Expected Sum Rate Research Articles

Optimal Position and Target Rate for Covert Communication in UAV-Assisted Uplink RSMA Systems

With the explosive increase in demand for wireless communication, the issue of wireless communication security has also become a growing concern. In this paper, we investigate a novel covert communication for unmanned aerial vehicle (UAV)-assisted uplink rate-splitting multiple access (RSMA) systems, where a UAV adopts the rate-splitting (RS) strategy to increase the total transmission rate while avoiding deteriorating the covert transmission of a ground user. In the proposed system, a ground user and a UAV adopt the RSMA scheme to simultaneously communicate with a base station surveilled by an evil monitor. The UAV acts as both the transmitter and the friendly jammer to cover the ground user’s transmission with random power. To maximize the expected sum rate (ESR), we first study the RS strategy and obtain the optimal power allocation factor. Then, the closed-form of minimum detection error probability (DEP), ESR, and optimal target rate of the UAV are derived. Constrained by the minimum DEP and expected covert rate (ECR), we maximize the ESR by optimizing the position and target rate of the UAV. Numerical results show that the proposed scheme outperforms the traditional NOMA systems in terms of ESR with the same DEP and ECR.

Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control

The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks. To enhance the network capacity and reliability, we propose a learning-based dynamic time-frequency division duplexing (D-TFDD) scheme that adaptively allocates the uplink and downlink time-frequency resources of base stations (BSs) to meet the asymmetric and heterogeneous traffic demands while alleviating the inter-cell interference. We formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP) that maximizes the long-term expected sum rate under the users' packet dropping ratio constraints. In order to jointly optimize the global resources in a decentralized manner, we propose a federated reinforcement learning (RL) algorithm named federated Wolpertinger deep deterministic policy gradient (FWDDPG) algorithm. The BSs decide their local time-frequency configurations through RL algorithms and achieve global training via exchanging local RL models with their neighbors under a decentralized federated learning framework. Specifically, to deal with the large-scale discrete action space of each BS, we adopt a DDPG-based algorithm to generate actions in a continuous space, and then utilize Wolpertinger policy to reduce the mapping errors from continuous action space back to discrete action space. Simulation results demonstrate the superiority of our proposed algorithm to benchmark algorithms with respect to system sum rate.

Design of Semipersistent Resource Allocation in LTE-V Network

Radio network access technology currently used in 4G/5G is Long Term Evolution-Advanced (LTE-A), which was developed by 3rd Generation Partnership Project (3GPP). Device-to-device (D2D) communication is a technology enabling direct communications among wireless devices without forwarding through an evolved Node B (eNB). Moreover, D2D transmission can support vehicles as a vehicle-to-vehicle (V2V) environment. It is possible to avoid accidents via exchanging movement-related information among vehicles and effectively increase driving safety (and efficiency). However, radio resources are limited in radio networks. A vehicle transmits through D2D in Long Term Evolution-Vehicle (LTE-V) mode-3 standard, and an eNB can allocate the same spectrum radio resources for cellular and V2V links simultaneously. When using the same radio resources, the probability of interference may increase. This study designed a semipersistent resource allocation algorithm based on different cycles in an LTE-V network. Moreover, resource allocation under different cycles was analyzed, and a scheme for resource selection is proposed based on cycle size. The proposed Semi Persistent Gain Aware Resource Allocation (SP-GARA) scheme selects resources based on the expected sum rate of the cycle size and analyzes and discusses the results of the total sum rate at different cycles and speeds for an improved performance.

Dual-spectrum resource allocation for coexisting LTE-advanced and Wi-Fi systems with imperfect channel state information

Long term evolution (LTE) and LTE-Advanced (LTE-A) based transmissions extended from licensed to unlicensed bands, known as LTE-unlicensed and licensed assisted access, can effectively alleviate the congestion of cellular bands. Efficient integration of the dual-spectrum in small-cells can further improve the achievable spectrum efficiency. In this paper, we optimize the power and resource allocation for LTE small-cells by jointly utilizing licensed and unlicensed bands, where practical issues like imperfect channel state information (CSI) and the characteristics of LTE unlicensed traffic are considered. More specifically, we formulate a stochastic mixed-integer non-linear programming (MINLP) problem to maximize the expected sum-rate (ESR) of the system. To solve this NP-hard problem, the so-called reduced-complexity joint licensed and unlicensed resource allocation (RC-JLURA) algorithm is proposed, where the original problem is transformed to a deterministic MINLP problem by employing an analytic approximation. Thanks to the Lagrange dual optimization based on a framework of alternate iteration, the proposed method can provide a near-optimum closed-form solution to the problem concerned, while maintaining a low computational complexity. The achievable performance of the LTE dual-spectrum mechanism is evaluated through simulations, which demonstrate the superiority of the proposed algorithm.

Robust Design of Multicell D2D Communication Under Partial CSI

We consider device-to-device (D2D) communication underlaid in a cellular network to share the uplink resource of cellular users (CUs). It is a key the emerging Internet of Things to support vehicle-to-everything communication networks. In a multicell scenario, both D2D pairs and CUs may cause significant significant intercell interference (ICI) to the neighboring cells. Furthermore, due to substantial signaling overhead, we assume only partial channel state information (CSI) of D2D links at the base station. We consider joint power control, beamforming, and CU-D2D matching problem, assuming partial CSI from D2D pairs under the general Nakagami fading model. We formulate a joint receive beamforming and robust power control optimization problem for a CU-D2D pair to expected sum rate under the power budget, while meeting the minimum SINR requirements and worst case ICI limits at neighboring cells in a probabilistic sense. We propose an efficient algorithm that combines an iterative D2D feasibility check and a ratio-of-expectation approximation. A performance upper bound is also developed for benchmarking. For multiple CUs and D2D pairs, due to orthogonal channelization within each cell, we first focus on the problem of joint power control and beamforming for a CU-D2D pair and show how our proposed solution can be leveraged to find a solution for this general problem. The complexity analysis of the proposed approach is also provided. Simulation results show that the proposed algorithm gives performance close to the upper bound.

NOMA With Guaranteed Weak User QoS: Design and Analysis

Non-orthogonal multiple access (NOMA) in the power domain serves multiple users at the same time and frequency slot. The significance of NOMA is its ability to increase the sum rate of the system and serve more users relative to orthogonal techniques. In this paper, we look at the high rate NOMA implementations where a single beamforming vector is shared by multiple users in the same cluster in a NOMA zero-forcing beamforming (NOMA-ZFBF) system. In order to maintain user fairness, it is important to guarantee the quality of service (QoS) of all clustered users. First, considering a target signal to interference plus noise ratio (SINR) for the weak user, i.e., guaranteeing the weak user's QoS (W-QoS), we propose a simple W-QoS-based NOMA (W-QoS NOMA) algorithm for NOMA cluster formation and power allocation that always exceeds the time division multiple access rates. Second, the correlation between the users plays a vital role during NOMA cluster formation in NOMA-ZFBF system. Therefore, we integrate the proposed W-QoS with partially correlated user clustering algorithms in order to analyze the effect of using the W-QoS technique. The numerical results confirm that the integration of W-QoS with partially correlated user clustering algorithms improves the sum capacity of the system. Third, considering an arbitrary correlation between users, we derive the signal to noise ratio (SNR)/SINR expressions of strong and weak users. These expressions show the importance of correlation factor which has a higher impact on the power penalty of the strong user, which in turn affects the clustering rate. Finally, we devise a mathematical technique using a perfectly correlated channel for the strong and weak users in each NOMA cluster to derive the exact SNR/SINR distributions of singletons, strong, and weak users. This gives a closed form expression for the expected sum rate, which gives an upper bound on performance. Furthermore, we investigate how close that realistic partially correlated user clustering can get to this bound.

The Ergodic Capacity of the Multiple Access Channel Under Distributed Scheduling - Order Optimality of Linear Receivers

Consider the problem of a multiple-input multiple-output multiple-access channel at the limit of large number of users. Clearly, in practical scenarios, only a small subset of the users can be scheduled to utilize the channel simultaneously. Thus, a problem of user selection arises. However, since solutions which collect channel state information from all users and decide on the best subset to transmit in each slot do not scale when the number of users is large, distributed algorithms for user selection are advantageous. In this paper, we analyze a distributed user selection algorithm, which selects a group of users to transmit without coordinating between users and without all users sending CSI to the base station. This threshold-based algorithm is analyzed for both zero-forcing and minimum mean square error receivers, and its expected sum rate in the limit of large number of users is investigated. It is shown that for large number of users, it achieves the same scaling laws as the optimal centralized scheme.

Coordinated Multi-Point Transmission With Imperfect CSI and Other-Cell Interference

Coordinated Multi-Point (CoMP) transmission for LTE-Advanced systems promises enhanced throughput and coverage performance, especially for cell edge users. However, the performance of CoMP systems heavily depends on the feedback quality and channel imperfections. In this paper, we investigate the impact of quantized and delayed channel state information (CSI) on the average achievable rate of joint transmission (JT) and coordinated beamforming (CBF) systems. We derive closed-form expressions and accurate approximations on the expected sum rate of CoMP systems with imperfect CSI assuming small-scale Rayleigh fading, pathloss attenuation, and other-cell interference (OCI). Furthermore, for analytical tractability, we employ a moment matching technique that approximates the distributions of the received desired and interference signals. Based on our analytical framework, we show that CBF and JT to multiple users are more sensitive to CSI imperfections than single-user JT and we identify switching points and optimal operating regimes for each scheme. Furthermore, we propose an adaptive multimode transmission technique that switches between CoMP schemes to maximize the sum rate. Finally, the proposed approximate framework enables us to identify key system parameters, such as feedback resolution, delay, pathloss, and transmit SNR for which CoMP becomes a judicious choice of transmission strategy as compared to non-cooperative transmission.

Resource Allocation for OFDMA-Based Cognitive Radio Multicast Networks With Primary User Activity Consideration

This paper considers the primary user activity or the subchannel availability in optimally distributing the available resources for an orthogonal frequency-division multiple-access (OFDMA) cognitive radio multicast network. For this purpose, a risk-return model is presented, and a general rate-loss function, which gives a reduction in the attainable throughput whenever primary users reoccupy the temporarily accessible subchannels, is introduced. Taking the maximization of the expected sum rate of secondary multicast groups as the design objective, an efficient joint subcarrier and power-allocation scheme is proposed. Specifically, the design problem is solved via a dual optimization method under constraints on the tolerable interference thresholds at individual primary user's frequency bands. It is shown that as the number of subcarriers gets large (which is often the case in practice), the dual-domain solution becomes globally optimum with regard to the primal problem. More attractively, the ?practically optimal? performance of this approach is achieved with a substantially lower complexity, which is only linear in the total number of subcarriers as opposed to exponential complexity typically required by a direct search method. Our proposed design is valid for unicast and multicast transmissions and is applicable for a wide range of rate-loss functions, among which, the linear function is a special case. The superiority of the dual scheme is thoroughly verified by numerical examples.