Tomlinson-Harashima precoding with imperfect channel state information

Nonlinear Tomlinson-Harashima precoding (THP) is an attractive solution for a scenario where the transmission system employs multiple antennas at transmitter and multiple users with a single antenna at the receiver, so that the cooperation among the receive antennas are impossible (downlink scenario). THP solution based on zero forcing (ZF) and minimum mean square error (MMSE) criteria is one of the important techniques to achieve near multiple input multiple output channels capacity with reasonable complexity. In this paper, the effect of channel imperfection on THP is considered. At first, the achievable rate of THP with respect to ZF criterion in an imperfect channel state information (CSI) scenario is calculated. Moreover, based on MMSE criterion, a new robust solution is derived which provides a significant improvement with respect to the conventional optimisation method. Then, the effect of channel estimation error on THP is considered as an improved optimisation where THP filters are optimised together with a channel estimator. Spatial power loading is found to be important to the THP performance. This loading for robust/joint optimisation of MMSE THP is developed by minimum average symbol error rate sense. Simulation results show the capacity loss, the performance advantage attained by the robust/joint optimisation and the power loading in an imperfect CSI scenario.     

High mobility orthogonal frequency division multiple access channel estimation using basis expansion model

Owing to the loss of subcarrier orthogonalities in high-speed applications, the use of conventional frequency-domain-based channel estimation in high mobility orthogonal frequency division multiple access (OFDMA) systems such as mobile WiMax may give rise to an unacceptable high channel estimation error floor. To alleviate this problem, the authors develop some basis expansion model (BEM)-based estimation schemes for the OFDMA uplink. Specifically, the authors express the time-varying channel as a superposition of a small number of complex exponential basis functions spanning the entire Doppler range, and then formulate least square (LS) and linear minimum mean square error (LMMSE) algorithms to estimate the basis coefficients for two different types of pilot patterns. The authors also derive the respective Cramer-Rao lower bounds for these estimators. It has been shown that the time domain BEM using a pilot scheme where pilots are placed over time axis will give better performance under a high Doppler scenario. Lastly, using simulation results, the proposed algorithms have been found to have better estimation accuracy over current frequency domain estimation techniques. This is in addition to the advantage that the proposed algorithms have in general a lower computational complexity.     

OFDMA系统资源优化综述

在正交频分多址(OFDMA)系统中,有效的资源分配可以充分发挥系统性能.目前的资源优化主要分为频谱效率优化与系统能效优化两个方面.频谱效率优化即在给定的总功率下,最大化系统总容量;系统根据信道状态信息,分步对子载波和功率进行分配.根据不同的业务需求,频谱效率优化又细分为仅考虑频谱效率的优化以及考虑用户公平性与复杂通信环境下的优化.系统能效优化是在保证系统总容量的同时,降低系统的总功率消耗.能效优化算法采用了分式规划、约束条件松弛与拉格朗日对偶分解等最优化理论的方法进行资源分配的优化.在加入不同约束的考虑下,能效优化算法分为保证用户公平性、回程线路容量、通信安全等不同的能效优化方案.但是,这两方面的资源优化对不完整信道信息的情况考虑较少,对于多业务的资源分配,未能针对业务多样性进行高效资源优化.另外,资源优化在跨层资源分配上仍有不足.     

Multiuser scheduling in high speed downlink packet access

Multiuser scheduling is an important aspect in the performance optimisation of a wireless network as it allows multiple users to efficiently access a shared channel by exploiting multiuser diversity. For example, the 3GPP cellular standard supports multiuser scheduling in the high speed downlink packet access (HSDPA) feature. To perform efficient scheduling, channel state information (CSI) for users is required, and is obtained via their respective feedback channels. Multiuser scheduling is studied assuming the availability of perfect CSI, which would require a high bandwidth overhead. A more realistic imperfect CSI feedback in the form of a finite set of channel quality indicator values is assumed, as specified in the HSDPA standard. A global optimal approach and a simulated annealing (CSA) approach are used to solve the optimisation problem. Simulation results suggest that the performances of the two approaches are very close even though the complexity of the simulated annealing (SA) approach is much lower. The performance of a simple greedy approach is found to be significantly worse.     

Decision-directed channel estimation based on iterative linear minimum mean square error for orthogonal frequency division multiplexing systems

A decision-directed (DD) channel estimation based on iterative linear minimum mean square error (LMMSE) is proposed for orthogonal frequency division multiplexing systems. Existing DD channel estimation is well known to have the problem of error propagation because of symbol-by-symbol detection. The proposed algorithm can estimate the correction term of current channel state information (CSI) according to the error vector of previous CSI by applying the orthogonality principle, and corrects the current CSI with this correction term. Analysis and simulation results have shown that this method has no error propagation problem. The performance of the proposed algorithm is much better than the conventional DD channel estimation, and close to the optimal LMMSE estimator, but with much less computational complexity compared with the optimal LMMSE estimator.     

Interpolation-based precoding with limited feedback for MIMO-OFDM systems

The channel state information (CSI) at the transmitter can significantly improve the performance of multiple-antenna systems. However, providing full knowledge of CSI at the transmitter may not be affordable in many practical cases. Thus, exploiting the partial channel knowledge to improve system performance seems to be attractive. An interpolation based limited feedback precoding scheme (ILFP) for MIMO-OFDM systems is proposed. In this scheme, both the transmitter and the receiver store the codebook of precoding matrices constructed offline using two-variable joint vector quantisation. Considering the correlation between OFDM subcarriers, they are divided into subcarrier clusters. At the receiver, precoding is carried out on the clusters, and then the precoding information is conveyed to the transmitter by limited bits of feedback. At the transmitter, the precoding matrices for each subcarrier are obtained by interpolation according to the feedback. Simulation results show that the proposed scheme outperforms the existing schemes.     

Spectral efficiency evaluation for selection combining diversity (SCD) scheme over slow fading

In this paper we derive closed-form expressions for the single-user capacity of selection combining diversity (SCD) system, taking into account the effect of imperfect channel estimation at the receiver. The channel considered is a slowly varying spatially independent flat Rayleigh fading channel. The complex channel estimate and the actual channel are modelled as jointly Gaussian random variables with a correlation that depends on the estimation quality. Three adaptive transmission schemes are analysed: 1) optimal power and rate adaptation opra; 2) constant power with optimal rate adaptation ora; and 3) channel inversion with fixed rate cifr. Furthermore, we derive in this paper analytical results for capacity statistics including moment generating function (MGF), complementary cumulative distribution function (CDF) and probability density function (PDF). These statistics are valid for arbitrary number of receive antennas. Our numerical results show the effect of Gaussian channel estimation error on the achievable spectral efficiency.     

Outage Capacity Analysis for Cognitive Non-Orthogonal Multiple Access Downlink Transmissions Systems in the Presence of Channel Estimation Error

In this paper, we propose a downlink cognitive non-orthogonal multiple access (NOMA) network, where the secondary users (SUs) operate in underlay mode. In the network, secondary transmitter employs NOMA signaling for downlink transmission, and the primary user (PU) is interfered by the transmission from SU. The expressions for the outage probabilities are derived in closed-form for both primary and secondary users in the presence of channel estimation error. Numerical simulation results show that the channel estimation error and the inter-network interference cause degradation of the downlink outage performance. Also the power allocation and the location have a significant impact on the outage probability. The numerical experiments demonstrate that the analytic expressions of the outage probabilities match with the simulation results.     

Resource Allocation and Power Control Policy for Device-toDevice Communication Using Multi-Agent Reinforcement Learning

Device-to-Device (D2D) communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity. In this paper, we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput. Firstly, we treat each D2D pair as an independent agent. Each agent makes decisions based on the local channel states information observed by itself. The multi-agent Reinforcement Learning (RL) algorithm is proposed for our multi-user system. We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected, so the problem is modeled as a stochastic non-cooperative game. Hence, each agent becomes a player and they make decisions together to achieve global optimization. Thereby, the multi-agent Q-learning algorithm based on game theory is established. Secondly, in order to accelerate the convergence rate of multi-agent Q-learning, we consider a power allocation strategy based on Fuzzy Cmeans (FCM) algorithm. The strategy firstly groups the D2D users by FCM, and treats each group as an agent, and then performs multi-agent Q-learning algorithm to determine the power for each group of D2D users. The simulation results show that the Q-learning algorithm based on multi-agent can improve the throughput of the system. In particular, FCM can greatly speed up the convergence of the multi-agent Q-learning algorithm while improving system throughput.     

Energy Efficient Resource Allocation Approach for Renewable Energy Powered Heterogeneous Cellular Networks

In this paper, maximizing energy efficiency (EE) through radio resource allocation for renewable energy powered heterogeneous cellular networks (HetNet) with energy sharing, is investigated. Our goal is to maximize the network EE, conquer the instability of renewable energy sources and guarantee the fairness of users during allocating resources. We define the objective function as a sum weighted EE of all links in the HetNet. We formulate the resource allocation problem in terms of subcarrier assignment, power allocation and energy sharing, as a mixed combinatorial and non-convex optimization problem. We propose an energy efficient resource allocation scheme, including a centralized resource allocation algorithm for iterative subcarrier allocation and power allocation in which the power allocation problem is solved by analytically solving the Karush-Kuhn-Tucker (KKT) conditions of the problem and a water-filling problem thereafter and a low-complexity distributed resource allocation algorithm based on reinforcement learning (RL). Our numerical results show that both centralized and distributed algorithms converge with a few times of iterations. The numerical results also show that our proposed centralized and distributed resource allocation algorithms outperform the existing reference algorithms in terms of the network EE.     

Machine Learning Based Resource Allocation of Cloud Computing in Auction

Resource allocation in auctions is a challenging problem for cloud computing. However, the resource allocation problem is NP-hard and cannot be solved in polynomial time. The existing studies mainly use approximate algorithms such as PTAS or heuristic algorithms to determine a feasible solution; however, these algorithms have the disadvantages of low computational efficiency or low allocate accuracy. In this paper, we use the classification of machine learning to model and analyze the multi-dimensional cloud resource allocation problem and propose two resource allocation prediction algorithms based on linear and logistic regressions. By learning a small-scale training set, the prediction model can guarantee that the social welfare, allocation accuracy, and resource utilization in the feasible solution are very close to those of the optimal allocation solution. The experimental results show that the proposed scheme has good effect on resource allocation in cloud computing.     

Effect of error in channel state information on the channel capacity with QAM signalling and the design of robust signal constellations

The effects of channel estimation errors on the channel capacity of a discrete time, discrete input, infinite output Rayleigh fading channel are investigated. The case of conventional modulation methods such as phase shift keying (PSK) and quatrature amplitude modulation (QAM) is investigated at first where it was observed that the capacity degrades rapidly with increasing channel estimation errors. The effect of error in the channel estimation is similar to the effect of higher noise in the channel that depends on the transmitted signal. A genetic algorithm is used to optimise the signal constellation in order to maximise the capacity for a given finite number of signal points. The aim of trying to maximise the capacity is to estimate the remaining gap in performance between a traditional modulation scheme such as QAM and the best possible constellation that is optimised for the channel. The constellations obtained from the genetic algorithm are, in general, not directly implementable. A method to design practical robust signal constellations that overcome the effect of channel state information (CSI) error is presented. The robust signal constellations obtained show a performance that is very close to the optimal constellations. In this work, the probability distribution of the error in CSI is assumed to be known.     

WiFi6 Dynamic Channel Optimization Method for Fault Tolerance in Power Communication Network

As the scale of power networks has expanded, the demand for multi-service transmission has gradually increased. The emergence of WiFi6 has improved the transmission efficiency and resource utilization of wireless networks. However, it still cannot cope with situations such as wireless access point (AP) failure. To solve this problem, this paper combines orthogonal frequency division multiple access (OFDMA) technology and dynamic channel optimization technology to design a fault-tolerant WiFi6 dynamic resource optimization method for achieving high quality wireless services in a wirelessly covered network even when an AP fails. First, under the premise of AP layout with strong coverage over the whole area, a faulty AP determination method based on beacon frames (BF) is designed. Then, the maximum signal-to-interference ratio (SINR) is used as the principle to select AP reconnection for the affected users. Finally, this paper designs a dynamic access selection model (DASM) for service frames of power Internet of Things (IoTs) and a scheduling access optimization model (SAO-MF) based on multi-frame transmission, which enables access optimization for differentiated services. For the above mechanisms, a heuristic resource allocation algorithm is proposed in SAO-MF. Simulation results show that the method can reduce the delay by 15% and improve the throughput by 55%, ensuring high-quality communication in power wireless networks.     

基于校验式判决的LDPC码编码的HARQ协议

针对RB-HARQ协议系统开销巨大的问题,提出一种基于校验式判决的HARQ协议.该协议根据不成立的校验式与错误比特之间的关系,将一部分不成立的校验式的序号通过反馈信道反馈给发射机,发射机按照一定的规则将这些校验式所包含的某些比特确定为重传比特,重新发送给接收端,接收端将重传的比特与先前发送的码字进行最大比合并,最后进行译码.数值仿真结果表明,由于只需要反馈一部分校验式的序号,该协议能够有效降低系统的开销,其吞吐量性能能够接近RB-HARQ协议.     

Receive maximal-ratio combining with outdated arbitrary transmit antenna selection in Nakagami-m fading

The use of transmit antenna selection algorithms in multiple-antenna systems enables significant reduction in implementation cost and complexity while maintaining acceptable performance. An attractive and quite flexible selection algorithm is to allow the receiver to pick any of the transmit antennas that can satisfy a predetermined performance target. Such an algorithm is referred to as the arbitrarily ordered transmit antenna selection algorithm. However, the effectiveness of transmit antenna selection is decreased by several propagation impairments over the feedback channel from the receiver to the transmitter. Of these impairments, the feedback channel time delay may impose a significant impact on the achieved performance. This paper aims to investigate the impact of this time delay on the performance of receive maximal-ratio combining (MRC) diversity employing the arbitrarily ordered transmit antenna selection algorithm. In order to obtain quantitative measures for this impact, new expressions for various performance criteria are obtained by using the new derived formulas for the probability density function (pdf) and the moment generating function (MGF) of the combined signal-to-noise ratio (SNR). Numerical and simulation results are presented to illustrate the effect of delayed (i.e. outdated) feedback information on the system performance for various transmit antenna selection scenarios.     

Concatenated coded modulation techniques and orthogonal space-time block codes in the presence of fading channel estimation errors

Performance of coding and modulation systems in fading channels is usually analysed under the assumption that the receiver has perfect knowledge of channel condition. However, various shortcomings in practical channel estimation techniques lead to imperfections, resulting in channel estimation errors. The authors analyse a practical coding and modulation scheme for multiple-antenna systems considering channel estimation errors. The novelty of this study resides in providing error probability bounds for concatenated trellis coded modulation (TCM) or bit-interleaved coded modulation (BICM) schemes with orthogonal space--time block codes (OSTBC) under imperfect channel estimation assumption. Moreover, our analytical results quantify the performance degradation associated with various levels of channel estimation error variance. The authors also show that if channel estimation quality does not improve sufficiently with SNR, there would be error floor in performance, such that the coded system could get outperformed by a system with differential signalling that requires no channel estimation. Simulation results are presented, which confirm the validity of the analytical results.     

Partial band jamming of multicarrier frequency hopping/binary phase shift keying receiver over a rayleigh fading channel with imperfect channel estimation

The combined effect of partial band jamming and imperfect channel estimation on the analysis of the performance of MC-FH/BFSK receiver over a Rayleigh fading channel is often neglected in the literature. In this study, this missing analysis is provided. The authors derive closed form bit error probability (BEP) expressions for studying the effect of partial band and broad-band jamming on the performance of a coherent uncoded MC-FH/ BFSK receiver over a Rayleigh fading channel where the effect of channel estimation errors is not neglected. The considered model of the channel estimation errors does not assume independency between amplitude and phase estimates. Computer simulations are provided to validate the theoretical developments. It is shown that the smart partial band jammer should be present in all sub-bands of the system in order to be effective.     

A Capacity Improving Scheme in Multi-RSUs Deployed V2I

The communication reliability and system capacity are two of the key performance indicators for Internet of Vehicles (IoV). Existing studies have proposed a variety of technologies to improve reliability and other performance, such as channel selection and power allocation in Vehicle-to-Infrastructure (V2I). However, these researches are mostly applied in a single roadside unit (RSU) scenario without considering inter-cell interference (ICI) of multi-RSUs. In this paper, considering the distribution characteristics of multi-RSUs deployment and corresponding ICI, we propose a reliable uplink transmission scheme to maximize the total capacity and decrease the interference of multi-RSUs (mRSU-DI) in condition of the uplink interruption performance. In the proposed mRSU-DI scheme, ICI is depressed by dynamic channel and power allocation algorithm. A heuristic algorithm based on penalty function is proposed to obtain the optimal power allocation solution of the model. In addition, we realize the scheme in both given conditions of channel state information (CSI) and channel state distribution, respectively. The results show that the proposed scheme can both improve the system capacity and guarantee the reliable transmission in both premises.     

非理想信道估计条件下OFDM系统的最大似然检测算法

基于最大似然的准则,研究了理想信道估计条件下和非理想信道估条件下OFDM系统的最优检测算法。研究结果表明,当发送信号为PSK调制方式时,无论是理想信道估计还是非理想信道估计,最大似然检测算法与传统的迫零检测算法等价。但当信道估计非理想且发送信号的调制方式为16QAM或高阶QAM时,采用最大似然检测算法才能够获得更好的性能。     

A Joint Algorithm for Resource Allocation in D2D 5G Wireless Networks

With the rapid development of Internet technology, users have an increasing demand for data. The continuous popularization of traffic-intensive applications such as high-definition video, 3D visualization, and cloud computing has promoted the rapid evolution of the communications industry. In order to cope with the huge traffic demand of today’s users, 5G networks must be fast, flexible, reliable and sustainable. Based on these research backgrounds, the academic community has proposed D2D communication. The main feature of D2D communication is that it enables direct communication between devices, thereby effectively improve resource utilization and reduce the dependence on base stations, so it can effectively improve the throughput of multimedia data. One of the most considerable factor which affects the performance of D2D communication is the co-channel interference which results due to the multiplexing of multiple D2D user using the same channel resource of the cellular user. To solve this problem, this paper proposes a joint algorithm time scheduling and power control. The main idea is to effectively maximize the number of allocated resources in each scheduling period with satisfied quality of service requirements. The constraint problem is decomposed into time scheduling and power control subproblems. The power control subproblem has the characteristics of mixed-integer linear programming of NP-hard. Therefore, we proposed a gradual power control method. The time scheduling subproblem belongs to the NP-hard problem having convex-cordinality, therefore, we proposed a heuristic scheme to optimize resource allocation. Simulation results show that the proposed algorithm effectively improved the resource allocation and overcome the co-channel interference as compared with existing algorithms.