Max-min Power Control Research Articles

Efficient resource allocation using whale optimization for cell-free massive MIMO networks in 6G HetNet

The increase in data rate demands has driven the development of Cell-Free massive Multiple-Input Multiple-Output (CFmMIMO) technology in 6G Heterogeneous Networks (HetNets). This paper proposes an integration of Unmanned Aerial Vehicles (UAVs) and Device-to-Device (D2D) communication in CFmMIMO, aiming to enhance network capacity, coverage, and service delivery in next-generation wireless networks. The paper investigates uplink transmission in a network where Access Points (APs) have imperfect channel state information. It considers Cell-free User Equipment (CUE), UAVs, and D2D pairs, deriving closed-form uplink achievable rates for each user type. Moreover, two optimization problems are formulated to enhance user data rates: one focuses on maximizing the sum data rate through pilot assignment, while the other aims to achieve weighted max–min power control through power allocation. Furthermore, to address these problem, we propose a Modified Whale Optimization Algorithm (MWOA) and a novel Pilot Assignment based on Whale Optimization Algorithm (PAWOA). Performance studies demonstrate significant improvements over state-of-the-art algorithms.

Wireless Energy Transfer Using Chirp Waveform With Joint Subband Selection and Max-Min Power Control for IoT Networks

Wireless Energy Transfer Using Chirp Waveform With Joint Subband Selection and Max-Min Power Control for IoT Networks

Throughput and error probability improvement in downlink communication based on MIMO‐NOMA using reconfigurable intelligent surface (RIS)

SummaryThe user far from the base station (BS) can decode the signal via direct transmission or a relay network, but it cannot achieve higher throughput because of its high error probability. The MIMO‐NOMA‐Relay networks are replaced by MIMO‐NOMA‐RIS to achieve higher throughput in the proposal. The MIMO‐NOMA‐RIS sends a superimposed signal to the receiver using a multiple RIS beam scanning algorithm. They can improve the strength of the signal as well as improve the throughput and reduce the probability of error. As more reflecting surface area increases, the strength of the beam‐forming signal also increases, which means that the user could receive more signal strength than the direct transmission, amplify and forward (A&F) relay network. In multi‐user case, use the max–min power control algorithm (MMPCA) to allocate optimum power to a weaker channel strength user and achieve an optimum result. Method 1 used a single user with RIS to find the system equations. In both single‐user and multi‐user scenarios, we use MIMO‐NOMA‐RIS. Our proposed method's complexity is low because of its simplicity. In our proposed method, the MMPCA is used for optimum power allocation, and the resulting non‐convex function is converted into a convex one by using successive convex approximations. The result section shows that the RIS achieved the highest throughput and the lowest probability of error.

Improve energy efficiency and minimise BER in MIMO-NOMA system with max-min power control algorithm

Improve energy efficiency and minimise BER in MIMO-NOMA system with max-min power control algorithm

Power Minimization in Cell-Free Massive MIMO with AP Selection Algorithm

Background: The cell-free massive multiple input multiple outputs (CF M-MIMO) is the key and emerging technology for 5G and beyond, which gains more attention from many researchers and academicians. The cell-free M-MIMO enhances the system throughput, latency, spectral efficiency (SE), and energy efficiency (EE) of the communication network. Objective: In this paper, we formulate a framework for joint access point selection (APS) and power control algorithm for improving the EE and at the same time maintaining the system capacity. Methods: The max-min power control algorithm is used for efficient power allocation among the access points (APs) by using the minimum mean square error (MMSE), zero-forcing (ZF), and conjugate beam-forming (CB). Results: We were successfully able to enhance and maintain the optimal system capacity of the cellfree M-MIMO systems. Conclusion: The simulation result shows that the system capacity is improved when efficiently allocating the power with max-min power control algorithm.

Multi-stream receive-spatial modulation based cell-free MIMO downlink transmission method

Multi-stream receive-spatial modulation (MSR-SM) technology is applied to the cell-free multiple-input multiple-output (MIMO) downlink transmission, where both users and access points (APs) are multi-antenna, providing a new idea for green and energy-saving wireless communication. The power control coefficients of each AP are obtained using the sequential convex approximation (SCA) algorithm to optimize energy efficiency (EE) under the constraints of the highest transmitting power of the AP and the lowest signal interference-to-noise ratio of the user. This paper proposes an access point selection scheme based on a water-filling algorithm to reduce the number of unnecessary service APs and the total power consumption of the system. The simulation results show that the proposed power allocation algorithm outperforms the max-min power control algorithm in terms of energy efficiency. The MSR-SM-based downlink transmission mode is more energy efficient than the traditional space division multiplexing MIMO digital modulation mode. Furthermore, the SCA algorithm based on AP selection can improve energy efficiency performance compared with the power control algorithm without AP selection.

Developing the max-min power control algorithm for distributed wireless body area networks

There has been increasing research on wireless body area networks (WBANs) due to their wide range of applications, rapidly developing personal equipment and healthcare support devices. This paper investigates a WBAN model that consists of many sensors. The sensors are distributed around the body and communicate with access points (APs). In order to improve the throughput of the system, an algorithm is applied to select the AP that has the best channel conditions to receive information from every sensor. Moreover, the authors developed the max–min power control algorithm to control the transmit power of sensors in the uplink and APs in the downlink depending on their channel condition. The proposed algorithm is investigated based on several parameters, such as the number of sensors, the number of APs, the length of uplink and downlink training in samples, and so on. According to simulation results, the sensor with max–min power control achieves higher throughput in all considered scenarios, leading to a significant improvement in the WBAN system throughput for both cases of the uplink and downlink.

Energy Efficiency and Resource Allocation Optimization with MIMONOMA and Backhaul Beam-forming in User-centric Ultra-dense Networks

Background: Non-orthogonal multiple access (NOMA) is viewed as the key multiple access technology for 5G and beyond networks, attracting the attention of academics and industries. NOMA and the multiple input multiple output (MIMO-NOMA) technology can improve a system’s throughput, latency, and energy efficiency (EE) in future-generation communication networks. Objective: The objective of this paper is to achieve maximum EE by applying the Max-min Power Control Algorithm (MMPCA) through sub-channel optimization, resource allocation (RA) optimization, access point selection (APS), and user association. The EE results obtained with and without using MMPCA are compared to the RA optimization from a conventional water-filling algorithm (WFA). Method: This paper formulates a framework for user-centric (UC) joint resource allocation, such as backhaul connection via beam-forming and Access point (AP) to user connection via MIMO-NOMA. The user without interference is decoded using the NOMA principle. The MMPCA was also used to optimize cooperative power allocation, sub-channel allocation, and efficient user association. The RA for EE is framed as a mixed non-convex and non-linear function using successive convex approximation and sum ratio decoupling converted into convex and linear. A bisection method was used to achieve optimal RA, user association, and sub-channel assignment. Results and Conclusion: The simulation shows energy efficiency (EE) improvement. Similarly, it is observed that MMPCA outperforms the WFA.

Cell-Free IoT Networks With SWIPT: Performance Analysis and Power Control

In this article, the performance of simultaneous wireless information and power transfer (SWIPT) in downlink (DL) Internet of Things (IoT) networks relying on the cell-free massive multiple-input–multiple-output (CF-mMIMO) technique is investigated. In such a network, the access points (APs) beam the radio-frequency (RF) energy toward IoT sensors during the DL wireless power transfer phase. Tight closed-form expressions for DL harvested energy (HE) and achievable rate with conjugate beamforming (CB) and normalized CB (NCB) are, respectively, derived, which enable us to analyze the behaviors of CB and NCB schemes in terms of both HE and achievable rate. Apart from this, to guarantee sensor fairness with respect to the HE and achievable rate, a max–min power control strategy based on the accelerated projected gradient (APG) method is proposed. Specifically, the proposed APG-based power control is able to determine the optimal solution in closed form and is more memory efficient than the convex-solver-based counterpart. These analytical results as well as the effectiveness of the proposed power control policy are verified by experimental simulations.

Resource Allocation for TDD Cell-Free Massive MIMO Systems

In this paper, we investigate a joint resource allocation algorithm in a time-division duplex (TDD)-based cell-free massive MIMO (CFMM) system, which has great potential to improve spectrum efficiency and throughput. Because the throughput of the system is a bottleneck due to the sharing of the pilot, we attempted to alleviate pilot contamination. We propose a pilot assignment approach called user-distance-ordering-based pilot assignment (UDOPA) based on the distance between users and the center, which can be calculated by the K-means method. Then, using an access point (AP) selection algorithm, only the APs having a major impact on the macro diversity gain of a user are selected as the serving APs. In contrast to the existing AP selection algorithms, users with the same pilot are not allowed to share the same serving AP in the proposed AP selection algorithm, which also significantly reduces the complexity of data processing. Finally, a modified max–min power control scheme with teaching–learning-based optimization (TLBO) is proposed to further improve the performance of the systems and guarantee the minimum user rate. Simulation results show that the proposed joint resource allocation scheme can effectively enhance CFMM systems’ performance.

Downlink Power Control for Cell-Free Massive MIMO With Deep Reinforcement Learning

Recently, model-free power control approaches have been developed to achieve the near-optimal performance of cell-free (CF) massive multiple-input multiple-output (MIMO) with affordable computational complexity. In particular, deep reinforcement learning (DRL) is one of such promising techniques for realizing effective power control. In this paper, we propose a model-free method adopting the deep deterministic policy gradient algorithm (DDPG) with feedforward neural networks (NNs) to solve the downlink max-min power control problem in CF massive MIMO systems. Our result shows that compared with the conventional convex optimization algorithm, the proposed DDPG method can effectively strike a performance-complexity trade-off obtaining 1,000 times faster implementation speed and approximately the same achievable user rate as the optimal solution produced by conventional numerical convex optimization solvers, thereby offering effective power control implementations for large-scale systems. Finally, we extend the DDPG algorithm to both the max-sum and the max-product power control problems, while achieving better performance than that achieved by the conventional deep learning algorithm.

Rate-Splitting Assisted Massive Machine-Type Communications in Cell-Free Massive MIMO

This letter focuses on integrating rate-splitting multiple-access (RSMA) with time-division-duplex Cell-free Massive MIMO (multiple-input multiple-output) for massive machine-type communications. Due to the large number of devices, their sporadic access behaviour and limited coherence interval, we assume a random access strategy with all active devices utilizing the same pilot for uplink channel estimation. This gives rise to a highly pilot-contaminated scenario, which inevitably deteriorates channel estimates. Motivated by the robustness of RSMA towards imperfect channel state information, we propose a novel RSMA-assisted downlink transmission framework for cell-free massive MIMO. On the basis of the downlink achievable spectral efficiency of the common and private streams, we devise a heuristic common precoder design and propose a novel max-min power control method for the proposed RSMA-assisted scheme. Numerical results show that RSMA effectively mitigates the effect of pilot contamination in the downlink and achieves a significant performance gain over a conventional cell-free massive MIMO network.

Joint Power and User Grouping Optimization in Cell-Free Massive MIMO Systems

To relieve the stress on channel estimation and decoding complexity in cell-free massive multiple-input multiple-output (MIMO) systems, user grouping problem is investigated in this paper, where access points (APs) based on time-division duplex (TDD) are considered to serve users on different time resources and the same frequency resource. In addition, when quality of service (QoS) requirements are considered, widely-used max-min power control is no longer applicable. We derive the minimum power constraints under diverse QoS requirements considering user grouping. Based on the analysis, we formulate the joint power and user grouping problem under QoS constraints, aiming at minimizing the total transmit power. A generalized benders decomposition (GBD) based algorithm is proposed, where the primal problem and master problem are solved iteratively to approach the optimal solution. Simulation results demonstrate that by user grouping, the number of users served in cell-free MIMO systems can be as much as the number of APs without increasing the complexity of channel estimation and decoding. Furthermore, with the proposed user grouping strategy, the power consumption can be reduced by 2–3 dB compared with the reference user grouping strategy, and by 7 dB compared with the total transmit power without grouping.

A Low-Complexity Hybrid Linear and Nonlinear Precoder for Line-Of-Sight Massive MIMO With Max-Min Power Control

In line-of-sight (LOS) massive MIMO, there is a nonnegligible probability that the channel vectors of some users become correlated. In these correlated scenarios, nonlinear precoders can be used instead of linear precoders at the cost of high computational complexity. To reduce the complexity of nonlinear precoders, hybrid linear and nonlinear precoders have been suggested in 5G New Radio (NR). In this paper, we find the probability that there is at least one pair of correlated users and we find the average number of correlated users. We propose a hybrid linear and nonlinear precoder (HLNP) with max-min power control for which the served users are divided into two groups. By employing a proposed modified Tomlinson-Harashima Precoding (THP), we design and combine the transmit vectors of the two groups such that inter-group interference is removed. Simulation results show that by employing HLNP instead of zero-forcing, the required transmit power to assure a given average block error rate (BLER) with 95% probability is reduced. For a 64-antennas BS, when modified THP is used for 3 out of 10 users in HLNP, the transmit power is reduced by up to 4.70 dB to assure an average BLER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> using 16QAM and 64QAM constellations with NR low-density parity-check codes.

Transmission Schemes and Power Allocation for Multiuser Massive MIMO Relaying

This paper proposes and analyzes the performance of two simple transmission protocols for a multiuser massive multiple input multiple-output relaying system, where K single-antenna users transmit data to a massive-antenna destination through an N-antenna relay node. In the first transmission protocol, the relay does not need to know the CSI. It just amplifies and forwards the received signals to the destination. In the second protocol, the relay first estimates the channels from all users. It then uses the maximum-ratio combining (MRC) technique to combine all received signals and forwards them to the destination. In both protocols, the destination estimates the channels and employs MRC to decode the signals. We propose an efficient channel estimation method at the destination in which the destination estimates only the effective channels gains. As a consequence, the channel estimation overhead does not depend on the numbers of relay and destination antennas. We derive closed-form expressions for the spectral efficiency of the two transmission protocols. These results allow us to further analyze the system performance and to allocate the transmit powers. Particularly, a max-min power control algorithm is proposed which selects the transmit powers at the relay and users to maximize the lowest spectral efficiency of all users. We show that, by using the max-min power allocation algorithm, the spectral efficiency can be increased significantly, compared to uniform power allocation. Furthermore, if the distance between the users and the relay is large, the first transmission protocol is better and vice versa if this distance is small.

Cell-Free Massive MIMO Systems With Oscillator Phase Noise: Performance Analysis and Power Control

Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising technology for satisfying higher rate requirement of users in beyond-5G networks. This paper investigates the impact of phase noise on the performance of cell-free MIMO systems which employ a large number of multi-antenna access points (APs). For both downlink and uplink transmissions, we derive the tractable closed-form achievable rate expressions when signal detection with channel state information (CSI) and channel distribution information (CDI), respectively, by utilizing the extended deterministic equivalence method in random matrix theory. The closed-form achievable rate expressions provide efficient evaluation for the impact of phase noise on system performance. The analytical results indicate that the phase noise influences the power of the desired signal, the beamforming uncertainty gain, and the inter-user interference increment caused by the pilot contamination. In addition, the special cases that only APs have phase noise and only users have phase noise are discussed. It is found that the impact of phase noise at users is more severe than that at APs. Besides, we discuss the system performance at low and high SNR regimes. It demonstrates that the phase noise is the major limitation of the system performance in high SNR regime while it can be neglected in low SNR regime. Furthermore, we formulate the max-min power control problem for both downlink and uplink transmissions to guarantee the users’ fairness and prove that the downlink max-min problem is a quasi-concave problem while the uplink max-min problem is a geometric programming (GP) problem. Then, the phase noise-aware max-min power control schemes for both downlink and uplink transmissions are proposed to ensure the users’ fairness. Finally, numerical results are provided to validate the analytical results and the proposed phase noise-aware power control scheme.

An Improved Successive Filter-Based Dropping Algorithm for Massive MIMO With Max-Min Power Control

In line-of-sight massive MIMO, there are use cases where the channel vectors of some users become highly correlated. Highly correlated users lead to a large reduction in the sum-rate of linear and nonlinear precoders with max-min power control. To alleviate the loss in the sum-rate, some users can be dropped and rescheduled. The optimal dropping strategy can be found by an exhaustive search. In this letter, a successive filter-based dropping algorithm (SFDA) is proposed, which improves upon the existing dropping algorithms in the literature. At each step, the user with the highest filter norm is dropped. By comparing the sum-rate of all the steps, the best set of dropped users is found. In contrast to previous threshold-based algorithms in the literature, SFDA does not require a predefined threshold for the spatial correlation of users. Compared to an exhaustive search, the complexity of SFDA is reduced. Simulations results show when a 100 antennas base station serves 10 users, SFDA improves the 5th percentile sum-rate compared to previous algorithms in the literature up to 6 bits/channel use.

Exploiting Cell-Free Massive MIMO for Enabling Simultaneous Wireless Information and Power Transfer

The performance of simultaneous wireless information and power transfer (SWIPT) in downlink (DL) cell-free massive multiple-input multiple-output (MIMO) is investigated. Tight approximations to the DL harvested energy and the DL/uplink (UL) achievable rates are derived for two practical channel state information (CSI) cases by using a non-linear energy harvesting model for time-switching and power-splitting protocols. Max-min fairness-based transmit power control policies are employed to mitigate the deleterious near-far effects caused by distributed transmissions/receptions in cell-free massive MIMO. The achievable common DL energy-rate trade-off is derived, and thereby, it is shown that the proposed max-min power control guarantees user-fairness regardless of near-far effects in terms of both harvested energy and achievable rate. The benefits of user estimated DL CSI to boost the SWIPT performance are explored. These performance metrics are compared against those of the conventional co-located massive MIMO, and thereby, it is revealed that the reduction of path-losses and lower average transmit powers offered by cell-free massive MIMO can be exploited to boost the energy-rate trade-off of SWIPT at the expense of increased backhaul requirements.

Impact of Max-Min Power Control, Channel Estimation and User Grouping Strategies on Uplink Massive MIMO-NOMA Systems

We analyze the uplink (UL) of a non-orthogonal multiple access (NOMA) based massive multiple input multiple output (MIMO) system and deduce new lower bounds on the achievable rate based on zero-forcing (ZF) decoding at the base station. Users are grouped to employ NOMA. In order to cancel the inter-group interference, the ZF decoder is designed as a function of channel estimates acquired based on two low overhead channel estimation schemes, namely, Scheme-I and Scheme-S. Under these estimation schemes, pilots are shared among users in a group while they are orthogonal across groups. User grouping and power allocation are two pivotal ways to regulate the performance of users in a NOMA system. Thus, to ensure uniform quality-of-service to all users, we obtain the max-min power control coefficients which maximize the minimum achievable rate. Furthermore, we investigate two different user grouping strategies, namely, non-scalable near-far grouping and scalable neighbor grouping, and study their impact on the UL max-min achievable rate. We benchmark against the performance obtained with the maximum ratio decoder. Extensive analysis and simulations show that in a substantial portion of the over-loaded regime and the entire under-loaded regime, ZF decoder designed using channel estimates acquired via Scheme-S with near-far grouping gives the highest max-min achievable rate among all the considered decoding and grouping strategies.

Uplink transmission design for crowded correlated cell-free massive MIMO-OFDM systems

In cell-free massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, user equipments (UEs) are served by many distributed access points (APs), where channels are correlated owing to finite angle-delay spread in realistic outdoor wireless propagation environments. Meanwhile, the number of UEs is growing rapidly for future fully networked society. In this paper, we focus on the uplink transmission design in crowded correlated cell-free massive MIMO-OFDM systems with a limited number of orthogonal pilots. For the pilot transmission phase, we identify active UEs based on non-orthogonal pilot phase shift hopping patterns and non-orthogonal adjustable phase shift pilots (APSP). We derive a closed-form expression of mean square error of channel estimation (MSE-CE) and obtain an optimal condition for minimizing MSE-CE. According to this condition, the APSP set allocation scheme is proposed. Furthermore, for the data transmission, the max-min power control algorithm is devised to maximize the minimum spectral efficiency (SE) lower bound among active UEs. Simulation results indicate significant performance gains in terms of MSE-CE for the proposed APSP set allocation scheme. The proposed power control scheme can further improve the minimum SE among active UEs. Hence, they are crucial for crowded correlated cell-free massive MIMO-OFDM systems.