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

Cellular localizability of unmanned aerial vehicles

To enable pervasive applications of cellular-connected unmanned aerial vehicles (UAVs), localization plays a key role. The successful reception of localization signals from multiple base stations (BSs) is the first step to localize targets, which is called cellular localizability. In this paper, we propose an analytical framework to characterize the B-localizability of UAVs, which is defined as the probability of successfully receiving localization signals above a certain signal-to-interference plus noise ratio (SINR) level from at least B ground BSs. Our framework considers UAV-related system parameters in a three-dimensional environment and provides a comprehensive insight into factors affecting localizability such as distance distributions, path loss, interference, and received SINR. We perform simulation studies to explore the relationship between localizability and the number of participating BSs, SINR requirements of the received localization signals, air-to-ground channel characteristics, and network coordination. We also formulate an optimization problem to maximize localizability and investigate the effects of UAV altitude in different scenarios. Our study reveals that in an urban macro environment, the effectiveness of cellular network-based localization increases with altitude, with localizability reaching 100% above 60 meters. This finding indicates that utilizing cellular networks for UAV localization is a viable option.

Intelligent reflecting surface aided co-existing radar and communication system

This paper explores the potential of intelligent reflecting surfaces (IRS) as a solution for enhancing radar detection and communication data transmission in crowded areas, where co-existing radar and communication (CRC) systems face distinct channel impairments. The proposed approach involves jointly optimizing the phase shift matrix of the IRS and the beamforming of the radar and communication systems, with the objective of maximizing the signal-to-interference-plus-noise ratio (SINR), while adhering to user-specified SINR requirements, radar transmit power, and base station (BS) power. To tackle this complex optimization problem, we introduce a Constrained Concave-Convex Procedure (CCCP) based Bisection Search (CCCP-BI) iterative algorithm. This algorithm utilizes CCCP to obtain the beamformers of the radar and communication systems, followed by the iterative Bisection Search (BI) to derive a solution for the PSM. Considering the computational complexity of CCCP-BI, we also propose an alternative Semi-Definite Programming (SDP)-based Bisection Search (BI-SDP) algorithm. Extensive simulations demonstrate the effectiveness of the IRS in the (CRC) system. Furthermore, we conduct performance comparisons with other benchmarks, evaluating the impact of various parameters on the received SINR at the radar receiver.

Joint Rate Allocation and Power Control for RSMA-Based Communication and Radar Coexistence Systems

We consider a rate-splitting multiple access (RSMA)-based communication and radar coexistence (CRC) system. The proposed system allows an RSMA-based communication system to share spectrum with multiple radars, which significantly improves spectral efficiency, energy efficiency and quality of service (QoS) of communication users (CUs). Due to the spectrum sharing, the communication network and the radars cause interference to each other, which reduces the signal-to-interference-plus-noise ratio (SINR) of the radars as well as the data rate of the CUs. Therefore, a major problem is to maximize the sum rate of the CUs while guaranteeing their QoS requirements of data transmissions and the SINR requirements of multiple radars. To achieve the objective, we formulate a new problem that optimizes i) common rates of the CUs, ii) transmit power of the common message of the CUs, iii) transmit power of the private messages for the intended CUs, and iv) transmit power of the radar systems. The problem is non-convex, and thus we propose two algorithms. The first sequential quadratic programming (SQP) as the state-of-the-art in nonlinear programming method can quickly return a local optimal solution. The second is an additive approximation scheme (AAS) which solves the problem globally in a reasonable amount of time. Simulation results show the significant improvement of the AAS compared with the SQP in terms of sum rate. Furthermore, with the AAS, the sum rate of the CUs only slightly decreases when the radars' SINR is significantly increased. This implies that the AAS supports the RSMA-based communication system which allows to well coexist with the radars.

Joint Transceiver Design for Dual-Functional Full-Duplex Relay Aided Radar-Communication Systems

Driven by the demand for massive and accurate sensing data to achieve wireless network intelligence under a limited available spectrum, the coexistence between radar and communication systems has attracted public attention. In this paper, we investigate a novel dual-functional full-duplex relay aided radar-communication system where the phased-array radar is employed at the amplify-and-forward (AF) relay. A joint transceiver design is proposed to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all detection directions at the radar receiver under communication quality-of-service and total energy constraints. The formulated optimization problem is particularly challenging due to the highly nonconvex objective function and constraints. Based on the problem structure, we equivalently decompose it into the radar-energy and relay-energy minimization problems under SINR requirements. To solve the radar-energy minimization problem, we propose a low-complexity algorithm based on the alternating direction method of multipliers to optimize the radar transmit power and receiver. The relay-energy minimization problem can be simplified into an equivalent quadratic programming problem by introducing an insightful unitary matrix. Then, the closed-form expression for the AF relay beamforming matrix can be derived, which is jointly determined by the channel condition of relay communication and the detection direction of the radar. After that, we introduce the overall transceiver design algorithm to the original problem and discuss its optimality and computational complexity. Simulation results verify that the proposed algorithm significantly outperforms other benchmark algorithms.

On the Pilot Contamination Attack in Multi-Cell Multiuser Massive MIMO Networks

In this paper, we analyze pilot contamination (PC) attacks on a multi-cell massive multiple-input multiple-output (MIMO) network with correlated pilots. We obtain correlated pilots using a user capacity-achieving pilot sequence design. This design relies on an algorithm which designs correlated pilot sequences based on signal-to-interference-plus-noise ratio (SINR) requirements for all the legitimate users. The pilot design is capable of achieving the SINR requirements for all users even in the presence of PC. However, this design has some intrinsic limitations and vulnerabilities, such as a known pilot sequence and the non-zero cross-correlation among different pilot sequences. We reveal that such vulnerabilities may be exploited by an active attacker to increase PC in the network. Motivated by this, we analyze the correlated pilot design for vulnerabilities that can be exploited by an active attacker. Based on this analysis, we develop an effective active attack strategy in the massive MIMO network with correlated pilot sequences. Our examinations reveal that the user capacity region of the network is significantly reduced in the presence of the active attack. Importantly, the SINR requirements for the worst-affected users may not be satisfied even with an infinite number of antennas at the base station.

Multi-Cell Interference Exploitation: Enhancing the Power Efficiency in Cell Coordination

In this paper, we propose a series of novel coordination schemes for multi-cell downlink communication. Starting from full base station (BS) coordination, we first propose a fully-coordinated scheme to exploit beneficial effects of both inter-cell and intra-cell interference, based on sharing both channel state information (CSI) and data among the BSs. To reduce the coordination overhead, we then propose a partially-coordinated scheme where only intra-cell interference is designed to be constructive while inter-cell is jointly suppressed by the coordinated BSs. Accordingly, the coordination only involves CSI exchange and the need for sharing data is eliminated. To further reduce the coordination overhead, a third scheme is proposed, which only requires the knowledge of statistical inter-cell channels, at the cost of a slight increase on the transmission power. For all the proposed schemes, imperfect CSI is considered. We minimize the total transmission power in terms of probabilistic and deterministic optimizations. Explicitly, the former statistically satisfies the users’ signal-to-interference-plus-noise ratio (SINR) while the latter guarantees the SINR requirements in the worst case CSI uncertainties. Simulation verifies that our schemes consume much lower power compared to the existing benchmarks, i.e., coordinated multi-point (CoMP) and coordinated-beamforming (CBF) systems, opening a new dimension on multi-cell coordination.

Optimized Power Control Scheme for Global Throughput of Cognitive Satellite-Terrestrial Networks Based on Non-Cooperative Game

In this paper, we investigate the power control problem for spectrum sharing in the cognitive satellite-terrestrial networks (CSTNs), aiming to maximize the throughput of global networks while meeting the requirements of signal to interference plus noise ratio (SINR) in the satellite links. Since the power control problem for global networks involves a mass of information collection or exchange, it is not conducive to centralized control in CSTNs. So we solve it by constructing a non-cooperative game with limited information exchange. First, we theoretically prove that the Nash equilibrium (NE) of the proposed game is consistent with the stationary point of the global throughput maximization problem. Then, we design a distributed power control (DPC) algorithm to obtain the NE of the game with guaranteeing the SINR demand of the satellite links. Considering that the precise channel state information (CSI) is often difficult to be obtained in actual communication scenarios, we propose a modified scheme of the DPC algorithm for the case of imperfect CSI. The modified scheme not only ensures that the SINR requirements of the satellite links can be met but also approximately converges to an NE of the problem with a little performance loss. Finally, the numerical results demonstrate that our scheme outperforms the existing typical algorithms in terms of the throughput of the global networks as well as the number of iterations.

Channel Access and Power Control for Energy-Efficient Delay-Aware Heterogeneous Cellular Networks for Smart Grid Communications Using Deep Reinforcement Learning

Cellular technology with long-term evolution (LTE)-based standards is a preferable choice for smart grid neighborhood area networks due to its high availability and scalability. However, the integration of cellular networks and smart grid communications puts forth a significant challenge due to the simultaneous transmission of real-time smart grid data which could cause radio access network (RAN) congestions. Heterogeneous cellular networks (HetNets) have been proposed to improve the performance of LTE because HetNets can alleviate RAN congestions by off-loading access attempts from a macrocell to small cells. In this paper, we study energy efficiency and delay problems in HetNets for transmitting smart grid data with different delay requirements. We propose a distributed channel access and power control scheme, and develop a learning-based approach for the phasor measurement units (PMUs) to transmit data successfully by considering interference and signal-to-interference-plus-noise ratio (SINR) constraints. In particular, we exploit a deep reinforcement learning(DRL)-based method to train the PMUs to learn an optimal policy that maximizes the earned reward of successful transmissions without having knowledge on the system dynamics. Results show that the DRL approach obtains good performance without knowing the system dynamic beforehand and outperforms the Gittin index policy in different normal ratios, minimum SINR requirements and number of users in the cell.

A robust energy efficiency power allocation algorithm in cognitive radio networks

In order to solve the problem that traditional energy efficiency power allocation algorithms usually require the assumption of constant or perfect channel state information in cognitive radio networks (CRNs), which may lead to performance degradation in real systems with disturbances or uncertainties, we propose a robust energy efficiency power allocation algorithm for underlay cognitive radio (CR) systems with channel uncertainty in consideration of interference power threshold constraint and minimum target SINR requirement constraint. The ellipsoid sets are used to describe the channel uncertainty, and a constrained fractional programming for the allocation is transformed to a convex optimization problem by worst-case optimization approach. A simplified version of robust energy efficiency scheme by a substitutional constraint having lower complexity is presented. Simulation results show that our proposed scheme can provide higher energy efficiency compared with capacity maximization algorithm and guarantee the signal to interference plus noise ratio (SINR) requirement of each cognitive user under channel uncertainty.

Non‐cooperative game‐theoretic distributed power control technique for radar network based on low probability of intercept

Here, the problem of non-cooperative game-theoretic distributed power control is studied in a radar network system based on low probability of intercept (LPI) subject to the signal-to-interference-plus-noise ratio (SINR) constraint and the transmit power constraint of each radar, where all the radars in the network share the same frequency band. The objective is to improve the LPI performance by reducing the transmit power caused by some radars' SINRs over the specified threshold. First, a novel LPI performance-oriented utility function is defined as a metric to evaluate power control. Then, consider that radars in the network are self-interested to maximise their own utilities, the distributed power control problem is formulated as a non-cooperative game, and an iterative power control algorithm is proposed that converges quickly to the Nash equilibrium (NE) of the non-cooperative game. Finally, the existence and uniqueness of NE are proved analytically. Numerical simulation results are provided to demonstrate that, compared with other methods, the presented algorithm not only guarantees the minimum SINR requirements of all radars but also improves the LPI performance for radar network.

User Association Under SINR Constraints in HetNets: Upper Bound and NP-Hardness

This letter studies the multi-slot user association problem (MSA) under signal-to-interference-plus-noise ratio (SINR) constraints in heterogeneous cellular networks. MSA consists of associating a set of users to a set of base stations during a set of slots while guaranteeing the SINR requirements of the associated users. To solve MSA, we study its single-slot version, called single-slot user association problem (SSA). We provide an upper bound of the optimal solution of MSA using SSA and prove its tightness. Next, we simplify the NP-hardness proof and show that both the SSA and MSA are NP-hard using a new and simple polynomial-time reduction. Finally, we present the simulation results to illustrate the tightness of the upper bound.

Spectrally Compatible Waveform Design for MIMO Radar in the Presence of Multiple Targets

This paper investigates the problem of the spectrally compatible waveform design for multiple-input multiple-output (MIMO) radar in the presence of multiple targets and signal-dependent interference. Unlike the existing approaches to designing the waveform for a single target via output signal-to-interference-plus-noise ratio (SINR) maximization, a new method is proposed to deal with a more general problem, i.e., designing a spectrally compatible waveform for multiple targets, by minimizing the waveform energy of the overlayed space-frequency bands under constraints of waveform similarity and individual SINR requirements. Due to the existence of signal dependence, the formulated optimization problem is NP-hard. Toward this end, an iterative algorithm is developed. Concretely, at each iteration, the receive filter is first obtained with the aid of the minimum variance distortionless response method, then the transmit waveform is optimized by employing the dual ascent method. Both the convergence and asymptotic optimality of this algorithm are discussed. Numerical simulations are provided to demonstrate the effectiveness of the proposed approach.

ADMM-Based Robust Beamforming Design for Downlink Cloud Radio Access Networks

The cloud radio access network (C-RAN) has been considered as a promising network architecture to improve both spectrum efficiency and energy efficiency of current wireless networks. In this paper, we aim to address the channel uncertainty issue in the multi-input single-output C-RAN by studying the robust beamforming. We formulate the robust beamforming design problem with an aim to minimize the overall network power and backhaul cost while satisfying the each radio remote head power constraint and guaranteeing individual signal-to-interference-plus-noise ratio (SINR) requirements. The channel state information is assumed to be imperfect, and the additive channel state information error is modeled as Gaussian distributed variables. Formulated problem is hard to solve due to the non-convex $\ell _{0}$ -norm functions and channel uncertainty in the constraints. Two statistical approaches, named average approach and probability approach, are proposed to deal with SINR requirements when including the channel uncertainty. $\ell _{0}$ -norm approximation and a majorization-minimization algorithm are utilized to transform $\ell _{0}$ -norm problem into a series of semidefinite programing (SDP) problems. After that, we propose an alternating direction method of multipliers (ADMM)-based algorithm to solve each SDP problem. We introduce two auxiliary variables to reformulate the SDP problems in an ADMM form, which further ensure that solving the SDP problem can be decomposed to solve three convex subproblems. A subgradient algorithm, Karush–Kuhn–Tucker conditions, and a projected gradient method are applied to solve them, respectively. Simulation results verify that the proposed robust algorithm can significantly enhance the performance compared the non-robust case.

Joint Resource Allocation With Weighted Max-Min Fairness for NOMA-Enabled V2X Communications

To achieve more efficient spectrum utilization in the intelligent transportation system, non-orthogonal multiple access-enabled (NOMA-enabled) V2X communications have been emerging as a promising technology. In this paper, the resource allocation problem for NOMA-enabled V2X communications is investigated. For V2I links, in view of the user fairness and the different requirements of cellular users (CUEs), weighted max-min rate fairness for CUEs is applied, where both identical weights and different weights are considered. As for V2V users (VUEs), the minimum signal-to-interference-plus-noise ratio (SINR) requirements are imposed on the problem formulation. To solve the proposed optimization problem, it is decoupled into three stages: 1) power allocation for CUEs; 2) power control and subcarrier (SC) assignment for VUE pairs; 3) SC assignment and user clustering for CUEs. For the three stages, algorithms based on the Perron-Frobenius theorem, the Kuhn-Munkres algorithm, and the matching theory are proposed separately. Finally, an algorithm integrated the three stages is presented to obtain a joint solution. The convergence and the optimality of the proposed algorithms can be verified by theoretical analysis and simulations. Moreover, simulation results also indicate that each cluster of the optimal user clustering scheme is inclined to be occupied by the CUEs with diverse channel gains and the minimum weighted rate of all CUEs, $\tau $ , will decrease with the increase of the numbers of V2V links and the minimum SINR requirements of VUE pairs.

Joint mode selection and resource allocation in D2D communication based underlaying cellular networks

Device-to-Device (D2D) communication technology, under the standardization of third generation partnership project and a component of the evolving fifth generation architecture, is mainly aimed to increase system capacity and data rate via providing direct communications between end devices without the use of routing data through the network. Apart from the attracting features, due to the resource sharing between cellular user equipment (CUE) and D2D user equipment (DUE) in such communications, an efficient algorithm for resource and power allocation to DUE, especially for mobile users is necessary to maintain the performance. The current paper introduces a joint mode algorithm for mobile user to choose between cellular and D2D communications and thereby, analyzes resource allocation issues. We propose an efficient algorithm for mobility management of users based on their current connection modes. The locations of D2D pairs are estimated by Levenberg–Marquardt method based on the received signal strength (RSS) from different macrocells. Since the range of D2D communication is much shorter than that of cellular communication, in order to prevent ping-pong handoffs between cellular and D2D modes, we propose the estimation of the next RSS samples in cellular mode prior to switching to D2D mode. In both cellular and D2D modes, the allocated resource block (RB) is the one with the highest signal to interference plus noise ratio (SINR) in order to increase the throughput, under the condition of providing minimum SINR requirement of CUE. This is achieved via transmission power control of the D2D pair. For performance evaluation, we studied the effects of increasing velocity of D2D and cellular users, number of users, and SINR threshold. The results indicate that the proposed solution fairly manages the communication mode of mobile users and incurs improvement in system throughput.

Multi-Cell Multiuser Massive MIMO Networks: User Capacity Analysis and Pilot Design

We propose a novel pilot sequence design to mitigate pilot contamination in multi-cell multiuser massive multiple-input multiple-output networks. Our proposed design generates pilot sequences in the multi-cell network and devises power allocation at base stations (BSs) for downlink transmission. The pilot sequences together with the power allocation ensure that the user capacity of the network is achieved and the pre-defined signal-to-interference-plus-noise ratio (SINR) requirements of all users are met. To realize our design, we first derive new closed-form expressions for the user capacity and the user capacity region. Built upon these expressions, we then develop a new algorithm to obtain the required pilot sequences and power allocation. We further determine the minimum number of antennas required at BSs to achieve certain SINR requirements of all users. Numerical results are presented to corroborate our analysis and to examine the impact of key parameters, such as the pilot sequence length and the total number of users, on the network performance. A pivotal conclusion is reached that our design achieves a larger user capacity region than the existing designs and needs less antennas at the BS to fulfill the pre-defined SINR requirements of all users in the network than the existing designs.

Game theoretic approach for joint transmit beamforming and power control in cognitive radio MIMO broadcast channels

In this paper, we present a game-theoretic approach to the problem of joint transmit beamforming and power control in cognitive radio (CR) multiple-input multiple-output broadcast channels (MIMO-BCs), where the primary users (PUs) coexist with the secondary users (SUs) and share the same spectrum. The cognitive base station (CBS) is equipped with multiantenna and transmits independent data streams to several decentralized single-antenna terminals. Our design goal is to jointly adjust the beamformers and transmission powers according to individual SINR (signal-to-interference-plus-noise ratio) requirements in order to meet SINR balancing for CR MIMO-BCs. In this context, two problems need to be solved: (1) the design beamforming must enable a balancing of the SINR among all SUs for a fixed total power of CBS and (2) the total transmission power must be minimized while satisfying a set of SINR constraints for fixed beamformers. The proposed approach is an application of separable games, where beamforming vectors are modeled as beamforming subgame and power control is modeled as power control subgame. We then use the convex theory of noncooperative game to solve the optimalization problem. Finally, we propose an iterative algorithm to reach Nash equilibrium (NE) of the joint beamforming subgame and power control subgame. Numerical results are provided to validate the optimality and the convergence of the proposed algorithm.

Tight Probabilistic SINR Constrained Beamforming Under Channel Uncertainties

In downlink multi-user beamforming, a single basestation is serving a number of users simultaneously. However, energy intended for one user may leak to other unintended users, causing interference. With signal-to-interference-plus-noise ratio (SINR) being one of the most crucial quality metrics to users, beamforming design with SINR guarantee has always been an important research topic. However, when the channel state information is not accurate, the SINR requirements become probabilistic constraints, which unfortunately are not tractable analytically for general uncertainty distribution. Therefore, existing probabilistic beamforming methods focus on the relatively simple Gaussian and uniform channel uncertainties, and mainly rely on probability inequality based approximated solutions, resulting in conservative SINR outage realizations. In this paper, based on the local structure of the feasible set in the probabilistic beamforming problem, a systematic method is proposed to realize tight SINR outage control for a large class of channel uncertainty distributions. With channel estimation and quantization errors as examples, simulation results show that the SINR outage can be realized tightly, which results in reduced transmit power compared to the existing inequality based probabilistic beamformers.

Downlink User Capacity of Massive MIMO Under Pilot Contamination

Pilot contamination has been regarded as a main limiting factor of time division duplexing (TDD) massive multiple-input-multiple-output (Massive MIMO) systems, as it will make the signal-to-interference-plus-noise ratio (SINR) saturated. However, how pilot contamination will limit the user capacity of downlink Massive MIMO, i.e., the maximum number of users whose SINR targets can be achieved, has not been addressed. This paper provides an explicit expression of the Massive MIMO user capacity in the pilot-contaminated regime where the number of users is larger than the pilot sequence length. This capacity expression characterizes a region within which a set of SINR requirements can be jointly satisfied. The size of this region is fundamentally limited by the pilot sequence length. Furthermore, the scheme for achieving the user capacity, i.e., the uplink pilot training sequences and downlink power allocation, has been identified. Specifically, the generalized Welch bound equality sequences are exploited and it is shown that the power allocated to each user should be proportional to its SINR target. With this capacity-achieving scheme, the SINR requirement of each user can be satisfied and energy-efficient transmission is achieved in the large-antenna-size (LAS) regime. The comparison with two non-capacity-achieving schemes highlights the superiority of our proposed scheme in terms of achieving higher user capacity. Furthermore, for the practical scenario with a finite number of antennas, the actual antenna size required to achieve a significant percentage of the asymptotic performance has been analytically quantified.

Distributed Link Scheduling Under SINR Model in Multihop Wireless Networks

Link adaptation technologies, such as Adaptive Modulation and Coding (AMC) and Multiple-Input-Multiple-Output (MIMO), are used in advanced wireless communication systems to achieve high spectrum efficiency. Communication performance can be improved significantly by adaptive transmissions based on the quality of received signals, i.e., the signal-to-interference-plus-noise ratio (SINR). However, for multihop wireless communications, most link scheduling schemes have been developed under simplified interference models that do not account for accumulative interference and cannot fully exploit the recent advances in PHY-layer communication theory. This paper focuses on developing link scheduling schemes that can achieve optimal performance under the SINR model. One key idea is to treat an adaptive wireless link as multiple parallel virtual links with different signal quality, building on which we develop throughput-optimal scheduling schemes using a two-stage queueing structure in conjunction with recently developed carrier-sensing techniques. Furthermore, we introduce a novel three-way handshake to ensure, in a distributed manner, that all transmitting links satisfy their SINR requirements. We evaluate the proposed schemes through rigorous analysis and simulations.