Assumption Of Channel State Information Research Articles

Performance of Multi-Antenna Proactive Eavesdropping in 5G Uplink Systems

This paper studies the performance of multi-antenna proactive eavesdropping in 5G uplink systems with spatially correlated Rayleigh fadings, where the base station (BS) serves an illegal user and a multi-antenna legitimate monitor eavesdrops the suspicious link with the help of jamming attack. Based on a practical assumption of channel state information (CSI) in 5G uplink, i.e., imperfect instantaneous CSI of the suspicious link at the BS and that of eavesdropping link at the monitor, and only jamming channel statistics at the monitor, we first give a statistical jamming beamforming design. Then, semi-closed form expressions of eavesdropping non-outage probability and relative average eavesdropping rate are, respectively, derived for delay-sensitive and delay-tolerant scenarios. Via reasonable approximations and asymptotic analysis, we gain many insights on the effect of key system parameters, e.g., location-dependent channel path loss and angular spread, jamming energy and the number of antennas. Further, we provide the optimal energy allocation between pilot and data jamming and the optimal antenna allocation between jamming and eavesdropping under total energy constraint and total antenna number, respectively, which are both explicit functions of system parameters. Finally, simulation results validate our analytical results.

Optimized Design for IRS-Assisted Integrated Sensing and Communication Systems in Clutter Environments

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system design in a clutter environment. Assisted by an IRS equipped with a uniform planar array (UPA), a multi-antenna base station (BS) is targeted for simultaneously sensing multiple targets in the non-light-of-sight (NLoS) region and communicating with multiple communication users (CUs). We consider the joint IRS-assisted ISAC design in the case with Type-I or Type-II CUs, where each Type-I CU and Type-II CU can and cannot cancel the interference from sensing signals, respectively. Under the perfect communication/sensing channel state information assumption, we aim to maximize the minimum sensing beampattern gain among multiple targets, where the sensing beampattern gain qualifies the achieved illumination signal power at the given location of the target of interest. We jointly optimize the BS’s communication-sensing beamformers and the IRS’s phase shifting matrix, subject to the signal-to-interference-plus-noise ratio (SINR) constraint for each Type-I/Type-II CU, the interference power constraint per clutter, the transmission power constraint at the BS, and the cross-correlation pattern constraint. Due to the design variable coupling, the joint IRS-assisted ISAC design problem is shown to be non-convex in the case with Type-I or Type-II CUs. To circumvent the non-convexity dilemma, we propose semidefinite relaxation (SDR) based alternating optimization algorithms in both cases, for which the computational complexity and convergency behavior are analyzed. In the case with Type-I CUs, we show that the dedicated sensing signal at the BS can help enhance the sensing performance gain. By contrast, the dedicated sensing signal at the BS is not required for the IRS-assisted ISAC designs in the case with Type-II CUs. Numerical results are provided to show that the proposed IRS-assisted ISAC design schemes achieve a significant gain over the existing benchmark schemes.

Beamforming Design for In-Band Full-Duplex Multi-Cell Multi-User MIMO Networks With Global and Local CSI

This paper investigates beamforming techniques for in-band full-duplex multi-cell multi-user (IBFD-MCMU) wireless networks considering hardware impairments (HWIs), channel uncertainty, and limited channel state information (CSI). For the global CSI scenario, we first enhance zero-forcing (ZF), maximum ratio transmission and combining (MRTC), and minimum mean-squared error (MMSE) beamforming to be compatible with multi-antenna users and IBFD base stations. Then, we investigate beamforming schemes for the local CSI assumption where only intra-cell channel knowledge is fully available at the base stations, and inter-cell channels are known statistically due to the limited training resources. With these limitations, two MMSE-based methods are proposed, which regard unknown CSI as random instances (i.e., eMMSE-RI) and noise (i.e., eMMSE-N). We explore the self-interference cancellation (SIC) capability of beamformers and evaluate the performance of these methods in 3GPP scenarios. Numerical results reveal that the enhanced MMSE beamforming can achieve the desired IBFD with low analog SIC depth, inspiring a low-cost IBFD transceiver design. The practical imperfections (e.g., transceiver HWIs, channel uncertainty, limited CSI) decrease the achievable sum rate but do not reduce the IBFD gain. With CSI limitations, eMMSE-RI outperforms eMMSE-N for microcells, where the probability of the presence of LOS is high, whereas eMMSE-N performs better when cell size is enlarged.

Integer Forcing Interference Management for the MIMO Interference Channel

A new interference management scheme based on integer forcing (IF) receivers is studied for the two-user multiple-input and multiple-output (MIMO) interference channel. The proposed scheme employs a message splitting method that divides each data stream into common and private sub-streams, in which the private stream is recovered by the dedicated receiver only while the common stream is required to be recovered by both receivers. Specifically, to enable IF sum decoding at the receiver side, all streams are encoded using the same lattice code. Additionally, the number of common and private streams of each user is carefully determined by considering the number of antennas at transmitters and receivers, the channel matrices, and the effective signal-to- noise ratio (SNR) at each receiver to maximize the achievable rate. Furthermore, we consider various assumptions of channel state information at the transmitter side (CSIT) and propose low-complexity linear transmit beamforming suitable for each CSIT assumption. The achievable sum rate and rate region are analytically derived and extensively evaluated by simulation for various environments, demonstrating that the proposed interference management scheme strictly outperforms the previous benchmark schemes in a wide range of channel parameters due to the gain from IF sum decoding.

Physical-layer security enhancement using artificial noise for D2D communication in vehicular platooning networks

In this article, we introduce physical-layer security enhancement technique based on artificial noise for secure device-to-device (D2D) communications within a platoon in vehicular platooning networks. In particular, we consider a scenario that two D2D-enabled platoons are on the same way. All the vehicles in one platoon are used to generate artificial noise to enhance the secrecy of D2D communications within the other platoon against information leakage to a potential eavesdropping vehicle. We propose artificial noise generation strategies by the platoon according to the assumption on channel state information (CSI): full-CSI and partial CSI assumptions. We further analyze the effect of artificial noise depending on the relationship between the number of vehicles in two platoons. We finally evaluate the performance of our proposed schemes in terms of secrecy rate through extensive simulations and provide useful intuitions for secure platooning.

Reconfigurable Intelligent Surface Assisted Massive MIMO With Antenna Selection

Antenna selection is capable of reducing the hardware complexity of massive multiple-input multiple-output (MIMO) networks at the cost of certain performance degradation. Reconfigurable intelligent surface (RIS) has emerged as a cost-effective technique that can enhance the spectrum-efficiency of wireless networks by reconfiguring the propagation environment. By employing RIS to compensate for the performance loss due to antenna selection, in this paper we propose a new network architecture, i.e., RIS-assisted massive MIMO system with antenna selection, to enhance the system performance while enjoying a low hardware cost. This is achieved by maximizing the channel capacity via joint antenna selection and passive beamforming while taking into account the cardinality constraint of active antennas and the unit-modulus constraints of all RIS elements. However, the formulated problem turns out to be highly intractable due to the non-convex constraints and coupled optimization variables, for which an alternating optimization framework is provided, yielding antenna selection and passive beamforming subproblems. The computationally efficient submodular optimization algorithms are developed to solve the antenna selection subproblem under different channel state information assumptions. The iterative algorithms based on block coordinate descent are further proposed for the passive beamforming design by exploiting the unique problem structures. Moreover, the proposed algorithms are feasible to any finite number of antennas, and thus can be applicable in both ordinary MIMO and massive MIMO settings. Experimental results will demonstrate the algorithmic advantages and desirable performance of the proposed algorithms for RIS-assisted massive MIMO systems with antenna selection.

Energy Efficient Power Control for Cognitive Multibeam-Satellite Terrestrial Networks With Poisson Distributed Users

With great potential to alleviate spectrum scarcity in the next generation wireless communication, cognitive satellite terrestrial networks (CSTNs) have attracted considerable attention recently. To realize efficient spectrum sharing between satellite and terrestrial networks, in this paper, we propose an energy efficient power control scheme for CSTNs, where a multibeam satellite network acts as the secondary system and the cellular network acts as the primary system. Especially, satellite users and primary base stations are spatially Poisson distributed and the channel state information (CSI) of satellite interference link is assumed to be outdated. We firstly analyze the statistical characteristic of the terrestrial aggregate interference with probabilistic stochastic geometry. On this basis, to guarantee reliable communications for both networks, we formulate the power control scheme as an energy efficiency maximization problem subjected to interference power constraints imposed by terrestrial communications and outage constraints of satellite communications. Further, by employing the Dinkelbach and Lagrange duality method, we solve the nonlinear concave optimization problem and derive the optimal solution of the transmit power. Finally, simulation results demonstrate the validity of the theoretical results, reveal the trade-off between the performance of two networks, present the impacts of aggregate interference on the performance of satellite networks, and show the performance superiority of considering outdated CSI compared with perfect CSI assumption in terms of interference violation probability.

Distributed robust power control in two-tier vehicle networks under uncertain channel environments

This paper proposes a novel optimization scheme to support stable and reliable vehicle-to-everything connections in two-tier networks, where the uplink channel of the cellular user is reused by underlay vehicle-to-vehicle communications. However, considering complex channel fading and high-speed vehicle movement, the certainty assumption is impractical and fails to maintain power control strategy in reality in the traditional statical vehicular networks. Rather than the perfect channel state information assumption, the first-order Gauss-Markov process which is a probabilistic model affected by vehicle speed and fading is introduced to describe imperfect channel gains. Moreover, interference management is a major challenge in reusing communications, especially in uncertain channel environments. Power control is an effective way to realize interference management, and optimal power allocation can ensure that interference of the user meets the communication requirements. In this study, the sum-rate-oriented power control scheme and minimum-rate-oriented power control scheme were implemented to manage interference and satisfy different design objectives. Since both of these schemes are non-convex and intractable, the Bernstein approximation and successive convex approximation methods were adopted to transform the original problems into convex ones. Furthermore, a novel distributed robust power control algorithm was developed to determine the optimal solutions. The performance of the algorithm was evaluated through numerical simulations, and the results indicate that the proposed algorithm is effective in vehicular communication networks with uncertain channel environments.

On Performance of Energy Harvested Cooperative NOMA Under Imperfect CSI and Imperfect SIC

With the advent of 5G and the need for energy-efficient massively connected wireless networks, in this work, we consider an energy harvesting (EH) based multi-relay downlink cooperative non-orthogonal multiple access (NOMA) system with practical constraints. The base station serves NOMA users with the help of decode-and-forward based multiple EH relays, where relays harvest the energy from the base station's radio frequency. A relay is selected from the multiple K-relay by using a partial relay selection protocol. The system is considered to operate in half-duplex mode over a generalized independent and identical Nakagami$-m$ fading channel. The closed-form expression of outage probability and ergodic rate are derived for users, under the assumption of imperfect channel state information (CSI) and imperfect successive interference cancellation (SIC) at the receiver node. Expression of outage probability and ergodic rate for both users under the assumption of perfect CSI and perfect SIC are also presented. Further, the asymptotic expression for the outage probability is also shown. The derived analytical expressions are verified through Monte-Carlo simulations.

Bit-Interleaved Coded Multiple Beamforming With Perfect Coding in Millimeter-Wave MIMO Systems

This letter investigates bit-interleaved coded multiple beamforming (BICMB) with perfect coding in millimeter-wave (mm-wave) multiple-input multiple-output (MIMO) systems to achieve the maximum diversity gain. Using perfect coding with BICMB enables us to do this. We show that by using BICMB and perfect coding, the diversity gain becomes independent of the number of transmitted data streams and the number of antennas in each remote antenna unit (RAU) at the transmitter and the receiver. The assumption is that the perfect channel state information (CSI) is known at both the transmitter and the receiver. With the assumption of the perfect CSI at the transmitter and the receiver, simulation results show that the use of BICMB with perfect coding results in the diversity gain values predicted by the analysis.

Robust Beamforming Design for Intelligent Reflecting Surface Aided MISO Communication Systems

Perfect channel state information (CSI) is challenging to obtain due to the limited signal processing capability at the intelligent reflection surface (IRS). This is the first work to study the worst-case robust beamforming design for an IRS-aided multiuser multiple-input single-output (MU-MISO) system under the assumption of imperfect CSI. We aim for minimizing the transmit power while ensuring that the achievable rate of each user meets the quality of service (QoS) requirement for all possible channel error realizations. With unit-modulus and rate constraints, this problem is non-convex. The imperfect CSI further increases the difficulty of solving this problem. By using approximation and transformation techniques, we convert the optimization problem into a squence of semidefinite program (SDP) subproblems that can be efficiently solved. Numerical results show that the proposed robust beamforming design can guarantee the required QoS targets for all the users.

Deep Reinforcement Learning-Based Resource Allocation and Power Control in Small Cells With Limited Information Exchange

In multi-user downlink small cell networks, cooperative resource allocation (RA) within a small cell cluster is a key technique to enhance network capacity. However, capacity-maximizing RA in frequency-selective fading channels requires global channel state information (CSI) of users within a small cell cluster, which makes it infeasible in practical networks with limited direct link capacity. To circumvent this global CSI assumption, most of the existing studies on RA have been based on several CSI assumptions such as local CSI and local CSI at the transmitters (CSIT). Nevertheless, cost functions with local CSI or local CSIT in the literature rely on heuristic formulations, because the sum-rate cannot be computed if without global CSI. In this paper, we propose a deep reinforcement learning-based RA algorithm to maximize the sum-rate for any given limited information on instantaneous CSI or sum-rate at the previous period. The proposed scheme is not restricted to certain CSI assumptions, but attempts to find the best RA for any given information such as quantized local CSI and quantized local CSIT; thus, it is applicable to any given direct link capacity. The proposed algorithm is self-adaptive in time-varying channels, since it is not divided into training and test phases. We modify the target neural network (TNN) scheme to enhance the sum-rate and the convergence speed. Numerical simulations confirm that: i) the proposed algorithm outperforms the conventional algorithms even under the same CSI assumption such as local CSI and local CSIT; ii) a flexible trade-off between the amount of CSI and the sum-rate is realizable in practical systems.

Robust Hybrid Beamforming in Millimeter Wave Systems With Closed-Form Least-Square Solutions

Robust hybrid beamforming is developed for millimeter wave multiple-input multiple-output (MIMO) systems. The channel state information (CSI) with correlated estimation errors is used for averaging the mutual information formulation. It is proved that the robust hybrid beamforming scheme can be solved via an Euclidean distance minimization problem as the design with the perfect CSI assumption. An alternating method is proposed to solve the hybrid processors with closed-form least-square solutions in each iteration. Simulation results show that the proposed beamforming scheme prevails existing hybrid beamforming designs.

Secure polar coding with delayed wiretapping information

In this study, the authors investigate the secure coding issue for a wiretap channel model with fixed main channel and varying wiretap channel, by assuming that legitimate parties can obtain the wiretapping channel state information (CSI) with some delay. For the symmetric degraded delay CSI case, they present an explicit weak security scheme by constructing secure polar codes on a one-time pad chaining structure, and prove its weak security, reliability and capability of approaching the secrecy capacity for perfect CSI case with delay CSI assumption. Further for the symmetric no-degraded delay CSI case, they present a modified multi-block chaining structure in which the original subset of frozen bit is designed for conveying functional random bits securely. Then they combine this modified multi-block chaining structure with the weak security scheme to construct an explicit strong security polar coding scheme, and prove its strong security, reliability and also the capability of approaching the secrecy capacity for perfect CSI case with delay CSI assumption. At last, they carry out simulations to prove the performance of both secure schemes.

Bit-Interleaved Coded Multiple Beamforming in Millimeter-Wave Massive MIMO Systems

An analysis of the diversity gain for bit-interleaved coded multiple beamforming (BICMB) method in millimeter-wave (mm-Wave) massive multiple-input multiple-output (MIMO) systems is carried out for both the single-user and multi-user scenarios. We show that full diversity gain and full spatial multiplexing order can be achieved in both scenarios. Also, we show that the diversity gain in the multi-user scenario is independent of the number of users in the system. The assumption here is that the channel state information (CSI) is known at both sides of the transmitter and the receiver and the number of antennas in each remote antenna unit (RAU) tends to infinity. Based on these assumptions, the diversity gain for the single-user scenario is (Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i,j</sub> β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ij</sub> /) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i,j</sub> β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ij</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ij</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> where L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ij</sub> is the number of propagation paths and β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ij</sub> is the large scale fading coefficient between the ith RAU in the transmitter and the jth RAU in the receiver. The diversity gain in the multi-user scenario for the kth user is M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">kj</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> where M represents the number of RAUs at the transmitter. Simulation results show that when the perfect CSI assumption is satisfied, the use of BICMB results in the diversity gain values predicted by the analysis.

Interference Alignment in Relay-Aided Networks with Imperfect CSIT

In this paper, a relay-aided interference alignment (RIA) strategy was considered based on the benefits offered by relay-based cooperative networks. Along with the benefits offered by cooperative networks, the RIA was shown as a practical solution for a K-user BC network. The relationship between the achievable DoF region of the RIA and the different levels of outdated channel state information (CSI) were derived. The trade-off between the NRIA and RIA were analysed and the specific trade-off region in each outdated CSI assumption was indicated in the numerical results. Through the achievable DoF numerical results, it was recognised that regarding achievable DoF, the RIA outperforms the NRIA in most of the outdated CSI scenarios.

Supervised-Learning for Multi-Hop MU-MIMO Communications With One-Bit Transceivers

This paper considers a nonlinear multi-hop multi-user multiple-input multiple-output (MU-MIMO) relay channel, in which multiple users send information symbols to a multi-antenna base station (BS) with one-bit analog-to-digital converters via intermediate relays, each with one-bit transceiver. To understand the fundamental limit of the detection performance, the optimal maximum-likelihood (ML) detector is proposed with the assumption of perfect and global channel state information (CSI) at the BS. This multi-user detector, however, is not practical due to the unrealistic CSI assumption and the overwhelming detection complexity. These limitations are addressed by presenting a novel detection framework inspired by supervised-learning. The key idea is to model the complicated multihop MU-MIMO channel as a simplified channel with much fewer and learnable parameters. One major finding is that, even using the simplified channel model, a near ML detection performance is achievable with a reasonable amount of pilot overheads in a certain condition. In addition, an online supervised-learning detector is proposed, which adaptively tracks channel variations. The idea is to update the model parameters with a reliably detected data symbol by treating it as a new training (labelled) data. Lastly, a multi-user detector using a deep neural network is proposed. Unlike the model-based approaches, this model-free approach enables to remove the errors in the simplified channel model, while increasing the computational complexity for parameter learning. Via simulations, the detection performances of classical, model-based, and model-free detectors are thoroughly compared to demonstrate the effectiveness of the supervised-learning approaches in this channel.

Training Optimization and Performance of Single Cell Uplink System With Massive-Antennas Base Station

We study the performance of uplink transmission in single-cell wireless systems, where all the transmitters have single antennas and the base station has a large number of antennas. We consider both maximum ratio combining and zero-forcing receivers and both small- and large-scale fading channels. We also characterize the achievable total degrees of freedom (DoF) of such a system without assuming channel state information at the receiver. The system DoF turns out to be the same as that of a single-user multiple-input multiple-output system. However, when the number of users is the same as the number of receive antennas, linear receivers are not sufficient for achieving the maximum total DoF. The amount of energy savings that are possible through the increased number of base-station antennas or increased coherence interval are quantified. Furthermore, the training period and training energy allocation under the average and peak power constraints are optimized jointly to maximize the achievable sum spectral efficiency (SE). The improvement on achievable SE provided by the training duration and energy optimization is verified through multiple numerical simulations.

Massive MIMO Relay Networks With Underlay Spectrum Sharing

The achievable rates of multicell/multiuser massive multiple-input multiple-output (MIMO) relay networks with underlay spectrum sharing are investigated for imperfect/perfect channel state information (CSI). The transmit power constraints of the secondary user nodes and relays are derived. A max–min fairness optimal secondary transmit power control algorithm is formulated, and thereby, optimal power allocation coefficients and the maximum common achievable user rates are derived. The detrimental effects of intracell and intercell pilot contamination are studied for three pilot sequence designs (PSDs). Thereby, it is shown that for some PSDs and CSI assumptions, the secondary network can be operated at the peak average transmit power without degrading the performance of the primary network when the base-stations are equipped with massive MIMO. Consequently, the asymptotic achievable rates become independent of the primary interference temperature. For the worst-case scenario of full pilot reuse in the primary and secondary networks as well as in the co-channel cells, the achievable rates are severely degraded by intra-cell and inter-cell pilot contamination. Counter-intuitively, a significant portion of the achievable rate gains observed under the perfect CSI assumption can still be obtained by using a PSD with a fundamental trade-off between system performance and practical viability.

Loading-balance relay-selective strategy based on stochastic dynamic program

A Relay-Assisted (RA) network with relay selection is considered as a type of effective technology to improve the spectrum and energy efficiency of a cellular network. However, loading balance of the assisted relay node becomes an inevitable bottleneck in RA network development because users do not follow uniform distribution. Furthermore, the time-varying channel condition of wireless communication is also a major challenge for the RA network with relay selection. To solve these problems and improve the practicability of the RA network, a Loading Balance-Relay Selective (LBRS) strategy is proposed for the RA network in this paper. The proposed LBRS strategy formulates the relay selection of the RA network under imperfect channel state information assumption as a Multistage Decision (MD) problem. An optimal algorithm is also investigated to solve the proposed MD problem based on stochastic dynamic program. Numerical results show that the performance of the LBRS strategy is better than that of traditional greedy algorithm and the former is effective as an exhaustive search-based method.