Channel State Information Feedback Delay Research Articles

Deep Learning for Parametric Channel Estimation in Massive MIMO Systems

Massive Multiple-Input Multiple-Output (MIMO) communication with a low bit error rate depends upon the availability of accurate Channel State Information (CSI) at the base station. The massive MIMO systems can be either deployed using time division duplexing with channel reciprocity assumption or by availing frequency division duplexing, which requires closed-loop feedback for CSI acquisition. The channel reciprocity simplifies transmission in time division duplexing; however, it suffers a bottleneck due to pilot contamination, whereas transmission in frequency division duplexing is challenged by channel estimation complexity, CSI feedback, and overall delay in CSI transfer. This paper proposes a simplified parametric channel model, its deep neural network aided estimation along with pilot decontamination for time division duplexing and a low rate parametric feedback and improved precoding for frequency division duplexing based massive MIMO systems. This novel framework integrates the massive MIMO parametric estimation and deep learning for improved estimation and precoding. Our proposed model also offers a unified approach for CSI acquisition with a performance bound on channel correlation in fast time-varying conditions. A theoretical model has been presented using Gaussian assumptions and validated by Monte-Carlo simulations. The results show total nullification of pilot contamination and high-performance gains when the proposed technique is employed for estimation.

CA Energy Saving Joint Resource Optimization Scheme Based on 5G Channel Information Prediction of Machine Learning

Carrier aggregation (CA) is considered as a key enabling technology to provide higher rates for users in LTE/5G networks. However, the increased transmission rate is accompanied by higher energy consumption. The existing CA energy efficiency resource optimization allocation scheme in 5G does not fully consider the following two issues, namely, the impact of delayed channel state information feedback on the rationality of resource allocation and the increasing in energy consumption caused by frequent switching of component carriers (CCs) by narrowband users; this paper proposed a CA energy-efficient joint resource optimization allocation (PEJA) scheme based on channel information prediction. The proposed scheme (PEJA) fully considers the above two issues. Firstly, the algorithm of random forest predicting channel state information is designed. On the basis, the CA energy-efficient joint resource optimization allocation (PEJA) scheme based on channel information prediction is proposed. The simulation results show that the proposed algorithm PEJA has a higher energy efficiency and throughput than the comparison algorithm under different numbers of users and different transmission powers. The PEJA algorithm is more energy efficient than the PEJA-NC algorithm, which does not consider the CC handover of narrowband users. To sum up, the proposed PEJA energy-efficient resource allocation scheme maximizes system energy efficiency and achieves a higher throughput.

Coordinated Beamforming for UAV-Aided Millimeter-Wave Communications Using GPML-Based Channel Estimation

In the 5th generation (5G) networks, coordinated multiple point (CoMP) is one of key technologies to improve the quality of service (QoS) of edge users. To meet the requirement of growing data rates, millimeter-wave (mmWave) can be employed in the CoMP system. However, the QoS of users may be degraded if line-of-sight (LoS) mmWave channels are not guaranteed. In this article, an unmanned aerial vehicle (UAV)-aided communication scheme is proposed to enhance the QoS of edge users, where the UAV helps a primary base station (BS) and a coordinated BS simultaneously. In the proposed scheme, since the UAV only feeds back the channel state information (CSI) to the primary BS, the CSI obtained at the coordinated BS through a backbone network becomes outdated. In order to overcome the performance loss caused by the CSI feedback delay, a machine learning based channel estimation scheme is studied for the coordinated BS to perform hybrid beamforming. Furthermore, to eliminate the inter-BS interference, a maximize signal to interference-plus-noise ratio (Max-SINR) based beamforming compensation scheme is proposed for the primary BS and UAV. The simulation results show that both the bit error rate (BER) and sum rate performance can be improved by employing the proposed schemes.

Deep Learning Based Predictive Power Allocation for V2X Communication

As an essential technology of the fifth generation communication (5G), Vehicle-to-Everything (V2X) has attracted wide attention lately. A well-designed power allocation scheme can decrease the interference among terminals to yield a significant performance gain for V2X communication. Accurate channel state information (CSI) feedback, which is crucial to power allocation, is hard to be tracked due to fast time-varying channel in V2X scenario. This paper investigates power allocation problem under delayed CSI feedback in V2X system. We focus on maximizing sum throughput of V2X system while meeting Quality of Service (QoS) constraint requirement of each link. First of all, a power allocation scheme utilizing projective constraint analysis (PCA) is proposed to guarantee reliability of V2X network and improve system throughput, namely PCA-PA. Subsequently, we develop a Deep Neural Network (DNN) based predictive power allocation algorithm, namely DNN-PPA. This algorithm aims to address delayed CSI feedback in V2X system utilizing the normalized data obtained from PCA-PA scheme. Furthermore, a V2X communication model compliant with the 3rd Generation Partnership Project (3GPP) standard is simulated to validate the performances of PCA-PA and DNN-PPA. Simulation results illustrate that PCA-PA has superior performances compared to existing power allocation approaches and the throughput performance of DNN-PPA is impressively close to optimal solution under delayed CSI feedback.

A Precoding Compensation Scheme for Heterogeneous Communication Networks With CSI Feedback Delay

In the 5th generation cellular systems, heterogeneous communication networks allow multiple base stations (BSs) to serve the users cooperatively, which can satisfy the requirement of growing data rate and optimum coverage. However, suffering from the imperfect channel state information (CSI) feedback, optimal performance may not be guaranteed for coordinated multiple point (CoMP) scenarios when certain users are served by multiple BSs, simultaneously. To this end, it is essential to develop transmission schemes with imperfect CSI to improve the performance in CoMP systems. Under the circumstance that an edge user is served by a primary BS and coordinated BS with the same time-frequency resource, at which the CSI can be outdated at the coordinated BS, in this paper, we propose a precoding compensation scheme at the primary BS to eliminate the interference caused by outdated CSI at the coordinated BSs. Two algorithms, namely, maximum signal-to-interference-plus-noise ratio (MaxSINR) and minimum-mean-square-error (MMSE), are proposed for the precoding at the transmitter and the detection at the receiver, respectively, to further improve the performance as well as mitigate the interference. Simulation results show that our proposed transmission scheme is able to provide sufficiently good performance comparing to that of the perfect CSI feedback scenario.

D2D-Based Vehicular Communication With Delayed CSI Feedback

Device-to-device (D2D) communications can combine the superiorities of both the cellular network and the Ad hoc network, and it is an efficient way to improve the capacity of short distance communication. D2D communications underlaying a cellular network can support vehicle-to-vehicle (V2V) communications. Channel state information (CSI) is crucial to the performance optimization of network. However, it is difficult to obtain the accurate CSI in the highly mobile vehicular environment. CSI of vehicular links is reported to the BS periodically. The delayed CSI feedback exists in the links of V2V and vehicle-to-CUE. The delayed message and the dynamic environment in the V2V network result in the uncertainty of channel gain. In order to improve the reliability of transmission, the vehicular delay time and the speed information are considered in the channel model. The sum-rate maximization problem under uncertainty constrains is formulated, and successive convex approximation is adopted to make the non-convex problem solvable. Two different forms of uncertain channel model are analyzed, and they are converted to the same form of optimization problem. The robust power allocation algorithm is given. The simulation results show the feasibility of the proposed scheme.

Spectrum and Power Allocation for Vehicular Communications With Delayed CSI Feedback

Channel state information (CSI) at the base station (BS) is critical to resource allocation design for wireless networks, but it is hard to obtain accurate CSI in a high mobility vehicular environment. In this letter, we study the spectrum and power allocation problem in device-to-device-enabled vehicular networks, where CSI of vehicular links is only reported to the BS periodically. We maximize the sum throughput of all vehicle-to-infrastructure (V2I) links while guaranteeing the reliability of each vehicle-to-vehicle (V2V) link with the delayed CSI feedback. We propose a low-complexity algorithm to find the optimal spectrum sharing strategy among V2I and V2V links and properly adjust their transmit powers. Its desirable performance is confirmed by computer simulation.

The Degrees of Freedom Region of the Cognitive Interference Channel With Delayed Channel State Information Feedback

This paper studies two-user cognitive multiple-input multiple-output (MIMO) interference channel with delayed channel state information at transmitter (CSIT), where a transmitter possesses noncasual knowledge of data originating at the other user. We first derive an upper bound of degrees-of-freedom (DoF) region and then propose DoF bound achieving schemes for various antenna configurations. Since interference condition and interference suppression capability vary with antenna configurations, the proposed DoF optimal schemes differently utilize cognitive transmission based on delayed CSIT to suppress interference and thus differ in the required numbers of time slots and transmit symbols, although the key idea of swapping interfering signals between users is common. Comparing with the DoF regions of relevant channels, we identify and analyze the contributions of cognitive transmission and delay CSIT toward enlarging the DoF region. Our analysis specifies antenna configurations where delayed CSIT and cognitive transmission are useful, respectively.

Overview of Relay Selection and dynamic resource Allocation for Cooperative Wireless Network

In the past decades, increasing demand for wireless multimedia and interactive Internet services, along with rapid proliferation of a multitude of communications and computational gadgets, are fuelling intensive research efforts on the design of novel wireless communication systems architectures for high speed, reliable and cost effective transmission solutions. Cooperative relay communication is a new paradigm shift for the next generation wireless system that will guarantee high data rates to all users in the network. It is anticipated to be the key technology aspect in the fifth generation wireless networks. In this Paper, we use system level simulations to gain insight into and quantify the influence of channel state information feedback delay in a multi-cell cooperative wireless system, and focus is put in relay selection and resource allocation for its optimal use. We analyzed the interplay between scheduling, power control in a multi-cell environment when the channel state information is subject to feedback delay. Detailed analysis is carried out using MATLAB and subsequent results are thoroughly discussed for deeper insight into the dynamics of cooperative networks. General Terms OFDMA, Cooperative relay selection, resource allocation, LTE.

Adaptive Mode Selection in Multiuser MISO Cognitive Networks With Limited Cooperation and Feedback

In this paper, we consider a multiuser (MU) multiple-input-single-output (MISO) downlink cognitive network coexisting with a primary network. With the purpose of exploiting the spatial degree of freedom to counteract the internetwork interference and intranetwork (interuser) interference simultaneously, we propose performing zero-forcing beamforming (ZFBF) at the multiantenna cognitive base station (BS) based on the instantaneous channel state information (CSI). The challenge of designing ZFBF in cognitive networks lies in how to obtain the interference CSI. To solve it, we introduce a limited internetwork cooperation protocol, namely, the quantized CSI conveyance from the primary receiver to the cognitive BS via purchase. Clearly, the more the feedback amount, the better the performance, but the higher the feedback cost. To achieve a balance between the performance and feedback cost, we take the maximization of the feedback utility function (FUF), which is defined as the difference of the average sum rate and feedback cost while satisfying the interference constraint, as the optimization objective, and derive the transmission mode and feedback amount joint optimization scheme. Moreover, we quantitatively investigate the impact of CSI feedback delay and obtain the corresponding optimization scheme. Furthermore, through asymptotic analysis, we present some simple schemes. Finally, numerical results confirm the effectiveness of our theoretical claims.

Space-Time Interference Alignment and Degree-of-Freedom Regions for the MISO Broadcast Channel With Periodic CSI Feedback

This paper characterizes the degree-of-freedom (DoF) regions for the multiuser vector broadcast channel with periodic channel state information (CSI) feedback. As a part of the characterization, a new transmission method called space-time interference alignment is proposed, which exploits both the current and past CSI jointly. Using the proposed alignment technique, an inner bound of the sum-DoF region is characterized as a function of a normalized CSI feedback frequency, which measures CSI feedback speed compared to the speed of user's channel variations. One consequence of the result is that the achievable sum-DoF gain is improved significantly when a user sends back both current and outdated CSI compared to the case where the user sends back current CSI only. Then, a tradeoff between CSI feedback delay and the sum-DoF gain is characterized for the multiuser vector broadcast channel in terms of a normalized CSI feedback delay that measures CSI obsoleteness compared to channel coherence time. A crucial insight is that it is possible to achieve the optimal DoF gain if the feedback delay is less than a derived fraction of the channel coherence time. This precisely characterizes the intuition that a small delay should be negligible.

Two-Flow Capacity Region of the COPE Principle for Wireless Butterfly Networks With Broadcast Erasure Channels

This paper characterizes the full capacity region of the COPE principle for 2-flow wireless butterfly networks with broadcast packet erasure channels (PECs). The capacity results in this paper allow for random overhearing with arbitrary overhearing probabilities, arbitrary scheduling policies, network-wide channel state information (CSI) feedback after each transmission, and potential use of nonlinear network codes. An information-theoretic outer bound is derived that takes into account the delayed CSI feedback of the underlying broadcast packet erasure channels. For the achievability, this paper proposes a new class of linear network codes, named as the space-based linear network coding (SBLNC), that achieves the capacity outer bound. Further, the proposed outer and inner bounds are later generalized for the setting in which a transmission may be heard by its 2-hop neighbor(s), the so-called opportunistic routing scenario. When allowing the possibility of opportunistic routing, the proposed inner and outer bounds do not always meet. Numerical experiments, however, show that the relative gap of the two bounds is less than 0.08% in average. The proposed bounds thus tightly bracket the capacity region even when combining the COPE principle with opportunistic routing.

Outage Analysis of Opportunistic Scheduling in Dual-Hop Multiuser Relay Networks in the Presence of Interference

The outage probability of a multiuser two-hop amplify-and-forward relaying system employing opportunistic scheduling is investigated. A practically important case where there are cochannel interference signals present at the network is considered for the analysis. Exact expressions and closed-form lower bounds are derived for the outage probability. Exact closed-form expressions are derived for the system outage probability when interference signals are present at the relay and at the destination separately. A closed-form lower bound is derived for the outage probability when the relay and the destination nodes are affected by interference simultaneously. The effects of channel state information feedback delay are investigated for special cases. In addition, asymptotic outage probability results are derived to obtain useful insights on the effects of interference and feedback delay. The novel expressions can be used by practicing engineers to obtain reliable and realistic performance estimates for dual-hop multiuser relay networks. The results are useful for understanding the capabilities of the feedback channel required in this system.

Capacity bounds of transmit beamforming over MISO time-varying channels with imperfect feedback

In wireless multiple input single output (MISO) systems, transmit beamforming using feedback of quantized channel state information (CSI) can provide both diversity gain and array gain with simple implementation. Most of previous researches include several ideal assumptions, such as block-fading channels, perfect CSI at the receiver, or error-free and zero-delay feedback link. This paper investigates a more realistic Jakes’ time-varying MISO Rayleigh fading channel with CSI estimation error and feedback delay, derives the upper and lower bounds on the ergodic capacity, and obtains the optimal frame length according to the capacity lower bound given the normalized feedback bits. The numerical and simulation results show that the derived upper and lower bounds on the capacity is quite close, increasing channel estimation error or Doppler spread will reduce the capacity bounds, and the optimal frame length can maximize the capacity lower bound.

Cross-Layer-Model Based Adaptive Resource Allocation for Statistical QoS Guarantees in Mobile Wireless Networks

We propose a cross-layer-model based adaptive resource-allocation scheme for the diverse quality-of-service (QoS) guarantees over downlink mobile wireless networks. Our proposed scheme dynamically assigns power-levels and time- slots for heterogeneous real-time mobile users to satisfy the variation of statistical delay-bound QoS requirements. To achieve this goal, we apply Wu and Negi's effective capacity approach to derive the admission-control and power/time-slot allocation algorithms, guaranteeing the statistical delay-bound for heterogeneous mobile users. When designing such an algorithm, we study the impact of physical-layer issues such as adaptive power-control and channel-state information (CSI) feedback delay on the QoS provisioning performance. Through numerical and simulation results, we observe that the adaptive power adaptation has a significant impact on statistical QoS-guarantees. In addition, the analyses indicate that our proposed resource-allocation algorithms are shown to be able to efficiently support the diverse QoS requirements for various real-time mobile users over different wireless channels. Also, in an in-door mobile environment, e.g., the widely used wireless local-area networks (WLAN), our proposed algorithm is shown to be robust to the CSI feedback delay.