SER analysis and power allocation for hybrid cooperative transmission system

The symbol-error-rate（SER） and power allocation for hybrid cooperative（HC） transmission system are investigated.Closed-form SER expression is derived by using the moment generating function（MGF）-based approach.However,the resultant SER contains an MGF of the harmonic mean of two independent random variables（RVs）,which is not tractable in SER analysis.We present a simple MGF expression of the harmonic mean of two independent RVs which avoids the hypergeometric functions used commonly in previous studies.Using the simple MGF,closed-form SER for HC system with M-ary phase shift keying（M-PSK） signals is provided.Further,an approximation as well as an upper bound of the SER is presented.It is shown that the SER approximation is asymptotically tight.Based on the tight SER approximation,the power allocation of the HC system is investigated.It is shown that the optimal power allocation does not depend on the fading parameters of the source-destination（SD） channel and it only depends on the source-relay（SR） and relay-destination（RD） channels.Moreover,the performance gain of the power allocation depends on the ratio of the channel quality between RD and SR.With the increase of this ratio,more performance gain can be acquired.

A LINEAR PRECODING STRATEGY BASED ON PARTICLE SWARM OPTIMIZATION IN MULTICELL COOPERATIVE TRANSMISSION

An optimal linear precoding scheme based on Particle Swarm Optimization(PSO),which aims to maximize the system capacity of the cooperative transmission in the downlink channel,is proposed for a multicell multiuser single input single output system.With such a scheme,the optimal precoding vector could be easily searched for each user according to a simplified objective function.Simulation results show that the proposed scheme can obtain larger average spectrum efficiency and a better Bit Error Rate(BER) performance than Zero Forcing(ZF) and Minimum Mean Square Error(MMSE) algorithm.

Opportunistic best-relay node selection for cooperative transmission improvement over underlay cognitive radio networks

In cognitive radio (CR) relay networks,most previous work concentrates on maximizing physical layer quality of service (QoS),e.g.,spectral efficiency and achievable data rate,as relay selection criteria.However,the residual energy state of relay nodes is largely ignored,which has significant effects on the average network lifetime for those battery-limited wireless networks.In this paper,an opportunistic distributed best-relay node selection scheme is proposed for cooperative transmissions over underlay-paradigm based CR networks,and meanwhile guarantee that the primary link is provided with a minimum-rate for a certain percentage of time.The objective is to increase achievable data rate as well as prolong average network lifetime.Specifically,both the instantaneous channel state information (ICSI) and residual energy of candidate relay nodes are taken as weighted metric,and the relay node with the optimal weighted metric is selected as the best one.Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

Throughput analysis of slotted ALOHA with cooperative transmission using successive interference cancellation

With a cross-layer design approach, a novel random access protocol is proposed in this paper, which is based on conventional slotted ALOHA （S-ALOHA） using successive interference cancellation （SIC） technique to separate collided packets and cooperative transmission to exploit the physical layer advantages. And a general theoretic analysis model is presented to obtain its throughput, which is also suitable for analyzing the performance of other protocols （such as S-ALOHA and S-ALOHA with cooperative transmission （C-ALOHA）） and is shown to be right and effective. Numerical results demonstrate that the proposed protocol can improve the maximal throughput by 190% and 132% over a Rayleigh fading channel, respectively, as compared with S-ALOHA and C-ALOHA. And the results show that our protocol can provide an effective random access method with high throughput for wireless transmission.

Deep Unfolding for Cooperative Rate Splitting Multiple Access in Hybrid Satellite Terrestrial Networks

Rate splitting multiple access(RSMA)has shown great potentials for the next generation communication systems.In this work,we consider a two-user system in hybrid satellite terrestrial network(HSTN)where one of them is heavily shadowed and the other uses cooperative RSMA to improve the transmission quality.The non-convex weighted sum rate(WSR)problem formulated based on this model is usually optimized by computational burdened weighted minimum mean square error(WMMSE)algorithm.We propose to apply deep unfolding to solve the optimization problem,which maps WMMSE iterations into a layer-wise network and could achieve better performance within limited iterations.We also incorporate momentum accelerated projection gradient descent(PGD)algorithm to circumvent the complicated operations in WMMSE that are not amenable for unfolding and mapping.The momentum and step size in deep unfolding network are selected as trainable parameters for training.As shown in the simulation results,deep unfolding scheme has WSR and convergence speed advantages over original WMMSE algorithm.

QoS-Aware Power Allocation Scheme for Relay Satellite Networks

This paper investigated a QoS-aware power allocation for relay satellite networks.For the given QoS requirements,we analyzed the signal model of relay transmission and formulated the power minimization problem which is non-convex and difficult to solve.To find the optimal solution to the considered problem,we first analyzed the optimization problem and equivalently turn it into a convex optimization problem.Then,we provided a Lagrangian dual-based method to obtain the closed-form of the power allocation and provided an iterative algorithm to the optimal solution.Moreover,we also extended the results to the cooperative transmission mode.Finally,simulation results were provided to verify the superiority of the proposed algorithm.

Communication-Efficient Edge AI Inference over Wireless Networks

Given the fast growth of intelligent devices, it is expected that a large number of high-stakes artificial intelligence (AI) applications, e. g., drones, autonomous cars, and tac?tile robots, will be deployed at the edge of wireless networks in the near future. Therefore, the intelligent communication networks will be designed to leverage advanced wireless tech?niques and edge computing technologies to support AI-enabled applications at various end devices with limited communication, computation, hardware and energy resources. In this article, we present the principles of efficient deployment of model inference at network edge to provide low-latency and energy-efficient AI services. This includes the wireless distribut?ed computing framework for low-latency device distributed model inference as well as the wireless cooperative transmission strategy for energy-efficient edge cooperative model infer?ence. The communication efficiency of edge inference systems is further improved by build?ing up a smart radio propagation environment via intelligent reflecting surface.

Code Design and Latency Analysis of Distributed Matrix Multiplication with Straggling Servers in Fading Channels

This paper exploits coding to speed up computation offloading in a multi-server mobile edge computing(MEC)network with straggling servers and channel fading.The specific task we consider is to compute the product between a user-generated input data matrix and a large-scale model matrix that is stored distributively across the multiple edge nodes.The key idea of coding is to introduce computation redundancy to improve robustness against straggling servers and to create communication redundancy to improve reliability against channel fading.We utilize the hybrid design of maximum distance separable(MDS)coding and repetition coding.Based on the hybrid coding scheme,we conduct theoretical analysis on the average task uploading time,average edge computing time,and average output downloading time,respectively and then obtain the end-to-end task execution time.Numerical results demonstrate that when the task uploading phase or the edge computing phase is the performance bottleneck,the hybrid coding reduces to MDS coding;when the downlink transmission is the bottleneck,the hybrid coding reduces to repetition coding.The hybrid coding also outperforms the entangled polynomial coding that causes higher uplink and downlink communication loads.

Challenges for beyond 5G:ultra-densification of radio access network

Ultra-densification of radio access network(RAN)is a key to efficiently support the exponentially growing mobile data traffic in 5 G era.Furthermore,extremely high frequency band like mm Wave band was utilized to solve the bandwidth shortage problem.However,untra-dense reusing the same radio resource produced severe interference.And the mm Wave link was very harsh due to frequent blockage by obstacles.Therefore a new RAN architecture needed to be introduced to realize ultra-reliable communications in such a severe radio propagation environment.An architecture of distributed MIMO based RAN was presented.Then,enhanced interference coordination(e IC)was described.Finally,the effectiveness of distributed MIMO based RAN with e IC by computer simulation was showed.

A Stackelberg Game for Spectrum Leasing in Cooperative Cognitive Radio Networks

According to the property-rights model of cognitive radio, primary users （PUs） who own the spectrum resource have the right to lease part of spectrum to secondary users （SUs） in exchange for appropriate profit. In this paper, we propose a pricing-based spectrum leasing framework between one PU and multiple SUs. In this scenario, the PU attempts to maximize its utility by setting the price of spectrum. Then, the selected SUs have the right to decide their power levels to help PU s transmission, aiming to obtain corresponding access time. The spectrum leasing problem can be cast into a stackelberg game, where the PU plays the seller-level game and the selected SUs play the buyer-level game. Through analysis based on the backward induction, we prove that there exists a unique equilibrium in the stackelberg game with certain constraints. Numerical results show that the proposed pricing-based spectrum leasing framework is effective, and the performance of both PU and SUs is improved, compared to the traditional mechanism without cooperation.

Novel pricing model for spectrum leasing in secondary spectrum market

According to the property rights model of cognitive radio,primary users who own the spectral resource have the right to lease or trade part of it to secondary users in exchange for appropriate profit.In this paper,an implementation of this framework is investigated,where a primary link can lease the owned spectrum to secondary nodes in exchange for cooperation （relaying）.A novel pricing model is proposed that enables the trading between spectrum and cooperation.Based on the demand of secondary nodes,the primary link attempts to maximize its quality of service （QoS） by setting the price of spectrum.Taking the price asked by primary link,the secondary nodes aim to obtain most profits by deciding the amount of spectrum to buy and then pay for it by cooperative transmission.The investigated model is conveniently cast in the framework of seller/buyer （Stackelberg） games.Analysis and numerical results show that our pricing model is effective and practical for spectrum leasing based on trading spectral resource for cooperation.