Perfect Instantaneous Channel State Information Research Articles

Robust Precoding Design for Massive MIMO: An Efficient Fractional Programming-Based Approach

Due to the effects of channel aging and estimation errors, perfect instantaneous channel state information (CSI) is unavailable at a base station. Therefore, robust precoding under imperfect CSI is important for practical communications. In this letter, we propose an efficient robust precoding design for massive multiple-input multiple-output systems. Based on the fractional programming technique, we transform the original non-convex optimization problem into a much more tractable equivalent problem, which can be iteratively solved by alternating optimization. Since all variables are updated via closed-form optimal solutions, the proposed algorithm is guaranteed to converge to a locally optimal point. Simulation results reveal that the proposed robust precoding algorithm has fast convergence and achieves significant performance improvement over the conventional ones.

Learning-Based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks

This paper investigates the integrated sensing and communication (ISAC) in vehicle-to-infrastructure (V2I) networks. To realize ISAC, an effective beamforming design is essential which however, highly depends on the availability of accurate channel tracking requiring large training overhead and computational complexity. Motivated by this, we adopt a deep learning (DL) approach to implicitly learn the features of historical channels and directly predict the beamforming matrix to be adopted for the next time slot to maximize the average achievable sum-rate of an ISAC system. The proposed method can bypass the need of explicit channel tracking process and reduce the signaling overhead significantly. To this end, a general sum-rate maximization problem with Cramer-Rao lower bounds-based sensing constraints is first formulated for the considered ISAC system taking into account the multiple access interference. Then, by exploiting the penalty method, a versatile unsupervised DL-based predictive beamforming design framework is developed to address the formulated design problem. As a realization of the developed framework, a historical channels-based convolutional long short-term memory (LSTM) network (HCL-Net) is devised for predictive beamforming in the ISAC-based V2I network. Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance. Finally, simulation results show that the proposed predictive method not only guarantees the required sensing performance, but also achieves a satisfactory sum-rate that can approach the upper bound obtained by the genie-aided scheme with the perfect instantaneous channel state information available.

Hierarchical Reinforcement Learning for Relay Selection and Power Optimization in Two-Hop Cooperative Relay Network

In this paper, we study the outage probability minimizing problem in a two-hop cooperative relay network. To reduce outage probability, existing studies propose many schemes for relay selection and power allocation, which are usually based on the assumption of exact channel state information (CSI). However, it is difficult to obtain perfect instantaneous CSI in practical situations where channel states change rapidly, and thus traditional methods would not perform well. Considering these factors, we turn to the emerging reinforcement learning (RL) methods for solutions. RL methods do not need any prior knowledge of CSI, but use neural network for approximation and decision after interacting with communication environment. Nevertheless, conventional RL methods, including most deep reinforcement learning (DRL) methods, cannot perform well when the search space is too large. In addition, non-stationarity is a common problem when using hierarchical reinforcement learning (HRL), which is caused by the changing behavior in different hierarchies. Therefore, we first propose a DRL framework with an outage-based reward function, which is then used as a baseline. Then, we further design an HRL framework and training algorithm. By decomposing relay selection and power allocation into two hierarchical optimization objectives, and combining on- policy and off-policy methods in the HRL framework, our method successfully address the sparse reward and non-stationary problem. Simulation results reveal that compared with traditional DRL method, the proposed HRL training algorithm can converge faster and reduce the outage probability by 8% in two-hop relay network with the same outage threshold.

Energy Efficiency and Spectral Efficiency Tradeoff in Massive MIMO Multicast Transmission with Statistical CSI.

As the core technology of 5G mobile communication systems, massive multi-input multi-output (MIMO) can dramatically enhance the energy efficiency (EE), as well as the spectral efficiency (SE), which meets the requirements of new applications. Meanwhile, physical layer multicast technology has gradually become the focus of next-generation communication technology research due to its capacity to efficiently provide wireless transmission from point to multipoint. The availability of channel state information (CSI), to a large extent, determines the performance of massive MIMO. However, because obtaining the perfect instantaneous CSI in massive MIMO is quite challenging, it is reasonable and practical to design a massive MIMO multicast transmission strategy using statistical CSI. In this paper, in order to optimize the system resource efficiency (RE) to achieve EE-SE balance, the EE-SE trade-offs in the massive MIMO multicast transmission are investigated with statistical CSI. Firstly, we formulate the eigenvectors of the RE optimization multicast covariance matrices of different user terminals in closed form, which illustrates that in the massive MIMO downlink, optimal RE multicast precoding is supposed to be done in the beam domain. On the basis of this viewpoint, the optimal RE precoding design is simplified into a resource efficient power allocation problem. Via invoking the quadratic transform, we propose an iterative power allocation algorithm, which obtains an adjustable and reasonable EE-SE tradeoff. Numerical simulation results reveal the near-optimal performance and the effectiveness of our proposed statistical CSI-assisted RE maximization in massive MIMO.

On the Achievable Rate of MISO Fading Channels With Instantaneous CSI Only at the Transmitter

We investigate Multiple-Input Single-Output (MISO) fading channels with perfect instantaneous Channel State Information (CSI) at the transmitter and with statistical average CSI at the receiver. We outline achievable rates for maximum ratio transmission and show that the rate loss of not having instantaneous CSI at the receiver behaves like an additional noise term whose variance is equal to one fourth of the transmit power. Similar results are obtained for equal gain transmission. The simulation results are in agreement with the theoretical bounds and indicate that requirement of instantaneous CSI at the receiver becomes obsolete up to a certain transmit power.

On the secrecy performance of integrated satellite‐aerial‐terrestrial networks

SummaryThis paper investigates secure transmission of an integrated satellite‐aerial‐terrestrial network (ISATN), where multiple eavesdroppers (Eves) attempt to overhear the satellite signals cooperatively. The ISATN adopts an unmanned aerial vehicle (UAV) equipped with multiple antennas as a relay with threshold‐based decode‐and‐forward (DF) protocol. By assuming that perfect instantaneous channel state information (CSI) of the satellite‐UAV link and the statistical CSI of the UAV‐user link are available, we first propose a beamforming (BF) scheme for maximizing the achievable secrecy rate (ASR) of the considered network. Then, we derive the analytical expressions of the secrecy outage probability (SOP) and ergodic secrecy rate (ESR) of the considered system with the BF strategy under an assumption that the satellite‐UAV link undergoes the shadowed‐Rician fading, while the UAV‐user link experiences the correlated Rayleigh fading. Finally, numerical results are given to demonstrate the superiority of the proposed BF scheme against zero forcing (ZF) and maximal ratio transmission (MRT) schemes and the validity of the secrecy performance analysis.

IEEE 802.15.4.e TSCH-Based Scheduling for Throughput Optimization: A Combinatorial Multi-Armed Bandit Approach

In TSCH, which is a MAC mechanism set of the IEEE 802.15.4e amendment, calculation, construction, and maintenance of the packet transmission schedules are not defined. Moreover, to ensure optimal throughput, most of the existing scheduling methods are based on the assumption that instantaneous and accurate Channel State Information (CSI) is available. However, due to the inevitable errors in the channel estimation process, this assumption cannot be materialized in many practical scenarios. In this paper, we propose two alternative and realistic approaches. In our first approach, we assume that only the statistical knowledge of CSI is available a priori. Armed with this knowledge, the average packet rate on each link is computed and then, using the results, the throughput-optimal schedule for the assignment of (slot-frame) cells to links can be formulated as a max-weight bipartite matching problem, which can be solved efficiently using the well-known Hungarian algorithm. In the second approach, we assume that no CSI knowledge (even statistical) is available at the design stage. For this zero-knowledge setting, we introduce a machine learning-based algorithm by formally modeling the scheduling problem in terms of a combinatorial multi-armed bandit (CMAB) process. Our CMAB-based scheme is widely applicable to many real operational environments, thanks to its reduced reliance on design-time knowledge. Simulation results show that the average throughput obtained by the statistical CSI-based method is within the margin of 15% from the theoretical upper bound associated with perfect instantaneous CSI. The aforesaid margin is around 18% for our learning-theoretic solution.

Optimal Power Allocation for CC-HARQ-based Cognitive Radio with Statistical CSI in Nakagami Slow Fading Channels

Abstract This paper addresses the power allocation problem for cognitive radio (CR) based on hybrid-automatic-repeat-request (HARQ) with chase combining (CC) in Nakagamimslow fading channels. We assume that, instead of the perfect instantaneous channel state information (CSI), only the statistical CSI is available at the secondary user (SU) transmitter. The aim is to minimize the SU outage probability under the primary user (PU) interference outage constraint. Using the Lagrange multiplier method, an iterative and recursive algorithm is derived to obtain the optimal power allocation for each transmission round. Extensive numerical results are presented to illustrate the performance of the proposed algorithm.

Sample Approximation-Based Deflation Approaches for Chance SINR Constrained Joint Power and Admission Control

Consider the joint power and admission control (JPAC) problem for a multi-user single-input single-output (SISO) interference channel. Most existing works on JPAC assume the perfect instantaneous channel state information (CSI). In this paper, we consider the JPAC problem with the imperfect CSI, that is, we assume that only the channel distribution information (CDI) is available. We formulate the JPAC problem into a chance (probabilistic) constrained program, where each link's SINR outage probability is enforced to be less than or equal to a specified tolerance. To circumvent the computational difficulty of the chance SINR constraints, we propose to use the sample (scenario) approximation scheme to convert them into finitely many simple linear constraints. Furthermore, we reformulate the sample approximation of the chance SINR constrained JPAC problem as a composite group sparse minimization problem and then approximate it by a second-order cone program (SOCP). The solution of the SOCP approximation can be used to check the simultaneous supportability of all links in the network and to guide an iterative link removal procedure (the deflation approach). We exploit the special structure of the SOCP approximation and custom-design an efficient algorithm for solving it. Finally, we illustrate the effectiveness and efficiency of the proposed sample approximation-based deflation approaches by simulations.

Optimal Beamforming for Multi-antenna Relaying Schemes in Cognitive Radio Networks

This paper studies the optimal beamforming design for a cognitive relay network in which a secondary network transmits its data using a multi-antenna relay simultaneously with the primary users over the same spectrum. An optimal beamforming approach is proposed to maximize the received signal-to-noise ratio (SNR) of the secondary users, whereas the received interference power at the primary user is kept below a predefined threshold. The maximization of the SNR at the secondary users is derived as an objective function to fumulate a constrained beamforming optimization problem, and the optimization problem can be transformed into a Rayleigh-Ritz ratio problem which is then solved by the generalized singular-value decomposition in a closed form. In response to the practical problem that perfect instantaneous channel state information (CSI) is difficult to be available, we assume that partial CSI is available in the proposed method. Simulation results show that the performance can be further improved by either decreasing the uncertainty in channel coefficients or increasing the number of relay antennas.

Two‐mode limited feedback block‐fading adaptive transmission with minimum guaranteed rate in MIMO channel

AbstractThis paper develops an adaptive scheme relying on a part‐time availability of the channel state information and supplying the constant minimum rate achievable for an arbitrary fading realization. The two‐mode transmission is investigated with the instantaneous perfect channel state information being known at the transmitter in the limited portion of time. We derived a general procedure for a suitable probabilistic description of the channel under such a scheme. The first mode is without any feedback and it uses a simple uniform symbol energy assignment. The second one is the emergency mode with the perfect instantaneous channel state information. It is implemented by the channel inversion (or eigenmode sub‐space channel inversion) guaranteeing the minimum required rate regardless of the channel observation. The parameters of the transmission are the total available transmitted symbol energy, the portion of time the perfect feedback channel is used, the minimum required rate, and the average symbol energy transmitted in the first and the second mode. For these cases, we have found very essential trade‐off among the system parameters and also we have derived a general procedure how to evaluate long‐term performance. Copyright © 2010 John Wiley & Sons, Ltd.

Opportunistic File Transfer Over a Fading Channel Under Energy and Delay Constraints

We consider transmission control (rate and power) strategies for transferring a fixed-size file (finite number of bits) over fading channels under constraints on both transmit energy and transmission delay. The goal is to maximize the probability of successfully transferring the entire file over a time-varying wireless channel modeled as a finite-state Markov process. We study two implementations regarding the delay constraints: an average delay constraint and a strict delay constraint. We also investigate the performance degradation caused by the imperfect (delayed or erroneous) channel knowledge. The resulting optimal policies are shown to be a function of the channel-state information (CSI), the residual battery energy, and the number of residual information bits in the transmit buffer. It is observed that the probability of successful file transfer increases significantly when the CSI is exploited opportunistically. When the perfect instantaneous CSI is available at the transmitter, the faster channel variations increase the success probability under delay constraints. In addition, when considering the power expenditure in the pilot for channel estimation, the optimal policy shows that the transmitter should use the pilot only if there is sufficient energy left for packet transfer; otherwise, a channel-independent policy should be used.