Channel State Information Model Research Articles

Physiological Motion Sensing via Channel State Information in NextG Millimeter-Wave Communications Systems

Wireless communications systems provide channel state information (CSI), which can be used to characterize the physical propagation environment. The small physiological motion of human subjects in that environment, such as that associated with respiration, can modulate the CSI, thus allowing wireless physiological sensing through which a communications system detects and monitors cardiopulmonary motion. NextG millimeter-wave communications systems present even greater opportunities for wireless physiological sensing due to the advantages of small wavelength and high directionality. But challenges also arise due to the multipath effect and phase aliasing caused by larger-than-wavelength motion displacement. This work introduces a comprehensive mathematical CSI model to accurately characterize physiological motion captured by the amplitude and phase of the CSI of a millimeter-wave communications system. The model allows for the interpretation of intricate CSI pattern variations to avoid aliasing error and has been validated with experiments involving both parametric measures conducted with a robotic mover and respiration rate measurements for human subjects. In all cases, rate measurements could be consistently resolved within 10%, or 0.02 Hz, demonstrating the potential for incorporating physiological sensing in NextG millimeter-wave communications systems.

Interplay Between Interference-Aware Resource Allocation Algorithm Design, CSI, and Feedback in Underlay D2D Networks

A key problem in underlay device-to-device (D2D) systems is assigning cellular users and D2D users to subchannels to improve spatial reuse while controlling the interference they cause to each other. We present a unified treatment of this problem for two practically motivated partial and statistical channel state information (CSI) models with quantized feedback. They differ in the CSI available at the D2D receiver. In both models, the nodes only have statistical information of inter-D2D and inter-cell interferences, and employ fractional power control. We present two polynomial-time algorithms to assign multiple D2D pairs to subchannels, namely, relaxation-pruning algorithm (RPA) and cardinality-constrained subchannel assignment algorithm (CCSAA). RPA and CCSAA guarantee a D2D sum rate that is at least one-half and one-third, respectively, of the optimal sum rate. We also propose a novel statistical rate upgradation technique that exploits the allocation information to improve the D2D rates. We observe that inter-D2D interference has a more pronounced effect in the statistical CSI model. The algorithms respond differently to the two CSI models. RPA outperforms CCSAA in the partial CSI model, while CCSAA outperforms RPA in the statistical CSI model despite its weaker performance guarantee.

Robust Beamforming Design and Time Allocation for IRS-Assisted Wireless Powered Communication Networks

In this paper, a novel intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) architecture is proposed for power-constrained Internet-of-Things (IoT) smart devices, where IRS is exploited to improve the performance of WPCN under imperfect channel state information (CSI). We formulate a hybrid access point (HAP) transmit energy minimization problem by jointly optimizing time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient under the imperfect CSI and non-linear energy harvesting model. On account of the high coupling of optimization variables, the formulated problem is a non-convex optimization problem that is difficult to solve directly. To address the above-mentioned challenging problem, alternating optimization (AO) technique is applied to decouple the optimization variables to solve the problem. Specifically, through AO, time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient are divided into three sub-problems to be solved alternately. The difference-of-convex (DC) programming is used to solve the non-convex rank-one constraint in solving IRS energy reflection coefficient and information reflection coefficient. Numerical simulations verify the superiority of the proposed optimization algorithm in decreasing HAP transmit energy compared with other benchmark schemes.

Tractable Modelling and Robust Coordinated Beamforming Design With Partially Accurate CSI

Coordinated Beamforming (CB) is an effective technique to maximize weighted sum rate (WSR) targeting at on-demand network capacity. However, the channel state information (CSI) of the base stations (BSs) far away can’t be measured accurately, which will lead to the invalidation of the traditional WSR-maximization beamformer design and eventually result in capacity decrease. This letter proposes a tractable user-centric CSI model and a robust CB design to maximize WSR with partially accurate CSI. Specifically, the CSI model of the coordinated BSs is built for each user, where the inaccurate CSI of BSs far away from this user is modeled according to its static statistic characteristic. To derive a robust beamformer, deterministic equivalent method is adopted to obtain an approximate closed-form expression of the ergodic sum rate. Moreover, the performance of the robust CB design with different model parameters is considered to provide guidance for practical network deployment. Simulation results show that the proposed robust CB design has 30% capacity gain through careful interference management compared with traditional beamforming.

Interference alignment in MIMO interference broadcast channels with imperfect CSI

In this study, the authors propose four interference alignment (IA) schemes for multiple-input multiple-output (MIMO) interference broadcast channels (IBCs) under imperfect channel state information (CSI). They propose two new algorithms based on minimising the leakage of the interference signals under a generalised imperfect CSI model from two new points of view. The first algorithm is based on alternating minimisation algorithm. The second proposed algorithm is an improved version of minimum weighted leakage interference algorithm with a faster convergence rate. Inspired by the CSI uncertainty model, the authors present two robust IA algorithms based on exploiting stochastic properties of the CSI mismatch. The first one is based on joint signal and IA which reduces the leakage of the interference signals outside the interference subspace and forces the intended signal to fall into the orthogonal complement of the interference subspace. The second algorithm maximises the signal-to-interference-plus-noise ratio (SINR) in a per-user-based approach, while the existing maximum SINR (MSINR) algorithm in literature is a per-stream-based approach. The authors illustrate that the proposed MSINR algorithm requires less channel information and has less computational costs compared to the existing MSINR. The superiority of the proposed schemes over the existing algorithms are confirmed by simulation examples.

Energy Efficient and Robust Beamforming for MISO Cognitive Small Cell Networks

This paper studies a cognitive small cell downlink network, where one cognitive base station (CBS) transmits information to a cognitive user and transfers energy to energy harvesting receivers (EHRs). The spectrum sensing interval, the spectrum sensing time, and the beamforming matrices of the CBS are jointly optimized to maximize the energy efficiency (EE) of the CBS and to minimize the energy cost of the CBS under both the bounded channel state information (CSI) model and the probabilistic CSI model. The interference constraints of the macrocell users, the secrecy rate constraint, the transmit power constraint of the CBS and the energy harvesting constraints of the EHRs are all considered in this paper. All the three formulated optimization problems are nonconvex, for which semidefinite relaxation, a 2-D line search method, S-Procedure, and Bernstein-type inequalities are exploited. This paper also derives the conditions under which the EE maximization problem and the energy cost of the CBS minimization problem using the bounded CSI model have rank-one solutions. Simulation results demonstrate that the proposed algorithms have significant gain on the CBS EE under the perfect CSI and also gain on the CBS energy cost under imperfect CSI, all compared to the benchmark scheme.

Max–min fair beamforming designs of SWIPT-aided full-duplex two-way relay systems

A full-duplex two-way relaying (FD-TWR) system with an energy-constrained relay is studied with simultaneous wireless information and power transfer (SWIPT). In this network, the sources have a reliable power supply, whereas the relay has to harvest energy from the source-emitted radio-frequency signal via time switching (TS) operation. Our aim is to maximize the minimum achievable rate of the two sources by jointly optimizing the relay beamforming vector and TS ratio under the energy-harvesting-then-transmitting mode, i.e., the relay harvested energy need to meet the consumed energy. Specifically, the max–min fairness beamforming design problems are formulated for both the perfect channel state information (CSI) model and imperfect CSI model. A one-dimensional search algorithm and the semidefinite relaxation technique are proposed to solve these complex non-convex problems. It is shown that the optimal beamforming vectors and the TS ratio can always be found under the two considered CSI model. Numerical results show that the proposed two FD schemes achieve significant performance improvement compared to the traditional half-duplex (HD) scheme. Moreover, the robust scheme can get most of the performance gain of the perfect scheme. Some tradeoffs are elucidated among the achievable rate and the TS ratio, the system performance and the self-interference cancellation (SIC) level under a max–min fairness criterion.

Independent Power Splitting for Interference-Corrupted SIMO SWIPT Systems

In this letter, the design and performance of independent power splitting (PS) is investigated for interference-corrupted SIMO simultaneous wireless information and power transfer (SWIPT) systems. Namely, each antenna is connected to an individual power splitter at the receiver, and the corresponding PS factors for different antennas are jointly optimized to provide the more favorable tradeoff between the achieved data rate and harvested energy. Besides perfect channel state information (CSI) model, imperfect CSI model is also considered for the interference channel to evaluate the performance gain more practically. By proving the convexity of the considered two problems, the optimal algorithms are proposed to derive the achievable rate–energy (R–E) regions. Through simulations, it is shown that the R–E region increases notably by adopting independent PS, especially when the signal processing noise is large at the receiver. Moreover, when the energy demand is high, interference signal is beneficial and can enhance the achievable data rate significantly.

Channel inversion and regularization revisited

In multiuser multiple-input-single-output (MISO) downlink, linear precoders like channel inversion (CI) and regularized CI (RCI) are more desirable than their nonlinear counterparts due to their reduced complexity. To achieve the full benefits of linear precoding, the availability of perfect channel state information (CSI) at base stations (BSs) is necessary. Since in practice, having access to perfect CSI is not pragmatic, in this paper, we evaluate the performance of CI and RCI under a generalized, imperfect CSI model where the variance of the channel estimation error depends on the signal-to-noise ratio (SNR) and thus covers digital and analog feedbacks as two special cases. Then, based on this imperfect CSI model, we quantify the asymptotic mean loss in sum rate and the achievable degrees of freedom (DoFs) by deriving the received signal-to-interference-plus-noise ratio (SINR) of each user. For example, it is shown that the achievable DoF is directly related to the SNR exponent of the channel estimation error variance. Also, two asymptotic gaps to capacity for the analog feedback are derived: mean loss in sum rate and power loss. In addition, we propose an adaptive RCI technique by deriving an appropriate regularization parameter as a function of the error variance and without imposing any restrictions on the number of users or antennas. It is shown that in the presence of CSI mismatch, while the comparative improvement of the standard RCI to CI becomes negligible, the adaptive RCI compensates this degraded performance of the standard RCI without introducing any extra computational complexity.

MIMO Capacity Analysis Using Adaptive Semi Blind Channel Estimation with Modified Precoder and Decoder for Time Varying Spatial Channel

Multiple Input Multiple Output(MIMO) has been in much importance in recent past because of high capacity gain over a single antenna system. In this article, analysis over the capacity of the MIMO channel systems with spatial channel with modified precoder and decoder has been considered when the channel state information (CSI) is considered partial. Due to delay in acquiring transmitted information at the receiver end, the time selective fading wireless channel often induces incomplete or partial CSI. The dynamic CSI model has also been implemented consisting channel mean and covariance which leads to extracting of channel estimates and error covariance which then further applied with the modified precoder and decoder utilizing both the parameters indicating the CSI quality since these are the functions of temporal correlation factor, and based on this, the model covers data from perfect to statistical CSI, either partially or fully blind. It is found that in case of partial and imperfect CSI, the capacity depends on the statistical properties of the error in the CSI which has been manipulated according to the precoder and decoder conditions. Based on the knowledge of statistical distribution of the deviations in CSI knowledge, a new approach which maximizes the capacity of spatial channel model with modified precoder and decoder has been tried. The interference then interactively reduced by employing the iterative channel estimation and data detection approach, where by utilizing the detected symbols from the previous iteration, multiuser/MIMO channel estimation and symbol detection is improved.

Capacity Analysis of MIMO Spatial Channel Model using Novel Adaptive Semi Blind Estimation Scheme

Multiple Input Multiple Output (MIMO) has been remained in much importance in recent past because of high capacity gain over a single antenna system. In this article, analysis over the capacity of the MIMO channel systems with spatial channel has been considered when the channel state information (CSI) is considered imperfect or partial. The dynamic CSI model has also been tried consisting of channel mean and covariance which leads to extracting of the channel estimates and error covariance. Both parameters indicated the CSI quality since these are the functions of temporal correlation factor, and based on this, the model covers data from perfect to statistical CSI, either partial or full blind. It is found that in case of partial and imperfect CSI, the capacity depends on the statistical properties of the error in the CSI. Based on the knowledge of statistical distribution of the deviations in CSI knowledge, a new estimation approach which maximizes the capacity of spatial channel model has been tried. The interference interactively reduced by employing the iterative channel estimation and data detection approach.

Achievable Rates with Imperfect Transmitter Side Information Using a Broadcast Transmission Strategy

rdquoWe investigate the performance of the broadcast approach for various fading distributions, which correspond to different models of partial transmit channel state information (CSI). The first model considered is the quantized limited feedback. In this model, the receiver can send as feedback only a finite number of bits describing the fading gain. We derive the optimal power allocation for the broadcast approach for the quantized feedback model. For a Rayleigh fading channel, numerical results here show that if the feedback word can be longer than one bit, the broadcasting gain becomes negligible, due to diminished channel uncertainty. The second partial transmit CSI model is a stochastic Gaussian model with mean and variance information, which is commonly used for modeling the channel estimation error. In a single-input single-output (SISO) channel, this model also corresponds to the Ricean fading distribution, for which we derive maximal achievable broadcasting rates. We further consider a multiple-input single-output (MISO) channel, and derive the optimal power allocation strategy in a broadcast approach. Numerical results here show that uniform power allocation is preferable over beamforming power allocation in the region where broadcasting gain over single level coding is non-negligible.

Adaptive MIMO-OFDM Based on Partial Channel State Information

Relative to designs assuming no channel knowledge at the transmitter, considerably improved communications become possible when adapting the transmitter to the intended propagation channel. As perfect knowledge is rarely available, transmitter designs based on partial (statistical) channel state information (CSI) are of paramount importance not only because they are more practical but also because they encompass the perfect- and no-knowledge paradigms. In this paper, we first provide a partial CSI model for orthogonal frequency division multiplexed (OFDM) transmissions over multi-input multi-output (MIMO) frequency-selective fading channels. We then develop an adaptive MIMO-OFDM transmitter by applying an adaptive two-dimensional (2-D) coder-beamformer we derived recently on each OFDM subcarrier, along with an adaptive power and bit loading scheme across OFDM subcarriers. Relying on the available partial CSI at the transmitter, our objective is to maximize the transmission rate, while guaranteeing a prescribed error performance, under the constraint of fixed transmit-power. Numerical results confirm that the adaptive 2-D space-time coder-beamformer (with two basis beams as the two strongest eigenvectors of the channel's correlation matrix perceived at the transmitter) combined with adaptive OFDM (power and bit loaded with M-ary quadrature amplitude modulated (QAM) constellations) improves the transmission rate considerably.