the minimum SINR constraint of the legitimate receiver, the pre-equalizer and AN’s covariance matrix are jointly optimized to minimize the SINR of the eavesdropper. The optimization problem is non-convex and is transformed into a convex problem, which can be solved using the CVX toolbox. The optimization complexity is independent of the number of transmit antennas. Simulation results demonstrate that compared with TR systems, by optimizing the pre-equalizer, the security performance of ETR multi-antenna systems can be significantly improved, and the optimization complexity is acceptable.

Energy Efficiency Optimization with SINR Constraints in Downlink MIMO-NOMA Systems

In downlink multiple-input, multiple-output, (MIMO) and non-orthogonal multiple access (NOMA) systems, the inter-cluster interference can be cancelled by optimizing pre-coding and detection matrices. Thus, the MIMO-NOMA channel is decomposed into multiple single-input, single-output and (SISO)-MOMA channels. Then, we formulate an energy efficiency (EE) optimization problem subject to the signal-to-interference-plus-noise ratio (SINR) constraints which is non-convex. To solve the problem, we propose the algorithm based on one-dimensional linear search and first-order Taylor expansion. Moreover, as in conventional communication systems, EE and spectrum efficiency (SE) cannot always be improved simultaneously. Thus, the trade-off between EE and SE is investigated based on the formulated problem. Numerical analyses and simulation results verify the proposed algorithm. In addition, the performance in terms of EE and the EE-SE trade-off can be improved by optimizing the user pairing method.

Hybrid Beamforming for Millimeter Wave MIMO Integrated Sensing and Communications

In this letter, we consider hybrid beamforming for millimeter wave (mmWave) MIMO integrated sensing and communications (ISAC). We design the transmit beam of a dual-functional radar-communication (DFRC) base station (BS), aiming at approaching the objective radar beam pattern, subject to the constraints of the signal to interference-plus-noise ratio (SINR) of communication users and total transmission power of the DFRC BS. To provide additional degree of freedom for the beam design problem, we introduce a phase vector to the objective beam pattern and propose an alternating minimization method to iteratively optimize the transmit beam and the phase vector, which involves second-order cone programming and constrained least squared estimation, respectively. Then based on the designed transmit beam, we determine the analog beamformer and digital beamformer subject to the constant envelop constraint of phase shifter network in mmWave MIMO, still using the alternating minimization method. Simulation results show that under the same SINR constraint of communication users, larger antenna array can achieve better radar beam quality.

Joint Design of Hybrid Beamforming and Reflection Coefficients in RIS-Aided mmWave MIMO Systems

This paper considers a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) downlink communication system where hybrid analog-digital beamforming is employed at the base station (BS). We formulate a power minimization problem by jointly optimizing hybrid beamforming at the BS and the response matrix at the RIS, under the signal-to-interference-plus-noise ratio (SINR) constraints at all users. The problem is highly challenging to solve due to the non-convex SINR constraints as well as the unit-modulus phase shift constraints for both the RIS reflection coefficients and the analog beamformer. A two-layer penalty-based algorithm is proposed to decouple variables in SINR constraints, and manifold optimization is adopted to handle the non-convex unit-modulus constraints. We also propose a low-complexity sequential optimization method, which optimizes the RIS reflection coefficients, the analog beamformer, and the digital beamformer sequentially without iteration. Furthermore, the relationship between the power minimization problem and the max-min fairness (MMF) problem is discussed. Simulation results show that the proposed penalty-based algorithm outperforms the state-of-the-art semidefinite relaxation (SDR)-based algorithm. Results also demonstrate that the RIS plays an important role in the power reduction.

Sensing-Assisted Secure Uplink Communications With Full-Duplex Base Station

This letter proposes a sensing-assisted uplink communications framework between a single-antenna user and a full-duplex (FD) base station (BS) against an aerial eavesdropper (AE). To protect the information from being overheard, the BS transmits radar signals to localize and jam AE while receiving uplink signals. The radar signal transmission is divided into detection phase and tracking phase. In detection phase, the BS synthesizes a wide beam to localize the AE under the secrecy rate constraint; while in tracking phase, the BS maximizes the signal-to-interference-plus-noise ratio (SINR) of its received signals under the AE’s SINR constraint while guaranteeing a predefined radar echo signal signal-to-noise ratio (SNR) level. To deal with the self interference, we jointly optimize the radar waveform and receive beamforming vector. An alternating optimization algorithm and a successive convex approximation (SCA) based algorithm are proposed to solve the two formulated problems, respectively. Simulation results verify the effectiveness of the proposed algorithms. They also show that the secrecy rate can be significantly improved with the assistance of BS sensing.

Efficient Link Scheduling Solutions for the Internet of Things Under Rayleigh Fading

Link scheduling is an appealing solution for ensuring the reliability and latency requirements of Internet of Things (IoT). Most existing results on the link scheduling problem were based on the graph or SINR (Signal-to-Interference-plus-Noise-Ratio) models, which ignored the impact of the random fading gain of the signals strength. In this paper, we address the link scheduling problem under the Rayleigh fading model. Both Shortest Link Scheduling (SLS) and Maximum Link Scheduling (MLS) problems are studied. In particular, we show that a set of links can be activated simultaneously under Rayleigh fading model if all link SINR constraints are satisfied. Based on the analysis of previous Link Diversity Partition (LDP) algorithm, we propose an Improved LDP (ILDP) algorithm and a centralized algorithm by localizing the global interference (denoted by CLT), building on which we design a distributed CLT algorithm (denoted by RCRDCLT) that converges to a constant approximation factor of the optimum with the time complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\ln n)$ </tex-math></inline-formula> , where <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> is the number of links. Furthermore, executing repeatedly RCRDCLT can solve the SLS with an approximation factor of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Theta (\ln n)$ </tex-math></inline-formula> . Extensive simulations indicate that CLT is more effective than previous six popular link scheduling algorithms, and RCRDCLT has the lowest time complexity while only losses a constant fraction of the optimum schedule.

Outage analysis of cognitive non‐orthogonal multiple access downlink system with secondary source selection in Nakagami‐m fading environment

SummaryCognitive inspired non‐orthogonal multiple access (Cognitive‐NOMA) technique allows multiple primary and secondary users to utilize the same time and frequency resources to explore the reliability and spectral efficient requirements of 5G communication networks. In this paper Cognitive‐NOMA downlink system with multiple secondary source (SS) nodes is proposed. An optimal SS node is selected based on the signal to interference plus noise ratio (SINR) requirements of both near and far primary users and the secondary user node. Each optimal SS node incorporates simultaneous wireless information and power transfer (SWIPT) and full‐duplex technique to harvest energy and transceiver operation. Analytical outage probability expressions of the proposed system are derived over Nakagami‐m fading environment with the assumption that SS transmission links are correlated. Numerical results infer that the outage performance of the proposed system depends on the number of SS nodes, self‐interference at the SS node, and the SINR constraint for the selection of optimal SS node.

Information-Theoretic Radar Waveform Design under the SINR Constraint.

This study investigates the information–theoretic waveform design problem to improve radar performance in the presence of signal-dependent clutter environments. The goal was to study the waveform energy allocation strategies and provide guidance for radar waveform design through the trade-off relationship between the information theory criterion and the signal-to-interference-plus-noise ratio (SINR) criterion. To this end, a model of the constraint relationship among the mutual information (MI), the Kullback–Leibler divergence (KLD), and the SINR is established in the frequency domain. The effects of the SINR value range on maximizing the MI and KLD under the energy constraint are derived. Under the constraints of energy and the SINR, the optimal radar waveform method based on maximizing the MI is proposed for radar estimation, with another method based on maximizing the KLD proposed for radar detection. The maximum MI value range is bounded by SINR and the maximum KLD value range is between 0 and the Jenson–Shannon divergence (J-divergence) value. Simulation results show that under the SINR constraint, the MI-based optimal signal waveform can make full use of the transmitted energy to target information extraction and put the signal energy in the frequency bin where the target spectrum is larger than the clutter spectrum. The KLD-based optimal signal waveform can therefore make full use of the transmitted energy to detect the target and put the signal energy in the frequency bin with the maximum target spectrum.

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.

Virtual MIMO Beamforming for Opportunistic Cooperative Time Division Multiple Access

The conventional studies on virtual multiple-input multiple-output based cooperative time division multiple access (TDMA) have been focused on the cooperative retransmission of failed data. In this paper, we propose an opportunistic cooperative TDMA (OC-TDMA) scheme where a source user transmits its own data and the cooperative users opportunistically transmit low-data-rate users’ data. To do this, time slots are divided into non-cooperative and cooperative time slots. The non-cooperative time slots are located at earlier time slots, in which weak users, who cannot satisfy the required signal-to-interference-plus-noise ratio (SINR), are scheduled. The cooperative time slots are located at later time slots, in which strong users, who can satisfy the required SINR, are scheduled. In the cooperative time slots, the source user transmits its own data and the other users (i.e., cooperative users) assist to transmit the weak users’ data which was transmitted at the non-cooperative time slots. The optimal and sub-optimal OC-TDMA algorithms are analyzed and evaluated in terms of the required excess capacity , which indicates the capacity additionally required to satisfy the SINR constraints of all the users after all the transmission is finished. Throughout the simulation and numerical results, it is demonstrated that OC-TDMA schemes outperform conventional TDMA schemes.

Power Allocation in Cellular Networks Based on Outage Probability and Normalized SINR

In this paper, power allocation in cellular networks is proposed based on the outage probability and normalized signal to interference plus noise ratio (SINR). Upper and lower bounds on the outage probability are determined using the normalized SINR considering path loss, shadowing, and fading. The problems of minimizing the user power subject to outage probability and target SINR constraints are then considered as power allocation problems. These problems are solved using Perron-Frobenius theory and geometric programming (GP). The objectives are to efficiently provide users with flexible date rates and reduce the outage probability and user transmit power. Typically, only the path loss is considered in determining the outage probability whereas path loss, multipath fading and lognormal shadowing are considered in this paper along with the interference from other users. Results are presented which show that the proposed power allocation schemes provide better performance than the target SINR tracking power control (TPC), opportunistic power control (OPC), and temporary removal and feasibility check power control (DFC) algorithms.

Performance analysis and SINR‐based power allocation strategies for downlink NOMA networks

This study analyses the performance of downlink non-orthogonal multiple-access (NOMA) networks over independent but not necessarily identically distributed Rayleigh fading channels by deriving closed-form expressions for the average signal-to-interference-plus-noise ratio (SINR), average achievable rate, and outage probability of all network users. Moreover, SINR-based power allocation strategies are studied. Specifically, the optimisation problems of max–min SINR, multi-objective SINR, sum-SINR and proportional-fairness-SINR maximisation under minimum SINR constraints are formulated and shown to be non-convex. Additionally, the problems of total power and sum-SINR minimisation are also formulated. Particularly, the formulated problems take the form of linear-fractional programming problems. By applying intelligent reformulation techniques, these problems have been reformulated into convex optimisation problems, and thus efficiently solved. Numerical evaluations are presented to validate the derived closed-form expressions for the different performance metrics, which are found to be in agreement with the network simulation results. More importantly, it has been demonstrated that the user with the best channel conditions achieves full diversity order, whereas all the other users achieve diversity orders equivalent to their ordered channel conditions. Finally, the reformulated power allocation strategies are evaluated, and shown to coincide with the original problem formulations.

Improved ADMM-based algorithms for multi-group multicasting in large-scale antenna systems with extension to hybrid beamforming

In this paper, multi-group multicast beamforming is considered for the full digital and hybrid beamforming. The wireless system comprises of a multiple-antenna base station and single-antenna users. Quality-of-service (QoS)-aware design is investigated where the optimization objective is to minimize the total transmitted power subject to the signal-to-interference-plus-noise ratio (SINR) constraint at each user. In addition to the SINR constraints, per-antenna power constraint is included for each antenna of the base station. The original optimization problem for full digital beamforming is transformed into an equivalent form such that the alternating direction method of multipliers (ADMM) can be applied in an effective and computationally inexpensive manner for the large-scale antenna systems. In this new formulation, the beamformer weight vectors are decomposed into two subspaces in order to decrease the number of dual variables and multiplications. The optimum update equations are obtained for the proposed ADMM algorithm. This new reformulation is used for two different hybrid beamforming structures employing phase shifters and vector modulators. Optimum updates are derived for each system. The proposed algorithms decrease computational complexity of the existing ADMM algorithms due to the effective reformulation as well as the direct solution of the nonconvex problem. In the simulation results, it is shown that the proposed methods have better convergence behavior and less computational time than the benchmark algorithms. Furthermore, the proposed method for hybrid beamforming with vector modulators performs better than its counterpart in the literature in terms of transmitted power.

Design of spectrally compatible waveform with constant modulus for colocated multiple‐input multiple‐output radar

This study investigates the problem of designing unimodular waveform with spectral compatibility for multiple-input multiple-output radar in the presence of multiple targets and the signal-dependent interferences. A new approach is proposed to optimise the spectrally compatible radar waveform subject to not only the constant modulus constraint and the similarity constraint but also the signal-to-interference-plus-noise ratio (SINR) requirements for targets, aiming at suppressing the radar energy in some space-frequency areas occupied by other cooperative communication systems. However, the formulated optimisation problem is NP-hard because of the existence of the non-convex constraints. Based on the dual ascent framework, the authors develop an iterative algorithm to solve this challenging problem. The receive filter is obtained at the beginning of each iteration with the off-the-shelf technique and an innovative method termed phase-only dual ascent method is proposed to get the desired waveform. Additionally, they analyse the performance of the waveforms designed under the global SINR constraint and the local SINR constraint, respectively. Moreover, the convergence of the proposed algorithm is discussed concretely. Finally, the numerical simulations are provided to show the validity of the proposed approach.

Energy-efficient association mechanism designed for uplink CDMA-based HCNs

ABSTRACTTo reduce the energy consumption and prolong the battery lifetimes of mobile terminals, the design of energy-efficient association scheme is necessary and meaningful. To this end, we design a type of association mechanism (scheme) to maximize the whole energy efficiency (WEE) for uplink code-division-multiple-access (CDMA)-based heterogeneous cellular networks (HCNs), and formulate it as a WEE maximization problem with users’ SINR (signal-to-interference-plus-noise ratio) constraints. Considering that the finally formulated problem is in a fractional mixed-integer form and hard to tackle, we introduce an energy efficiency (EE) parameter to convert it into a parametric subtractive form. At last, we design two two-layer iterative algorithms for the formulated problem, whose primary difference lies in the treatment of SINR constraints. In general, the outer loop utilizes a bisection method to search the optimal EE parameter, and the inner loop alternately optimizes the association index and transmit power. At last, we give the corresponding convergence analyses for some subalgorithms, and also give some detailed complexity analyses for the whole algorithm. In the simulation, we introduce an existing energy-efficient association mechanism for comparison, and finally investigate the impacts of different network parameters on the association performance.

On Millimeter Wave 5G Backhaul Link Scheduling

Heterogeneous networks that consist of densely deployed base stations of different (macro and small-cell) tiers are envisioned to be among the key technologies for providing the high data rates required in 5G systems. Moreover, interconnecting small-cell base stations via millimeter-wave (mm-wave) backhaul links is a flexible and cost-effective alternative to fiber optic backhauling. Given a set of mm-wave backhaul links, this paper addresses the problem of scheduling all links in the minimum number of time slots such that the subset of links scheduled to each time slot can be activated simultaneously. In particular, a set of links can be activated simultaneously if all link signal-to-interference-plus-noise-ratio (SINR) targets are satisfied, and the number of RF chains of every base station is not exceeded. We give a succinct optimization-based formulation of the problem, capturing both SINR constraints and number of RF chains limitations. Using reduction from the set-cover problem, we devise a provably good polynomial-time algorithm for the problem. Our numerical results further indicate the significant superiority of the presented approach.

SINR Distributions for Robust MIMO TX-Power Minimization With Controlled Outage

We offer a simple and comprehensive alternative to existing robust network optimization methods as a solution to the long-standing problem of cellular network optimization under imperfect channel state information at the transmitter (CSIT) conditions. Specifically, instead of relying on a relaxation of statistical constraints into deterministic convex equivalents, as done in current literature and which leads to tradeoffs between robustness and optimality, we derive accurate models for the distributions of users’ signal-to-interference-plus-noise ratio (SINRs), conditioned on constraint-matching SINR estimates obtained under imperfect CSIT. Using these distributions, precise accounting and compensation for imperfection on CSIT, as well as statistical outage matching, can be achieved directly via the design of deterministic, instantaneous, and convex SINR constraints. Simulated comparison with the state of the art demonstrates the significant performance gain achieved by the proposed solution.