Amount Of Channel State Information Research Articles

Secure Physical Layer Transmission and Authentication Mechanism Based on Compressed Sensing of Multiple Antenna Arrays

Large-scale antenna technology has become one of the most promising technologies in 5G because of its ability to effectively improve the spectral efficiency and energy efficiency of the system, as well as its better robustness. In this paper, a large amount of CSI (channel state information) data is characterized by feature mining and law analysis, and a large number of channel characteristics of the physical layer have the advantages of randomness and uniqueness, etc. From the perspective of improving the security of the authentication mechanism and reducing the computational complexity of the authentication mechanism, the physical layer security authentication model is analyzed, and the signal security transmission path, signal authenticity, and channel estimation are used to propose an effective physical layer secure transmission and authentication mechanism, which can be used as a security enhancement and lightweight authentication mechanism for existing authentication mechanisms. In this paper, we analyze the security advantage mechanism of physical layer authentication and prove the security performance boundary; propose an authentication security enhancement method based on the channel feature generation key, a message authentication method based on superimposed tag signals; and propose a method based on private guide frequency for high-speed service data authentication.

Deep learning based user grouping for FD-MIMO systems exploiting statistical channel state information

The joint spatial division and multiplexing (JSDM) is a two-phase precoding scheme for massive multiple-input-multiple-output (MIMO) system under frequency division duplex (FDD) mode to reduce the amount of channel state information (CSI) feedback. To apply this scheme, users need to be partitioned into groups so that users in the same group have similar channel covariance eigenvectors while users in different groups have almost orthogonal eigenvectors. In this paper, taking the clustered user model into account, we consider the user grouping of JSDM for the downlink massive MIMO system with uniform planar antenna array (UPA) at base station (BS). A deep learning based user grouping algorithm is proposed to improve the efficiency of the user grouping process. The proposed grouping algorithm transfers the statistical CSI of all users into a picture, and utilizes the deep learning enabled objective detection model you look only once (YOLO) to divide the users into different groups rapidly. Simulation results show that the proposed user grouping scheme can achieve higher sum rate with less time delay.

Downlink Connection Density Maximization for NB-IoT Networks Using NOMA With Perfect and Partial CSI

We address the issue of maximizing the number of connected devices in a Narrowband Internet-of-Things (NB-IoT) network using nonorthogonal multiple access (NOMA) in the downlink. We first propose an optimal joint subcarrier and power allocation strategy assuming perfect channel state information (CSI) called stratified device allocation (SDA), which maximizes the connectivity under data rate, power, and bandwidth constraints. Then, we generalize the connectivity maximization problem to the case of partial CSI, where only the distance-dependent path-loss component of the channel gain is available at the base station (BS). We introduce a novel framework called the stochastic connectivity optimization (SCO) framework. In this framework, we propose a heuristic improvement to SDA, namely, SDA with excess power (SDA-EP) algorithm for operation under partial CSI. Furthermore, we derive a concave approximation (SCO-CA) algorithm of near-optimal performance to SCO given the same amount of CSI. Through computer simulations, we show that SDA-EP and SCO-CA outperform conventional NOMA and OMA schemes in the presence of partial CSI over a wide range of service scenarios.

СПОСОБЫ МИНИМИЗАЦИИ ОБЪЁМА ПЕРЕДАВАЕМОЙ ИНФОРМАЦИИ В ОБРАТНОМ КАНАЛЕ МНОГОАНТЕННЫХ СИСТЕМ MIMO

Multiple antenna systems MIMO (Multiple input multiple output) are widely used in LTE-Advanced mobile systems and in the IEEE 802.11 radioaccess standards. In the international standard of 3GPP for mobile communication systems 5G New Radio (Release 15), MIMO systems are regulated as a fundamental technology of the new Air interface. In this paper, we consider algorithms for quantization of information about the channel state using Grassmann manifolds, which significantly reduce the amount of channel state information required for transmission. The results of computer simulation allow to evaluate the noise immunity of precoding algorithms based on Grassmannian manifold quantization.

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.

Random Space–Time Line Code With Proportional Fairness Scheduling

As the demand for and services of the Internet of Things (IoT) increase, the dense deployment of a massive number of devices is expected in future IoT networks. Accordingly, an appropriate scheduling method will be required to fairly support the overwhelmingly large number of devices or users operating in practical environments. In this paper, we consider a single-cell downlink network in which one transmitter, such as an access point and base station, supports the massive number of users by using a space–time line code (STLC) scheme. Here, to resolve a prohibitively large amount of channel state information (CSI) feedback from the users to the transmitter, we propose a new STLC scheme called random STLC that uses a random vector for the STLC encoding. Furthermore, a proportional fairness (PF) scheduler is modified with a priority factor that is proportional to the devised alternative signal-to-interference-plus-noise ratio. By a comparison with an optimal STLC system that uses full CSI with a PF scheduler, we justify the benefit of the proposed random STLC with a PF scheduler using partial CSI for massive-user networks. Numerical results obtained from a rigorous simulation confirm that the proposed scheme can provide a comparable to achievable rate and fairness with massive number of users together with affordable feedback overhead in massive networks.

Sub-System SVD Hybrid Beamforming Design for Millimeter Wave Multi-Carrier Systems

In this paper, the hybrid beamforming design for multiple-input multiple-output orthogonal frequency-division multiplexing systems is studied over the indoor millimeter wave (mm-wave) channels. Under practical hybrid beamforming constraints for multi-carrier systems and the study of sub-systems, we propose the sub-system SVD (SS) hybrid beamforming design. To alleviate the complexity problem of channel estimation, reduction of the amount of the required channel state information (CSI) is also studied based on the statistical properties of indoor mm-wave channels. The SS hybrid beamforming algorithm is then extended to the limited feedback SS (LSS) scheme in which the transmit beamformers are designed based on limited information fed back from the receiver. A fast codeword selection algorithm is also developed to reduce the search complexity of the LSS algorithm. The simulation results show that our SS algorithm achieves the performance level of traditional full-digital beamforming with low complexity and the LSS algorithm has better performance and lower complexity than the previous algorithm, even if the amount of CSI is greatly reduced.

A Receiving Antenna Allocation Scheme for Downlink MU-MIMO-OFDM Transmission

This paper proposes a novel receiving antenna allocation scheme for downlink multiuser multiple input multiple output orthogonal frequency division multiplexing (MU-MIMO-OFDM) transmission; an access point (AP) simultaneously transmits data frames to a combination of allocated receiving antennas on a subcarrier basis at each station (STA). The proposed scheme combines a limited channel state information (CSI) feedback sequence with a receiving antenna decision method. In the proposed scheme, each STA estimates the channel responses of all receiving antennas by using the training preamble transmitted from an AP, and then feeds the channel response of the antenna with maximum norm back to the AP when the spatial correlation value between receiving antennas is higher than a threshold. Otherwise, each STA feeds full channel responses back to the AP. This scheme decreases the amount of CSI fed back while exploiting the spatial diversity gain, and the AP’s computational complexity is also decreased regarding the antenna allocation. Moreover, the receiving antenna decision method eliminates the overhead to notify the allocated antenna information from the AP to each STA by simply comparing its own receiving antenna powers. We clarify the effectiveness of the proposed scheme in our computer simulations using channel responses measured in an indoor environment. The results show that the proposed scheme maintains the channel capacity of the downlink MU-MIMO-OFDM transmission while greatly reducing the overhead and computational complexity.

Opportunistic Scheduling in Wireless Powered Communication Networks

In this paper, we apply the notion of opportunistic scheduling in wireless-powered communication networks (WPCNs). The considered WPCN model consists of a base station (BS) and multiple energy harvesting users (EHUs), where the BS broadcasts radio frequency energy to the EHUs over the downlink and receives information from the EHUs over the uplink. We differentiate the WPCNs based upon the battery management policy at the EHUs, i.e., whether an EHU spends the total amount of energy harvested in its battery for each IT (WPCN type 1), or spends only a part of it for the current IT and saves the other part for future ITs (WPCN type 2). We propose two opportunistic scheduling policies, referred to as the harvest-then-select and harvest-or-select protocols, employed at the WPCN type 1 and WPCN type 2, respectively. These protocols have significant practical advantages over the state-of-the-art schemes proposed for maximizing the WPCN sum-rate, because they introduce fairness in the resource utilization by the EHUs, and require much lower amount of channel state information. Both protocols achieve these benefits at the expense of a minor rate degradation relative to the rates achieved by their counterpart protocols employing multiple access, denoted as the harvest-then-transmit and harvest-or-concurrently-transmit protocols.

An Opportunistic Spectrum Access Scheme for Multicarrier Cognitive Sensor Networks

In this paper, we consider a broadband cognitive radio wireless sensor network operating in a crowded spectrum scenario, where a sensor transmitter (i.e., a sensor node that needs to transmit information to another sensor node or to a sink) opportunistically uses the channel of a primary system adopting an underlay paradigm. Both the primary and sensor systems employ an orthogonal frequency-division multiplexing (OFDM) modulation scheme. The proposed approach is reminiscent of additive superposition coding: specifically, the sensor transmitter behaves as an amplify-and-forward relay for the primary system, by sending a linearly weighted combination of the received primary signal and its own block of symbols. Such a superposition is performed by the sensor transmitter on the fly, in a single OFDM symbol of the primary system. Moreover, the proposed scheme does not require spectrum sensing at the sensor nodes and does not demand any cooperation between primary and sensor systems, thereby accounting for the inherent resource constraints of sensor nodes. With reference to both primary and sensor communications, we address the problem of power allocation by considering different amounts of channel state information and develop a worst case analysis of the achievable information rates. The developed algorithms and the derived formulas, which are corroborated by numerical Monte Carlo simulations, allow one to recognize and discuss interesting tradeoffs between the main parameters of the cognitive radio sensor transmission.

Performance Analysis of Physical Layer Security of Opportunistic Scheduling in Multiuser Multirelay Cooperative Networks.

In this paper, we study the physical layer security (PLS) of opportunistic scheduling for uplink scenarios of multiuser multirelay cooperative networks. To this end, we propose a low-complexity, yet comparable secrecy performance source relay selection scheme, called the proposed source relay selection (PSRS) scheme. Specifically, the PSRS scheme first selects the least vulnerable source and then selects the relay that maximizes the system secrecy capacity for the given selected source. Additionally, the maximal ratio combining (MRC) technique and the selection combining (SC) technique are considered at the eavesdropper, respectively. Investigating the system performance in terms of secrecy outage probability (SOP), closed-form expressions of the SOP are derived. The developed analysis is corroborated through Monte Carlo simulation. Numerical results show that the PSRS scheme significantly improves the secure ability of the system compared to that of the random source relay selection scheme, but does not outperform the optimal joint source relay selection (OJSRS) scheme. However, the PSRS scheme drastically reduces the required amount of channel state information (CSI) estimations compared to that required by the OJSRS scheme, specially in dense cooperative networks.

Convolutive Superposition for Multicarrier Cognitive Radio Systems

Recently, we proposed a spectrum-sharing paradigm for single-carrier cognitive radio (CR) networks, where a secondary user (SU) can maintain or even improve the performance of a primary user (PU) transmission, while also obtaining a low-data rate channel for its own communication. According to such a scheme, when the SU is able to receive the signal transmitted from the PU, it can superimpose its transmission on the PU signal by simple multiplicative precoding, without requiring any cooperation between the primary and secondary systems. The scope of this paper is to extend such a paradigm to a multicarrier CR network, where the PU employs an orthogonal frequency-division multiplexing (OFDM) modulation scheme. To improve its achievable data rate, the SU is allowed to transmit multiple symbols in parallel over the OFDM subcarriers of the primary system. In particular, the SU convolves its block-precoded symbols with the received PU signal in the time domain, which gives rise to the term convolutive superposition . An information-theoretic analysis of the proposed scheme is developed, which considers different amounts of channel state information at the secondary transmitter, as well as different precoding strategies for the SU. Extensive simulations illustrate the merits of our analysis and designs, in comparison with other CR schemes, by using as a performance indicator the ergodic channel capacity.

Adaptive limited feedback for interference alignment in MIMO interference channels.

It is very important that the radar sensor network has autonomous capabilities such as self-managing, etc. Quite often, MIMO interference channels are applied to radar sensor networks, and for self-managing purpose, interference management in MIMO interference channels is critical. Interference alignment (IA) has the potential to dramatically improve system throughput by effectively mitigating interference in multi-user networks at high signal-to-noise (SNR). However, the implementation of IA predominantly relays on perfect and global channel state information (CSI) at all transceivers. A large amount of CSI has to be fed back to all transmitters, resulting in a proliferation of feedback bits. Thus, IA with limited feedback has been introduced to reduce the sum feedback overhead. In this paper, by exploiting the advantage of heterogeneous path loss, we first investigate the throughput of IA with limited feedback in interference channels while each user transmits multi-streams simultaneously, then we get the upper bound of sum rate in terms of the transmit power and feedback bits. Moreover, we propose a dynamic feedback scheme via bit allocation to reduce the throughput loss due to limited feedback. Simulation results demonstrate that the dynamic feedback scheme achieves better performance in terms of sum rate.

A Soft-Network-Coded Multilevel Forwarding Scheme for Multiple-Access Relay Systems

This paper proposes the novel technique of multilevel-threshold-based soft quantization (MLT-SQ) for a multiple-access relay system (MARS). The scheme is suitable for systems using binary phase-shift keying (BPSK) and network coding at the relay. In the proposed MLT-SQ protocol, the relay evaluates the reliabilities, which are expressed as log-likelihood ratios (LLRs), of the received signals from the two sources. It then computes the LLRs of the network-coded packet and quantizes these using a set of optimized multilevel thresholds, forwarding the resulting “ quantized soft symbols ” to the destination. We provide the derivation for the bit error rate (BER) at the destination, and based on this, we optimize the multilevel thresholds to minimize the BER. Simulation results are provided for the proposed MLT-SQ system, both without coding and for the case where low-density parity-check (LDPC) coding is employed. The proposed system achieves full diversity order. Compared with competing schemes, the performance of our system is superior in terms of BER when the same amount of channel state information (CSI) is exploited.

Limited-Feedback Resource Allocation in Heterogeneous Cellular Networks

In this paper, we develop and analyze three limited-feedback resource allocation algorithms suitable for uplink transmission in heterogeneous wireless networks (HetNets). In this setup, one macro-cell shares the spectrum with a set of underlay cognitive small-cells via the orthogonal frequency-division multiple access (OFDMA), where the interference from small-cells to the macro-cell should be kept below a predefined threshold. The resource allocation algorithms aim to maximize the weighted sum of instantaneous rates of all users over all cells by jointly optimizing power and subcarrier allocation under power constraints. Since in practice, the HetNet backhaul capacity is limited, reducing the amount of channel state information (CSI) feedback signaling passed over the backhaul links is highly desirable. To reach this goal, we apply the Lloyd algorithm to develop the limited-feedback two-phase resource allocation scheme. In the first offline phase , an optimal codebook for power and subcarrier allocation is designed and sent to all nodes. In the second online phase , based on channel realizations, the appropriate codeword of the designed codebook is chosen for transmission parameters, and the macro-cell only sends the codeword index represented by a limited number of bits for subcarrier and power allocation to its own users and small-cells. Then, each small-cell informs its own users by their related codewords. The offline phase involves a mixed-integer nonconvex resource allocation problem encountering high computational complexity. To solve it efficiently, we apply the general iterative successive convex approximation (SCA) approach, where the nonconvex optimization problem is transformed into the approximated convex optimization problem in each iteration. The simulation results reveal that the Lloyd algorithm can offer a performance close to the perfect-CSI case (without a limited number of feedback bits).

Time-Domain Compression Based Analog Feedback for MIMO-OFDM Systems

We investigate the analog feedback of channel state information (CSI) in multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) based wideband wireless communication systems. In MIMO-OFDM systems, the amount of CSI to be fed back grows prohibitively with the number of subcarriers. A straightforward way of reducing the overhead is to feed back a sampled version of the CSI. But sampling based feedback suffers from the aliasing effect in some scenarios. We proved that the aliasing effect can not be eliminated unless the system feeds back more subcarrier channel matrices than the length of the time-domain channel impulse response. To improve the feedback quality and cope with the aliasing problem, we make use of the sparsity of the time-domain channel impulse response and propose a time-domain compression based analog feedback scheme. The proposed scheme overcomes the the aliasing problem, and achieves good performance.

Outage Analysis for Underlay Cognitive Networks Using Incremental Regenerative Relaying

Cooperative relay technology has recently been introduced into cognitive radio (CR) networks to enhance the network capacity, scalability, and reliability of end-to-end communication. In this paper, we investigate an underlay cognitive network where the quality of service (QoS) of the secondary link is maintained by triggering an opportunistic regenerative relaying once it falls under an unacceptable level. Analysis is conducted for two schemes, referred to as the channel-state information (CSI)-based and fault-tolerant schemes, respectively, where different amounts of CSI were considered. We first provide the exact cumulative distribution function (cdf) of the received signal-to-noise ratio (SNR) over each hop with colocated relays. Then, the cdf's are used to determine a very accurate closed-form expression for the outage probability for a transmission rate R. In a high-SNR region, a floor of the secondary outage probability occurs, and we derive its corresponding expression. We validate our analysis by showing that the simulation results coincide with our analytical results in Rayleigh fading channels.

Dynamic radio resource and interference management for MIMO–OFDMA mobile broadband wireless access systems

A multi-user multi-cell Multiple-Input Multiple Output/Orthogonal Frequency Division Multiple Access (MIMO/OFDMA) system for next generation Broadband Wireless Access (BWA) networks is studied, in which the Base Station (BS) or evolved Node B (eNB) has only knowledge of the statistics of the channel. A combination of MIMO and OFDMA could increase the spectral efficiency in a high speed network. We investigate methods with scalable channel feedback and we analyze the trade off between the amount of Channel State Information (CSI) to the transmitter and the system performance. The proposed schemes with limited feedback are combined with other cell interference reduction strategies based on cooperation for improving the performance of a coordinated multi-cell system under very dynamic conditions like high velocity and fast fading. Simulation results demonstrate that substantial gain is obtained by the proposed schemes which take advantage of the statistical information of the highly dynamic channel.

MIMO Transceiver Designs for Spatial Sensing in Cognitive Radio Networks

We propose transceiver algorithms in cognitive radio networks where the cognitive users are equipped with multiple antennas. Prior work has focused on the design of precoding matrices to suppress interference to the primary receivers. This work considers designs of precoding and decoding matrices for spatial sensing to achieve two objectives: (i) to prevent interference to the primary receivers and (ii) to remove the interference, due to primary transmissions, at the secondary receiver. With single antenna primary terminals and two antenna cognitive terminals, a linear transceiver design has been introduced under a global channel state information (CSI) assumption . In this letter, multiple antenna primary and cognitive terminals and three different CSI scenarios depending upon the amount of CSI are studied: (i) local CSI, (ii) global CSI, and (iii) local CSI with side information. When local CSI is available, we leverage prior work and employ the projected-channel singular value decomposition (P-SVD). In the global CSI scenario, we propose a joint transmitter-receiver design under the assumption of full CSI of all the users at the secondary transceiver. To reduce the feedback overhead, we also propose a new iterative algorithm that exploits only local CSI with side information. In this algorithm, the secondary transmitter and receiver iteratively update precoding and decoding matrices based on the local CSI and side information (precoding/decoding matrices at the previous iteration step) to maximize the rate of the secondary link while maintaining the zero-interference constraint. Convergence is established in the special case of single stream beamforming. Numerical results confirm that the proposed joint design and the iterative algorithm show better achievable rate performance than the P-SVD technique at the expense, respectively, of CSI knowledge and side information.

Cognitive Radio Protocols Based on Exploiting Hybrid ARQ Retransmissions

This paper addresses user coexistence in cognitive radio systems by taking advantage of opportunities that arise during ARQ retransmission. It is shown that if these opportunities are properly exploited, nontrivial rates can be made available to a secondary (cognitive) pair while impinging little or no interference on the primary pair. This can be accomplished with an oblivious primary system and without assuming any non-causal information at the secondary about the primary data. Several protocols are devised that work with varying amounts of channel state information about the cognitive and primary links. The protocols are further extended to the scenario where multiple cognitive receivers exist. Performance analysis of the protocols is presented and their effectiveness is verified via simulations.