Full Channel Information Research Articles

Delay-Oriented Scheduling in 5G Downlink Wireless Networks Based on Reinforcement Learning With Partial Observations

5G wireless networks are expected to satisfy different delay requirements of various traffics by network resource scheduling. Existing scheduling methods perform poorly in practice due to their unrealistic assumption on the access to the full channel state information (CSI) or the explicit mathematical expression of network delay. In this paper, we consider the delay-oriented packet scheduling problem in multi-cell 5G downlink networks with multiple users and traffic types (e.g., FTP, VoIP and video streaming), and formulate it as a partially observable Markov decision process (POMDP). We design a delay-oriented downlink scheduling framework based on deep reinforcement learning (DRL) to autonomously schedule the active traffic flows without the full channel information. Furthermore, a recurrent proximal policy optimization (RPPO) algorithm is proposed to perceive the underlying state and accelerate learning under different time granularities, with the policy gradient theorem under POMDP strictly proved. By incorporating the future traffic information provided by a proposed spatial-temporal prediction algorithm, RPPO can balance the load and achieve lower delay in real-time multi-cell multi-user scenarios. Results of extensive experiments on a realistic 5G simulator demonstrate that our framework significantly outperforms existing approaches in terms of both tail delay and average delay for up to 48% and 41.7%, respectively.

Ordinary Differential Equation-Based CNN for Channel Extrapolation Over RIS-Assisted Communication

The reconfigurable intelligent surface (RIS) is considered as a promising new technology for reconfiguring wireless communication environments. To acquire the channel information accurately and efficiently, we only turn on a fraction of all the RIS elements, formulate a sub-sampled RIS channel, and design a deep learning based scheme to extrapolate the full channel information from the partial one. Specifically, inspired by the ordinary differential equation (ODE), we set up connections between different data layers in a convolutional neural network (CNN) and improve its structure. Simulation results are provided to demonstrate that our proposed ODE-based CNN structure can achieve faster convergence speed and better solution than the standard CNN.

Intelligent User Association for Symbiotic Radio Networks Using Deep Reinforcement Learning

In this paper, we are interested in symbiotic radio networks (SRNs), in which an Internet-of-Things (IoT) network parasitizes in a primary cellular network to achieve spectrum-, energy-, and infrastructure-efficient communications. Each IoT device transmits its own information by backscattering the signals from the primary network without using active radio-frequency (RF) transmitter chain. We consider the symbiosis between the cellular network and the IoT network and focus on the user association problem in SRN. Specifically, the base station (BS) in the primary network serves multiple cellular users using time division multiple access (TDMA) and each IoT device is associated with one cellular user for information transmission. The objective of user association is to link each IoT device to an appropriate cellular user by maximizing the sum rate of all IoT devices. However, the difficulty in obtaining the full real-time channel information makes it difficult to design an optimal policy for this problem. To overcome this issue, we propose two deep reinforcement learning (DRL) algorithms, both use historical information to infer the current information in order to make appropriate decisions. One algorithm, referred to as centralized DRL, makes decisions for all IoT devices at one time with globally available information. The other algorithm, referred to as distributed DRL, makes a decision only for one IoT device at one time using locally available information. Finally, simulation results show that the two proposed DRL algorithms achieve performance comparable to the optimal user association policy which requires perfect real-time information, and the distributed DRL algorithm has the advantage of scalability.

Energy-Efficient Distributed Resource Allocation With Low Overhead in Relay Cellular Networks

In this paper, we design a resource allocation (RA) scheme for the downlink of relay-assisted orthogonal frequency division multiple access cellular networks. Our design goal is to maximize the energy efficiency (EE) with minimal signaling overhead and low computational complexity. To alleviate the signaling overhead of channel information, the proposed scheme allocates the resources in a distributed manner, where each relay station (RS) or base station (BS) individually performs a joint subchannel and power allocation for its subordinate users. To increase the cell throughput, the proposed scheme allows the subchannel reuse among RSs and this enhances the EE performance. The proposed scheme is composed of the resource allocation at RS (RS-RA) and the resource allocation at BS (BS-RA). The RS-RA is formulated as an optimization problem that minimizes the consumed power of RS while reusing as many subchannels as possible among RSs. The optimization objective of BS-RA is to maximize the EE of the cell. Simulation results show that the proposed distributed RA scheme achieves remarkably excellent EE performance with small signaling overhead, comparable with the centralized optimal RA scheme aiming at the EE maximization based on full channel information.

Wireless Video Multicast With Cooperative and Incremental Transmission of Parity Packets

In this paper, a cooperative multicast scheme that uses Randomized Distributed Space Time Codes (R-DSTC), along with packet level Forward Error Correction (FEC), is studied. Instead of sending source packets and/or parity packets through two hops using R-DSTC as proposed in our prior work, the new scheme delivers both source packets and parity packets using only one hop. After the source station (access point, AP) first sends all the source packets, the AP as well as all nodes that have received all source packets together send the parity packets using R-DSTC. As more parity packets are transmitted, more nodes can recover all source packets and join the parity packet transmission. The process continues until all nodes acknowledge the receipt of enough packets for recovering the source packets. For each given node distribution, the optimum transmission rates for source and parity packets are determined such that the video rate that can be sustained at all nodes is maximized. This new scheme can support significantly higher video rates, and correspondingly higher PSNR of decoded video, than the prior approaches. Three suboptimal approaches, which do not require full information about user distribution or the feedback, and hence are more feasible in practice are also presented. The proposed suboptimal scheme with only the node count information and without feedback still outperforms our prior approach that assumes full channel information and no feedback.

Wireless scheduling with partial channel state information: large deviations and optimality

We consider a server serving a time-slotted queued system of multiple packet-based flows, where not more than one flow can be serviced in a single time slot. The flows have exogenous packet arrivals and time-varying service rates. At each time, the server can observe instantaneous service rates for only a subset of flows (selected from a fixed collection of observable subsets) before scheduling a flow in the subset for service. We are interested in queue length aware scheduling to keep the queues short. The limited availability of instantaneous service rate information requires the scheduler to make a careful choice of which subset of service rates to sample. We develop scheduling algorithms that use only partial service rate information from subsets of channels, and that minimize the likelihood of queue overflow in the system. Specifically, we present a new joint subset-sampling and scheduling algorithm called Max-Exp that uses only the current queue lengths to pick a subset of flows, and subsequently schedules a flow using the Exponential rule. When the collection of observable subsets is disjoint, we show that Max-Exp achieves the best exponential decay rate, among all scheduling algorithms that base their decision on the current (or any finite past history of) system state, of the tail of the longest queue. To accomplish this, we employ novel analytical techniques for studying the performance of scheduling algorithms using partial state, which may be of independent interest. These include new sample-path large deviations results for processes obtained by non-random, predictable sampling of sequences of independent and identically distributed random variables. A consequence of these results is that scheduling with partial state information yields a rate function significantly different from scheduling with full channel information. In the special case when the observable subsets are singleton flows, i.e., when there is effectively no a priori channel state information, Max-Exp reduces to simply serving the flow with the longest queue; thus, our results show that to always serve the longest queue in the absence of any channel state information is large deviations optimal.

Zero‐forcing and codebook based beamforming scheme for practical usage of multiuser MIMO‐OFDM with uplink channel sounding

SummarySince the concept of the multiuser multiple input multiple output (MU‐MIMO) system has been introduced for enhancement of capacity and flexibility, it has been accepted in various wireless standards. To enjoy the benefits of the MU‐MIMO system, full or partial channel information is necessary at the transmitter, but how to use the full or partial feedback information in the practical system perspective has not been investigated well. In this paper, we analyze the interference of full usage concurrent transmission codebook based on the MU‐MIMO systems and also investigate the usage of channel information for a codebook based scheme and a zero‐forcing beamforming (ZFBF) scheme. Based on the analytic results, we propose two adaptive schemes for the practical usage perspective in MU‐MIMO‐OFDM systems. Firstly, we propose an adjustable uplink channel sounding scheme, which depends on the number of users in a given cell/sector in frequency division duplexing system, with ZFBF MU‐MIMO‐OFDM systems. Secondly, we propose an adaptive switching scheme, which depends on signal‐to‐noise ratio, between the codebook based scheme and the ZFBF scheme. The performance of the proposed scheme is evaluated with computer simulations, and the simulation results show that the proposed scheme provides the enhanced throughput over entire signal‐to‐noise‐ratio regions. Copyright © 2014 John Wiley & Sons, Ltd.

Power Allocation for Artificial-Noise Secure MIMO Precoding Systems

This paper investigates the power allocation problem for artificial noise (AN) secure precoding systems, and proposes closed-form solutions for maximizing the achievable secrecy rate. It is assumed that the transmitter knows the full channel information at the legitimate receiver, and knows only the statistics of the channel information at the eavesdropper. Lower bounds are derived for the secrecy rates in multiple-input single-output channels with single or multiple eavesdroppers and multiple-input multiple-output channels with multiple eavesdroppers. When the number of transmit antennas is sufficiently large, the bounds are tight, and closed-form solutions can be derived from these bounds. The analytical results suggest simple and yet informative solutions as follows: Let the numbers of receive antennas at the legitimate receiver and at the eavesdropper be $N_{r}$ and $N_{r,e}$ , respectively. The system should distribute $N_{r,e}/(N_{r}+N_{r,e})$ of the power to AN in the high SNR regime, and distribute zero power to AN in the low SNR regime; the rate loss due to the eavesdropper is $-N_{r}\log(N_{r}/(N_{r}+N_{r,e}))-N_{r,e}\log(N_{r,e}/(N_{r}+N_{r,e}))$ bits/sec/Hz in the high SNR regime and nearly negligible in the low SNR regime. The derived results also show that equal power and water-filling power allocations lead to similar solutions and rate loss. Simulation results corroborate the theoretical results.

레일레이 페이딩 채널에서 부분 및 전체 채널 정보를 이용하는 기회전송 증가 릴레이 시스템의 성능

Abstract Recently, the opportunistic incremental relaying systems have been studied to improve the system performance effectively in wireless fading channel. Most of the performance analysis of the system includes a source-destination direct link. And there are few analysis which consider source-relay-destination indirect paths only. Therefore this paper proposes a transmission protocol which relays the source information using the selected relay from the partial channel information at the first stage in an opportunistic incremental relaying system. If the transmission fails, the selected best relay from the full channel information retransmits the information to the destination incrementally. The performance of the proposed system is derived analytically and verified from Monte Carlo simulation. The derived results can be applied to the system design and the performance estimation of the mobile systems and the bidirectional TV broadcasting systems which adapt an opportunistic incremental relaying system.

Continuous Power Allocation Strategies for Sensing-Based Multiband Spectrum Sharing

We propose continuous power allocation strategies for secondary users (SUs) based on sensing the primary user (PU) channels in a multiband cognitive radio (CR) network. Unlike the conventional sensing-based spectrum sharing, where there are two transmit power levels corresponding to whether the PU is sensed present or not, in the proposed strategy, the power levels are continuous functions of the sensing statistics, and optimized with respect to the achievable rate of the SU. The continuous power allocation function is parameterized by some channel parameters of the PU and SU, and we treat the cases of perfect and quantized channel state information (CSI) separately, where the former provides an upper bound on the achievable rate with full channel information; and the latter constitutes an efficient practical power allocation method for the SU with statistic/partial channel information. The power control process consists of two phases: in the first phase, the SU listens to the multiple bands licensed to the PU and obtains the sensing statistics, e.g., the received signal energies on these bands; in the second phase, the SU adjusts its transmit power levels on these bands based on the sensing results. Optimal power allocation schemes are derived to maximize the achievable rate at the SU under several possible combinations of the peak/average transmit power constraints at the SU and the peak/average interference power constraints at the PU. Simulation results demonstrate that the proposed strategies can significantly improve the achievable throughput of the SU compared to the conventional methods.

User Scheduling for Cooperative Base Station Transmission Exploiting Channel Asymmetry

We study low-signalling overhead scheduling for downlink coordinated multi-point (CoMP) transmission with multi-antenna base stations (BSs) and single-antenna users. By exploiting the asymmetric channel feature, i.e., the path-loss differences towards different BSs, we derive a metric to judge orthogonality among users only using their average channel gains, based on which we propose a semi-orthogonal scheduler that can be applied in a two-stage transmission strategy. Simulation results demonstrate that the proposed scheduler performs close to the semi-orthogonal scheduler with full channel information, especially when each BS is with more antennas and the cell-edge region is large. Compared with other overhead reduction strategies, the proposed scheduler requires much less training overhead to achieve the same cell-average data rate.

Performance Comparison of Relay Selection-Based Amplify-and-Forward Relaying Systems using Full or Partial Channel Information

In this paper, we consider the amplify-and-forward relaying systems with the relay selection (AF-RS) using full channel information (FCI) and using partial channel information (PCI), respectively, in which the direct communication link between a source and a destination is available. And, the outage probabilities for the AF-RS using FCI and using PCI are investigated in Rayleigh fading channels, respectively. We provide exact outage probabilities, tight outage probability lower bounds, and asymptotic outage probabilities, respectively. And also, we present coding gains achievable by the AF-RS using FCI and using PCI. Through numerical investigation, all the analytic expressions are verified, and the coding gains are respectively evaluated with different average channel powers for the first and the second hop as well as different numbers of relays.

On Kalman filtering over fading wireless channels with controlled transmission powers

We study stochastic stability of centralized Kalman filtering for linear time-varying systems equipped with wireless sensors. Transmission is over fading channels where variable channel gains are counteracted by power control to alleviate the effects of packet drops. We establish sufficient conditions for the expected value of the Kalman filter covariance matrix to be exponentially bounded in norm. The conditions obtained are then used to formulate stabilizing power control policies which minimize the total sensor power budget. In deriving the optimal power control laws, both statistical channel information and full channel information are considered. The effect of system instability on the power budget is also investigated for both these cases.

Adaptive multiregression in reproducing kernel Hilbert spaces: the multiaccess MIMO channel case.

This paper introduces a wide framework for online, i.e., time-adaptive, supervised multiregression tasks. The problem is formulated in a general infinite-dimensional reproducing kernel Hilbert space (RKHS). In this context, a fairly large number of nonlinear multiregression models fall as special cases, including the linear case. Any convex, continuous, and not necessarily differentiable function can be used as a loss function in order to quantify the disagreement between the output of the system and the desired response. The only requirement is the subgradient of the adopted loss function to be available in an analytic form. To this end, we demonstrate a way to calculate the subgradients of robust loss functions, suitable for the multiregression task. As it is by now well documented, when dealing with online schemes in RKHS, the memory keeps increasing with each iteration step. To attack this problem, a simple sparsification strategy is utilized, which leads to an algorithmic scheme of linear complexity with respect to the number of unknown parameters. A convergence analysis of the technique, based on arguments of convex analysis, is also provided. To demonstrate the capacity of the proposed method, the multiregressor is applied to the multiaccess multiple-input multiple-output channel equalization task for a setting with poor resources and nonavailable channel information. Numerical results verify the potential of the method, when its performance is compared with those of the state-of-the-art linear techniques, which, in contrast, use space-time coding, more antenna elements, as well as full channel information.

Angular Beamforming Technique for MIMO Beamforming System

The method of MIMO beamforming has gained a lot of attention. The eigen beamforming (EB) technique provides the best performance but requiring full channel information. However, it is impossible to fully acquire the channel in a real fading environment. To overcome the limitations of the EB technique, the quantized beamforming (QB) technique was proposed by using only some feedback bits instead of full channel information to calculate the suitable beamforming vectors. Unfortunalely, the complexity of finding the beamforming vectors is the limitation of the QB technique. In this paper, we propose a new technique named as angular beamforming (AB) to overcome drawbacks of QB technique. The proposed technique offers low computational complexity for finding the suitable beamforming vectors. In this paper, we also present the feasibility implementation of the proposed AB method. The experiments are undertaken mainly to verify the concept of the AB technique by utilizing the Butler matrix as a two-bit AB processor. The experimental implementation and the results demonstrate that the proposed technique is attractive from the point of view of easy implementation without much computational complexity and low cost.

Cooperative Layered Video Multicast Using Randomized Distributed Space Time Codes

With the increased popularity of mobile multimedia services, efficient and robust video multicast strategies are of critical importance. Cooperative communications has been shown to improve the robustness and the data rates for point-to-point transmission. In this paper, a two-hop cooperative transmission scheme for multicast in infrastructure-based networks is used, where multiple relays forward the data simultaneously using randomized distributed space time codes (RDSTC). This randomized cooperative transmission is further integrated with layered video coding and packet level forward error correction (FEC) to enable efficient and robust video multicast. Three different schemes are proposed to find the system operating parameters based on the availability of the channel information at the source station: RDSTC with full channel information, RDSTC with limited channel information, and RDSTC with node count. The performance of these three schemes are compared with rate adaptive direct transmission and conventional multicast that does not use rate adaptation. The results show that while rate-adaptive direct transmission provides better video quality than conventional multicast, all three proposed randomized cooperative schemes outperform both strategies significantly as long as the network has enough nodes. Furthermore, the performance gap between RDSTC with full channel information and RDSTC with limited channel information or node count is relatively small, indicating the robustness of the proposed cooperative multicast system using RDSTC.

Relay Power Allocation in Distributed Space-Time Coded Networks with Channel Statistical Information

This letter considers two-relay networks with Rician fading channels. It is assumed that the receiver has full channel information while the relays know the channel means and covariances only. To optimize network performance, we combine distributed space-time coding (DSTC) with relay power allocation. For the high signal-to-noise ratio (SNR) regime, we analytically find the relay power allocation that minimizes an upper bound on the pairwise error probability (PEP). Simulation shows that the proposed scheme largely improves network reliability. In some cases, lack of power allocation causes diversity loss.

Optimal Routing Strategy in Multi-Hop Relaying Networks

Reducing the energy consumption of wireless communication systems with new technologies and solutions continues to be an important concern in developing future standards. In this paper, we study the routing strategies in multi-hop relaying networks. For a 2-way assignment routing method, an efficient feedback scheme is presented to minimize the power consumption over the whole system. Compared with the full channel information in traditional feedback scheme, only the backward accumulated feedback metrics are required. If the proposed routing calculation is used, there is no performance loss. When the number of the hops and the relays is large, the new scheme achieves a significant feedback overhead reduction. Moreover, we show a proof for the optimality of the presented routing strategy based on mathematical induction.

On Kalman Filtering with Fading Wireless Channels Governed by Power Control

AbstractWe study stochastic stability for Kalman filtering over fading wireless channels where variable channel gains are counteracted by the use of power control to alleviate the effects of packet drops. The Kalman filter and the controller are located at a single gateway which acquires data from the wireless sensors. We establish sufficient conditions which ensure that the Kalman filter covariance matrix is exponentially bounded in norm. The conditions obtained are then used to formulate stabilizing optimal power allocation laws which minimize the total sensor power budget. In deriving the optimal power allocation laws, both statistical channel information and full channel information are considered. The effect of system instability on the power budget is also investigated for both these cases.

Asymptotic Achievable Rate Analysis for Selection Strategies in Amplify-and-Forward MIMO Two-Hop Networks With Feedback

This paper investigates relay-selection strategies in multiple-input-multiple-output (MIMO) relay networks, where joint source and relay preceding are utilized, and a single MIMO relay is adaptively selected for forwarding signals during each transmission period. Assuming that an optimal relay-selection scheme is implemented with full channel-information feedback, we study the optimal selection performance by characterizing its achievable rate with two derived bounds. Based on the rate bounds, we show that the corresponding achievable rate scales as (M/2) log log K for a fixed number of antennas M and an increasing number of relay nodes K → ∞. Furthermore, for feedback reduction, we propose a relay-selection scheme with limited feedback while still achieving the optimal achievable rate-scaling law for a large K. Numerical results verify the effectiveness of the limited feedback-selection strategy under practical values of relay number K.