Channel assignment and power allocation for throughput improvement with PPO in B5G heterogeneous edge networks

In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.

Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication

The current resource allocation in 5G vehicular networks for mobile cloud communication faces several challenges,such as low user utilization,unbalanced resource allocation,and extended adaptive allocation time.We propose an adaptive allocation algorithm for mobile cloud communication resources in 5G vehicular networks to address these issues.This study analyzes the components of the 5G vehicular network architecture to determine the performance of different components.It is ascertained that the communication modes in 5G vehicular networks for mobile cloud communication include in-band and out-of-band modes.Furthermore,this study analyzes the single-hop and multi-hop modes in mobile cloud communication and calculates the resource transmission rate and bandwidth in different communication modes.The study also determines the scenario of one-way and two-way vehicle lane cloud communication network connectivity,calculates the probability of vehicle network connectivity under different mobile cloud communication radii,and determines the amount of cloud communication resources required by vehicles in different lane scenarios.Based on the communication status of users in 5G vehicular networks,this study calculates the bandwidth and transmission rate of the allocated channels using Shannon’s formula.It determines the adaptive allocation of cloud communication resources,introduces an objective function to obtain the optimal solution after allocation,and completes the adaptive allocation process.The experimental results demonstrate that,with the application of the proposed method,the maximum utilization of user communication resources reaches approximately 99%.The balance coefficient curve approaches 1,and the allocation time remains under 2 s.This indicates that the proposed method has higher adaptive allocation efficiency.

A Deep Reinforcement Learning-Based Technique for Optimal Power Allocation in Multiple Access Communications

Formany years,researchers have explored power allocation(PA)algorithms driven bymodels in wireless networks where multiple-user communications with interference are present.Nowadays,data-driven machine learning methods have become quite popular in analyzing wireless communication systems,which among them deep reinforcement learning(DRL)has a significant role in solving optimization issues under certain constraints.To this purpose,in this paper,we investigate the PA problem in a k-user multiple access channels(MAC),where k transmitters(e.g.,mobile users)aim to send an independent message to a common receiver(e.g.,base station)through wireless channels.To this end,we first train the deep Q network(DQN)with a deep Q learning(DQL)algorithm over the simulation environment,utilizing offline learning.Then,the DQN will be used with the real data in the online training method for the PA issue by maximizing the sumrate subjected to the source power.Finally,the simulation results indicate that our proposedDQNmethod provides better performance in terms of the sumrate compared with the available DQL training approaches such as fractional programming(FP)and weighted minimum mean squared error(WMMSE).Additionally,by considering different user densities,we show that our proposed DQN outperforms benchmark algorithms,thereby,a good generalization ability is verified over wireless multi-user communication systems.

Power control and channel allocation optimization game algorithm with low energy consumption for wireless sensor network

In a wireless sensor network （WSN）, the energy of nodes is limited and cannot be charged. Hence, it is necessary to reduce energy consumption. Both the transmission power of nodes and the interference among nodes influence energy consumption. In this paper, we design a power control and channel allocation game model with low energy consumption （PCCAGM）. This model contains transmission power, node interference, and residual energy. Besides, the interaction between power and channel is considered. The Nash equilibrium has been proved to exist. Based on this model, a power control and channel allocation optimization algorithm with low energy consumption （PCCAA） is proposed. Theoretical analysis shows that PCCAA can converge to the Pareto Optimal. Simulation results demonstrate that this algorithm can reduce transmission power and interference effectively. Therefore, this algorithm can reduce energy consumption and prolong the network lifetime.

Spot beam handover trigger and channel allocation scheme in GEO mobile satellite communication

An effective spot beam handover trigger and channel allocation scheme is proposed for GEO mobile satellite communication based on its characteristic and application. By using both signal strength and terminal location information, necessary handover is triggered promptly and accurately to reduce the negative effect of long signaling delay. Then handover decision is made with the handover queuing and channel allocation strategy. An adaptive channel resource allocation scheme is considered to optimize resource utilization with guarantee of emergency communication, which is significant for emergency rescue and disaster relief. Simulation results show that the proposed scheme prevents unnecessary handover effectively and has favorable adaptability to emergent requirement of satellite communication.

APO-Based Parallel Algorithm of Channel Allocation for Cognitive Networks

This article investigates channel allocation for cognitive networks, which is difficult to obtain the optimal allocation distribution. We first study interferences between nodes in cognitive networks and establish the channel allocation model with interference constraints. Then we focus on the use of evolutionary algorithms to solve the optimal allocation distribution. We further consider that the search time can be reduced by means of parallel computing, and then a parallel algorithm based APO is proposed. In contrast with the existing algorithms, we decompose the allocation vector into a number of sub-vectors and search for optimal allocation distribution of sub-vector in parallel. In order to speed up converged rate and improve converged value, some typical operations of evolutionary algorithms are modified by two novel operators. Finally, simulation results show that the proposed algorithm drastically outperform other optimal solutions in term of the network utilization.

CHANNEL DE-ALLOCATION SCHEMES FOR GSM/GPRS NETWORKS

Several channel de-allocation schemes for GSM/GPRS(General Packet Radio Service) networks are proposed in this paper. For DRA (Dynamical Resource Allocation) with de-allocation mechanism, if a new voice call arrives and finds that all the channels are busy,then one of the GPRS packets which occupy more than one channel for data transmission may release a channel for the new voice call. This paper presents 5 de-allocation mechanisms, i.e.DA-RANDOM, DA-RICHEST, DA-POOREST, DA-OLDEST and DA-YOUNGEST, to select the GPRS packet for releasing the appropriate channel. Simulation results show that DAOLDEST achieves the best performance, especially in packets blocking probability, among all the de-allocation schemes. Although the performance of the proposed de-allocation schemes is not significantly different, they are all much better than that of the scheme without de-allocation.

Energy Efficient Relay Positioning and Power Allocation for One-Way N-Relay Channel

Two end-users which have symmetric traffic requirements in terms of data rate are considered. They exchange information in Rayleigh flat-fading channels and multiple serial half-duplex relay nodes are employed to extend the communication coverage and assist the bidirectional communication between them using the analog network coding( ANC) protocol. With the objective of minimizing the sum transmit energy at the required data rate c,the optimal relay positioning and power allocation problem is firstly investigated and then the sub-optimal solutions for a two-relay channel are proposed,due to no close-form optimal solution. Furthermore,a sub-optimal scheme of relay positioning and power allocation,called equal-distance equal-transmit-power( EDEP) for an arbitrary Nrelay channel,N > 1 is proposed. Simulation results demonstrate a consistence with our proposed scheme.

Random Channel Allocation Scheme for SDMA Systems

A random allocation scheme for SDMA systems is proposed with a goal of more efficient dynamic allocation. Based on theoretical analysis and derivation, the blocking probability of the proposed scheme is calculated and compared with those of the ftrst duplicate （FD） and duplicate last （DL） schemes with different state-independent probabilities （p,） of acquring a dupicate channel suecessfully and 5 resources; moreover, a more realistic performance analysis of the random scheme is made with state-dependent ps in the SDMA/CDMA environment. The results show that the random scheme has a similar allocation pefformace to the FD and DL schemes, but is simpler than them in computation and scheduling.

A new channel allocation scheme and performance optimizing for mobile multimedia wireless networks

A multimedia channel allocation scheme is proposed and studied in terms of the connection-level QoS.A new traffic model based on multidimensional Markov chain is developed considering the traffic characteristic of two special periods of time.And the pre-emptive priority strategies are used to classify real-time services and non-real-time services.Real-time service is given higher priority for its allowance to pre-empt channels used by non-real-time service.Considering the mobility of persons in a day,which affects the mobile user's density,the simulation was conducted involving the two pre-emptive priority strategies.The result of some comparisons shows the feasibility of the proposed scheme.

Pilot Allocation Optimization Using Enhanced Salp Swarm Algorithm for Sparse Channel Estimation

Pilot pattern has a significant effect on the performance of channel estimation based on compressed sensing.However,because of the influence of the number of subcarriers and pilots,the complexity of the enumeration method is computationally impractical.The meta-heuristic algorithm of the salp swarm algorithm(SSA)is employed to address this issue.Like most meta-heuristic algorithms,the SSA algorithm is prone to problems such as local optimal values and slow convergence.In this paper,we proposed the CWSSA to enhance the optimization efficiency and robustness by chaotic opposition-based learning strategy,adaptive weight factor,and increasing local search.Experiments show that the test results of the CWSSA on most benchmark functions are better than those of other meta-heuristic algorithms.Besides,the CWSSA algorithm is applied to pilot pattern optimization,and its results are better than other methods in terms of BER and MSE.

Joint channel and power allocation scheme for cognitive wireless networks

Cognitive radio （CR） is a promising technology deemed to improve the efficiency of spectrum utilization. This paper considers a spectrum underlay cognitive radio network, in which the cognitive users （CUs） are allowed to use the radio spectrum concurrently with the primary users （PUs） under the interference temperature constraint. We investigate the system performance by using the proposed joint channel and power allocation scheme under two transmit strategies to achieve higher data rates and performance diversity gain respectively. Simulation results show that the proposed scheme provides a significant improvement on the bit error rate （BER） performance and spectrum efficiency of a cognitive wireless network.

Dynamic Channel Allocation in Sectored Cellular Systems

It is known that dynamic channel assignment(D CA ) strategy outperforms the fixed channel assignment(FCA) strategy in omni-direc tional antenna cellular systems. One of the most important methods used in DCA w as channel borrowing. But with the emergence of cell sectorization and spatial d ivision multiple access(SDMA) which are used to increase the capacity of cel lular systems, the channel assignment faces a series of new problems. In this pa per, a dynamic channel allocation scheme based on sectored cellular systems is p roposed. By introducing intra-cell channel borrowing (borrowing channels from n eighboring sectors) and inter-cell channel borrowing (borrowing channels from n eighboring cells) methods, previous DCA strategies, including compact pattern ba sed channel borrowing(CPCB) and greedy based dynamic channel assignment(GDCA) schemes proposed by the author, are improved significantly. The computer simu lation shows that either intra-cell borrowing scheme or inter-cell borrowing s cheme is efficient enough to uniform and non-uniform traffic service distributi ons.

Channel Allocation and Relay Selection Scheme in Relay-Based Networks

It has been demonstrated that either Channel Allocation （CA） or Relay Selection （RS） can improve the performance in relaying networks separately. However, there is little work concerning their combination in multi-cell uplink scenarios. In this paper, we investigate the issue which considers the CA and RS to optimize the system transmission rate in an uplink scenario, while maintaining the resource distribution fairness among users. This is first formulated as an optimization problem for a linear cellular system, where the same frequency channels can be reused in different cells. Based on the link and co-channel interference conditions, two low-complexity CA and RS schemes are then proposed with different decomposition se quences. Finally, numerical results are con ducted to verify the effectiveness of the pro posed CA and RS methods. Simulations re suits show that the proposed methods can yield significant improvements in system per formance in terms of average sum rate.

Video coding bit allocation algorithm over wireless transmission channel

For two-way video communications over wireless channels using the automatic repeat request (ARQ) retransmission scheme, TMN8 rate control scheme is not effective in minimizing the number of frames skipped and cannot guarantee video quality during the retransmissions of error packets. This paper presents a joint source channel bit allocation scheme that allocates target bits according to encoder buffer fullness and estimation of channel condition by retransmission information. The results obtained from implementing our scheme in H.263+coder over wireless channel model show that our proposed scheme encodes the video sequences with lower and steadier buffer delay, fewer frames skipped and higher average PSNR compared to TMN8.

A New Channel Allocation Scheme for Hierarchical Wireless Networks

A channel allocation scheme for hierarchical wireless networks was proposed in terms of the connection-level quality of service. The channel allocation scheme was analyzed using both horizontal channel borrowing and vertical traffic overflowing. Pre-emptive priority strategies are used to classify real-time services and non-real-time services, real-time service is given higher priority for it is allowed to pre-empt channels used by non-real-time service. Some channel borrowing thresholds and acceptance ratios are used to avoid channel locking or dynamic power control, which can also be dynamically adjusted according to network load. Simulation results show that the proposed schemes can improve the system performance.

Power Allocation Optimization for Spectrum-Efficient Multi-Pair Two-Way Massive MIMO Full-Duplex Relay over Ricean Channels

In this paper we investigate the power allocation optimization for spectrum efficient multi-pair two-way massive MIMO (TWMM) amplify-and-forward (AF) full-duplex (FD) relay over Ricean fading channels, where multiple user-pairs exchange information within pair through a AF-FD relay with very large number of antennas, while each user equipped with a single antenna. First, the zeroforcing reception/zeroforcing transmission and maximum-ratio combining/maximum ratio transmission processing matrices with imperfect channel state information at the relay are presented. Then, the unified asymptotic signal-to-interference-plus-noise ratio (SINR) expression of the system at general power scaling schemes are investigates. Finally, the joint user-relay power allocation (JURPA) scheme is proposed to improve the spectral efficiency of TWMM-AF-FD relay system. Simulation results show that the proposed JURPA scheme outperforms traditional user-side only power allocation scheme.

Joint channel allocation and power control with fairness in multi-hop cognitive radio networks

This paper investigates channel allocation and cognitive radio networks. The color-sensitive graph power control schemes in OFDM-based multi-hop coloring （CSGC） model is viewed as an efficient solution to the spectrum assignment problem. The model is extended to combine with the power con- trol strategy to avoid interference among secondary users and adapt dynamic topology. The optimiza- tion problem is formulated encompassing the channel allocation and power control with the interfer- ence constrained below a tolerable limit. Meanwhile, the proposed resource allocation scheme takes the fairness of secondary users into account in obtaining the solution of optimization. Numerical re- suits show that the proposed strategy outperforms the existing spectrum assignment algorithms on the performance of both the network throughput and minimum route bandwidth of all routes, as well as the number of connected multi-hop routes which implies the fairness among secondary users.

Framework for Random Power Allocation of Wireless Sensor Networks in Fading Channels

In naturally deaf wireless sensor networks or generally when there is no feedback channel, the fixed-level transmit power of all nodes is the conventional and practical power allocation method. Using random power allocation for the broadcasting nodes has been recently proposed to overcome the limitations and problems of the fixed power allocation. However, the previous work discussed only the performance analysis when uniform power allocation is used for quasi-static channels. This paper gives a general framework to evaluate the performance (in terms of outage and average transmit power) of any truncated probability density function of the random allocated power. Furthermore, dynamic Rayleigh fading channel is considered during the performance analysis which gives more realistic results that the AWGN channels assumed in the previous work. The main objective of this paper is to evaluate the communication performance when general random power allocation is used. Furthermore, the truncated inverse exponential probability distribution of the random power allocation is proposed and compared with the fixed and the uniform power allocations. The performance analysis for the proposed schemes are given mathematically and evaluated via intensive simulations.

A joint optimization scheme of resource allocation in downlink NOMA with statistical channel state information

In a real communication scenario,it is very difficult to obtain the real-time channel state infor-mation(CSI)accurately,so the non-orthogonal multiple access(NOMA)system with statistical CSI has been researched.Aiming at the problem that the maximization of system sum rate cannot be solved directly,a step-by-step resource allocation optimization scheme based on machine learning is proposed.First,in order to achieve a trade-off between the system sum rate and user fairness,the system throughput formula is derived.Then,according to the combinatorial characteristics of the system throughput maximization problem,the original optimization problem is divided into two sub-problems,that are power allocation and user grouping.Finally,genetic algorithm is introduced to solve the sub-problem of power allocation,and hungarian algorithm is introduced to solve the sub-problem of user grouping.By comparing the ergodic data rate of NOMA users with statistical CSI and perfect CSI,the effectiveness of the statistical CSI sorting is verified.Compared with the orthogonal multiple access(OMA)scheme,the NOMA scheme with the fixed user grouping scheme and the random user grouping scheme,the system throughput performance of the proposed scheme is signifi-cantly improved.