Sum Transmission Rate Research Articles

Joint subchannel power allocation for downlink NOMA systems based on quantum carnivorous plant algorithm

Currently, most existing research on non-orthogonal multiple access (NOMA) systems converts power allocation into two independent procedures, i.e., inter-subchannel power allocation and intra-subchannel power allocation. However, improper inter-subchannel power allocation will adversely affect the power allocation process of intra-subchannel. If the power allocations of inter-subchannel and intra-subchannel are optimized simultaneously, it usually needs high computational complexity, and is hard to obtain an optimal solution. To tackle this issue, this paper studies the joint subchannel power allocation for NOMA systems and proposes a new intelligent algorithm called quantum carnivorous plant algorithm (QCPA) for simultaneously optimizing inter-subchannel and intra-subchannel power allocations. Analysis of the convergence of QCPA verified its superior performance on the test functions. By utilizing the optimal solution obtained by QCPA as the power allocation scheme, energy efficiency and sum transmission rate in NOMA systems are significantly increased compared to existing traditional power allocation methods. The results above demonstrate that QCPA effectively addresses test functions and the power allocation problem for NOMA. QCPA possesses favorable convergence in comparison to other comparison algorithms and methods.

A cognitive spectrum allocation scheme for data transmission in smart distribution grids

As the communication needs in the smart distribution grid continue to rise, using existing resources to meet this growing demand poses a significant challenge. This paper researches on spectrum allocation strategies utilizing cognitive radio (CR) technology. We consider a model containing strong time-sensitive and regular communication service requirements such as distribution terminal communication services, which can be seen as a user with primary data (PD) and weak time-sensitive services such as power quality monitoring, which can be seen as a user with secondary data (SD). To fit the diversity of services in smart distribution grids (SDGs), we formulate an optimization problem with two indicators, including the sum of SD transmission rates and the maximum latency of them. Then, we analyze the two convex sub-problems and utilize convex optimization methods to obtain the optimal power and frequency bandwidth allocation for the users with SD. The simulation results indicate that, when the available transmission power of SD is low, Maximization of Transmission Sum Rate (MTSR) achieves lower maximum transmit time. Conversely, when the available transmission power is high, the performance of Minimization of the Maximum Latency (MML) is better, compared with MTSR.

Deep Reinforcement Learning Based End-to-End Multiuser Channel Prediction and Beamforming

In this paper, reinforcement learning (RL) based end-to-end channel prediction (CP) and beamforming (BF) algorithms are proposed for multi-user downlink system. Different from the previous methods which either require perfect channel state information (CSI), or estimate outdated CSI and set constraints on pilot sequences, the proposed algorithms have no such premised assumptions or constraints. Firstly, RL is considered in channel prediction and the actor-critic aided CP algorithm is proposed at the base station (BS). With the received pilot signals and partial feedback information, the actor network at BS directly outputs the predicted downlink CSI without channel reciprocity. After obtaining the CSI, BS generates the beamforming matrix using zero-forcing (ZF). Secondly, we further develop a deep RL based two-layer architecture for joint CP and BF design. The first layer predicts the downlink CSI with the similar actor network as in the CP algorithm. Then, by importing the outputs of the first layer as inputs, the second layer is the actor-critic based beamforming layer, which can autonomously learn the beamforming policy with the objective of maximizing the transmission sum rate. Since the learning state and action spaces in the considered CP and BF problems are continuous, we employ the actor-critic method to deal with the continuous outputs. Empirical numerical simulations and the complexity analysis verify that the proposed end-to-end algorithms could always converge to stable states under different channel statistics and scenarios, and can beat the existing traditional and learning based benchmarks, in terms of transmission sum rate.

Overlapping Coalition Game for Resource Allocation in Many-to-Many D2D Communication

Device-to-device (D2D) communication is one of the promising technologies for the next-generation cellular network, which uses direct communication between two neighboring devices to obtain a high transmission rate. This paper focuses on wireless resource allocation in a many-to-many D2D communication scenario to maximize the D2D transmission sum rate. Through the model analysis, the objective function is formulated while guaranteeing the minimum transmission rate of both the cellular users (CUs) and the D2D pairs. Based on the candidate sequence, an overlapping coalition game algorithm is proposed to enhance the D2D transmission sum rate. Furthermore, the preference sequence of CUs is adopted in the coalition initialization. According to the interference intensity, a power allocation scheme is designed further to improve the transmission sum rate of the D2D pairs. The simulation results have verified the validity of the proposed algorithm.

Hybrid LSTM‐PCA‐Powered Renewable Energy‐Based Battery Life Prediction and Management for IoT Applications

To ensure self‐sustainable and long‐term requirement of Internet of Things (IoT) operation, energy harvesting (EH) is a promising technique. In this work, battery prediction problems and joint access control are studied in a IoT cell system with a base station (BS) and EH user equipment (UE) with limited uplink access channels. Every UE present holds a limited capacity rechargeable battery. In order to address this issue, we take into consideration the uplink wireless system with “N” EH user equipment (UE) along with the base station. To build the UE uplink access control, the first step involves the application of hybrid Q network (HQN) with long short‐term memory (LSTM) to tackle the access control issue. This technique maximizes the uplink transmission sum rate. The LSTM DQN network designed has two layers with battery prediction and joint access control solution. The first layer is developed to predict battery levels, while the next layer generates the access control information based on channel information along with the predicted values. This network is trained to decrease the discounted prediction loss and increase long‐term discounted sum rate, at the same time by training the two layers jointly. In recent years, IoT sensors have played a major role in collecting and monitoring data gathered from real‐world applications such as environmental monitoring, transportation, urban security, smart energy management, agriculture, and health care. Several of these appliances function with batteries. Hence, the primary task would be to predict the batter’s lifespan and provide a timeframe to replace or recharge the battery. The battery life used in an IoT network is predicted with the help of a hybrid random forest‐principal component analysis (PCA) regression algorithm with machine learning.

OFDM based bidirectional multi-relay SWIPT strategy for 6G IoT networks

6G IoT networks aim for providing significantly higher data rates and extremely lower latency. However, due to the increasingly scarce spectrum bands and ever-growing massive number IoT devices (IoDs) deployed, 6G IoT networks face two critical challenges, i.e., energy limitation and severe signal attenuation. Simultaneous wireless information and power transfer (SWIPT) and cooperative relaying provide effective ways to address these two challenges. In this paper, we investigate the energy self-sustainability (ESS) of 6G IoT network and propose an OFDM based bidirectional multi-relay SWIPT strategy for 6G IoT networks. In the proposed strategy, the transmission process is equally divided into two phases. Specifically, in phase1 two source nodes transmit their signals to relay nodes which will then use different subcarrier sets to decode information and harvest energy, respectively. In phase2 relay nodes forward signals to corresponding destination nodes with the harvested energy. We maximize the weighted sum transmission rate by optimizing subcarriers and power allocation. Our proposed strategy achieves larger weighted sum transmission rate comparing with the benchmark scheme.

Auction-Based Multichannel Cooperative Spectrum Sharing in Hybrid Satellite-Terrestrial IoT Networks

In this article, we investigate the multichannel cooperative spectrum sharing in hybrid satellite-terrestrial Internet of Things (IoT) networks with the auction mechanism, which is designed to reduce the operational expenditure of the satellite-based IoT (S-IoT) network while alleviating the spectrum scarcity issues of terrestrial-based IoT (T-IoT) network. The cluster heads of selected T-IoT networks assist the primary satellite users transmission through cooperative relaying techniques in exchange for spectrum access. We propose an auction-based optimization problem to maximize the sum transmission rate of all primary S-IoT receivers with the appropriate secondary network selection and corresponding radio resource allocation profile by the distributed implementation while meeting the minimum transmission rate of secondary receivers of each T-IoT network. Specifically, the one-shot Vickrey-Clarke-Groves (VCG) auction is introduced to obtain the maximum social welfare, where the winner determination problem is transformed into an assignment problem and solved by the Hungarian algorithm. To further reduce the primary satellite network decision complexity, the sequential Vickrey auction is implemented by sequential fashion until all channels are auctioned. Due to incentive compatibility with those two auction mechanisms, the secondary T-IoT cluster yields the true bids of each channel, where both the nonorthogonal multiple access (NOMA) and time division multiple access (TDMA) schemes are implemented in cooperative communication. Finally, simulation results validate the effectiveness and fairness of the proposed auction-based approach as well as the superiority of the NOMA scheme in secondary relays selection. Moreover, the influence of key factors on the performance of the proposed scheme is analyzed in detail.

Securing Aerial-Ground Transmission for NOMA-UAV Networks

The efficient spectrum utilization of non-orthogonal multiple access (NOMA) and flexible mobility of unmanned aerial vehicle (UAV) enable NOMA UAV to become a prospective approach for future wireless networks. However, its secure transmission remains a great challenge. in this article, we summarize several potential solutions to mitigate adversarial eavesdropping toward aerial-ground transmission in NOMA-UAV networks, and propose three effective security schemes accordingly. First, a power allocation scheme is proposed at the UAV to ensure the privacy of a specific user with the sum transmission rate of others maximized. A closed-form solution is derived. Then, beamforming is leveraged to provide efficient privacy protection for multiple users in the network via jointly optimizing the precoding vectors to avoid eavesdropping and to maximize the spectrum efficiency. Finally, we perform artificial jamming together with beamforming to protect all the users in the network. Numerical results are provided to evaluate the superiority of the proposed schemes. Some future research issues and challenges of this direction are also pointed out.

An experimental study of NOMA in underwater visible light communication system

In this paper, power domain non-orthogonal multiple access (NOMA) is introduced in underwater visible light communication (UVLC) to improve the spectrum efficiency and the sum transmission rate. A NOMA-UVLC system employing two LEDs with different colors is designed to improve the propagation efficiency, by bearing different information on different carriers. We experimentally investigate the BER performance of the system in air and water environment, respectively. The results demonstrate that the proposed NOMA system works better in underwater environment than in air environment. The total transmission rate in underwater environment achieves 117.4 Mb/s when employing red/blue LEDs in the proposed system, and 93.9 Mb/s when employing red/green LEDs in the proposed system, with a transmission distance of 1 m.

Outage of Multiple Access Channels With Applications to Performance Analysis of Massive MTC Transmissions

In this letter, we study a slotted grant-free access scheme for massive machine-type communications (mMTC) in narrowband Internet of Things (IoT) environments. We allow multiple IoT devices to transmit simultaneously at the beginning of each time slot over the multiple access channel (MAC), and the AP jointly detect the transmissions. The outage of the MAC is defined by an event that transmission rates are outside the instantaneous capacity region. Considering all the individual transmission rate constraints and the total sum transmission rate constraint, we obtain a lower bound of the outage probability of the MAC with distinct average channel quality and distinct transmission rates among devices. Then we derive the throughput of the overall system and the transmission-success probability of an individual device. We show that our analyses are very accurate and that all the performance highly depends on the turn-on probability of each device to transmit over the MAC.

An efficient power control game model for wireless sensor networks

Efficient energy usage is a major design challenge in wireless sensor networks. In this paper, an efficient power control scheme that mitigates interference and reduces the energy usage of the sensor nodes in a wireless sensor network is presented using the game theory. A non-cooperative game was formulated among the sensor nodes in the modeled network by setting a transmission power limit at the receiving nodes which ensured that the transmitting nodes transmits at the optimal power level. The utility of the sensor nodes and the interference proportion within the network was evaluated at the optimal and discrete transmit powers. The Nash equilibrium of the proposed game was studied and it corresponds to a stability point where the network performance was optimized. Simulation results showed that the proposed scheme is effective for optimization of network resource utilization, reduction in the energy consumption of the nodes, increasing the transmission sum rate, reduction of interference within the network, and improving the network capacity. Keywords : Power control, Wireless sensor network, Non-cooperative game, Interference, Energy

Device-to-Device Secure Coded Caching

This paper studies device to device (D2D) coded-caching with information theoretic security guarantees. A broadcast network consisting of a server, which has a library of files, and end users equipped with cache memories, is considered. Information theoretic security guarantees for confidentiality are imposed upon the files. The server populates the end user caches, after which D2D communications enable the delivery of the requested files. Accordingly, we require that a user must not have access to files it did not request, i.e., secure caching. First, a centralized coded caching scheme is provided by jointly optimizing the cache placement and delivery policies. Next, a decentralized coded caching scheme is developed that does not require the knowledge of the number of active users during the caching phase. Both schemes utilize non-perfect secret sharing and one-time pad keying, to guarantee secure caching. Furthermore, the proposed schemes provide secure delivery as a side benefit, i.e., any external entity which overhears the transmitted signals during the delivery phase cannot obtain any information about the database files. The proposed schemes provide the achievable upper bound on the minimum delivery sum rate. Lower bounds on the required transmission sum rate are also derived using cut-set arguments indicating the multiplicative gap between the lower and upper bounds. Numerical results indicate that the gap vanishes with increasing memory size. Overall, the work demonstrates the effectiveness of D2D communications in cache-aided systems even when confidentiality constraints are imposed at the participating nodes and against external eavesdroppers.

Reinforcement Learning-Based Multiaccess Control and Battery Prediction With Energy Harvesting in IoT Systems

Energy harvesting (EH) is a promising technique to fulfill the long-term and self-sustainable operations for Internet of Things (IoT) systems. In this paper, we study the joint access control and battery prediction problems in a small-cell IoT system including multiple EH user equipments (UEs) and one base station (BS) with limited uplink access channels. Each UE has a rechargeable battery with finite capacity. The system control is modeled as a Markov decision process without complete prior knowledge assumed at the BS, which also deals with large sizes in both state and action spaces. First, to handle the access control problem assuming causal battery and channel state information, we propose a scheduling algorithm that maximizes the uplink transmission sum rate based on reinforcement learning (RL) with deep Q -network enhancement. Second, for the battery prediction problem, with a fixed round-robin access control policy adopted, we develop an RL-based algorithm to minimize the prediction loss (error) without any model knowledge about the energy source and energy arrival process. Finally, the joint access control and battery prediction problem is investigated, where we propose a two-layer RL network to simultaneously deal with maximizing the sum rate and minimizing the prediction loss: the first layer is for battery prediction, the second layer generates the access policy based on the output from the first layer. Experiment results show that the three proposed RL algorithms can achieve better performances compared with existing benchmarks.

OFDM Based SWIPT for Two-Way AF Relaying Network

We consider a three-node two-way relaying network, where an amplify-and-forward (AF) relay helps to forward the information of source nodes by using the energy harvested from the radio frequency signals transmitted from the two source nodes. The source and relay nodes work on half-duplex, which completes the transmission in two phases. Specifically, in the first transmission phase, two source nodes transmit their signals to relay. The received signals at relay will be divided into two subcarrier groups, to perform information decoding and energy harvesting separately. In the second transmission phase, after subcarrier pairing, relay amplifies the received signals then forwards them to the two source nodes with the harvested energy. An joint resource optimization problem, including subcarrier grouping, paring and power allocation, is formulated to maximize the sum transmission rate of source nodes with power constraints. Simulation analyses show that our proposed algorithm outperforms the other two benchmark algorithms and reveal the system performance influence of relay location and total transmission power.

Location-Fair Beamforming for High Speed Railway Communication Systems

In this paper, transmission scheme for the base station with a large number of antennas in the high-speed train (HST) networks in the presence of Doppler shift is considered. A location-fair transmission scheme based on sectorized beams, in which the received signal-to-noise ratio or signal-to-interference-plus-noise ratio (SINR) inside the beam range varies little, is first proposed to counter the effect of high Doppler shift. An optimization problem for the beam design is formulated to maximize the expected average power within the beam coverage range with limited variation in the instantaneous received power in different locations. Subsequently, the two HSTs encountering scenario is considered. An optimization problem to maximize the sum transmission rate with suppressed average interference and limited variation in the instantaneous received SINR for each HST is formulated, which is nonlinear and cannot be solved directly. Alternatively, the objective function is shifted to minimizing the power consumption, for which an iterative algorithm to derive the optimal solution is designed. Through numerical results, the tradeoff between fairness and cumulated mobile service is demonstrated. Overall, a location-fair solution for the HST networks in the presence of Doppler shift is provided.

Cooperative Beamforming for Cognitive-Radio-Based Broadcasting Systems in Presence of Asynchronous Interference

To address the asynchronous interference issue in a generalized scenario with multiple primary and multiple secondary receivers, in this paper, we propose an innovative cooperative beamforming technique. The cooperative beamforming design is formulated as an optimization problem that maximizes the weighted sum transmission rate of secondary destinations while maintaining the asynchronous interference at the primary receivers below their target thresholds. In particular, we obtain the optimum beamforming directions and allocate power values among such beamforming directions. In light of the intractability of the power allocation problem, an approximation is used to convert the nonconvex and nonlinear optimization problem into a convex and linear one. Due to the multiple interference constraints corresponding to multiple primary receivers, the power allocation scheme is still complex. Therefore, we also propose a low-complexity power allocation algorithm. The proposed beamforming technique is extended for the case when cooperating CR nodes (CCRNs) have statistical channel knowledge of the primary receivers by developing an upper bound on the probability of introducing asynchronous interference at a given primary receiver beyond a given threshold value. We also propose two CCRN selection strategies for cooperative beamforming.

Power Allocation for Cache-Aided Small-Cell Networks With Limited Backhaul

Cache-aided small-cell network is becoming an effective method to improve the transmission rate and reduce the backhaul load. Due to the limited capacity of backhaul, less power should be allocated to users whose requested contents do not exist in the local caches to maximize the performance of caching. In this paper, power allocation is considered to improve the performance of cache-aided small-cell networks with limited backhaul, where interference alignment is used to manage interferences among users. Specifically, three power allocation algorithms are proposed. First, we come up with a power allocation algorithm to maximize the sum transmission rate of the network, considering the limitation of backhaul. Second, to have more users meet their rate requirements, a power allocation algorithm to minimize the average outage probability is also proposed. Third, to further improve the users’ experience, a power allocation algorithm that maximizes the average satisfaction of all users is also designed. Simulation results are provided to show the effectiveness of the three proposed power allocation algorithms for cache-aided small-cell networks with limited backhaul.

Turbo coding for the noisy 2-user binary adder channel with punctured convolutional codes

This paper investigates the use of punctured recursive systematic convolutional codes for turbo coding in a 2-user binary adder channel (2-BAC) in the presence of additive white Gaussian noise, aiming to achieve a higher transmission sum rate with reduced decoding complexity. The encoders for the 2-BAC are assumed to be block synchronized and to employ identical puncturing patterns. Iterative decoding combining the Bahl Cocke Jelinek Raviv algorithm and a two-user punctured trellis is employed. For each user and for a fixed puncturing pattern, random interleavers of length 256 bits or 1024 bits, respectively, are simulated and corresponding curves relating bit error rate versus signal to noise ratio are presented for performance comparison purposes. Computer simulation indicates that the loss in performance of a punctured turbo code can be negligible when longer interleavers are used for both users, similarly to the single user case.

Sum Transmission Rate Maximization Based Cooperative Spectrum Sharing with Both Primary and Secondary QoS-Guarantee

In this paper, we propose a sum transmission rate maximization based cooperative spectrum sharing protocol with quality-of-service (QoS) support for both of the primary and secondary systems, which exploits the situation when the primary system experiences a weak channel. The secondary transmitter ST b which provides the best performance for the primary and secondary systems is selected to forward the primary signal. Specifically, ST b helps the primary system achieve the target rate by using a fraction of its power to forward the primary signal. As a reward, it can gain spectrum access by using the remaining power to transmit its own signal. We study the secondary user selection and optimal power allocation such that the sum transmission rate of primary and secondary systems is maximized, while the QoS of both primary and secondary systems can be guaranteed. Simulation results demonstrate the efficiency of the proposed spectrum sharing protocol and its benefit to both primary and secondary systems.

Joint resource allocation with subcarrier pairing in cooperative OFDM DF multi‐relay networks

For conventional subcarrier pairing scheme in cooperative orthogonal frequency division multiplexing decode-and-forward multi-relay networks, to avoid interference, each subcarrier pair (SP) is assigned to a single relay. Over a specific subcarrier, the destination receives signals transmitted from the relay. In this study, to better exploit the degrees of spatial freedom, the authors propose to assign each SP to multiple relays. Thus, over a specific subcarrier, the destination receives signals transmitted from multiple relays. Under the total network power constraint, to maximise the sum transmission rate, they propose a joint resource allocation scheme, in which they jointly optimised the four types of resources: assisting relays selection, transmission mode selection, subcarrier pairing and power allocation. They further propose a suboptimal algorithm which can significantly reduce the computational complexity of the aforementioned optimal allocation scheme with sacrificing little on the performance. It is shown from simulation results that the author's proposed schemes have significant performance improvement over the resource allocation schemes in the literature.