Energy aware resource control mechanism for improved performance in future green 6G networks

We study support for unmanned aerial vehicle (UAV) communications through a cell-freemassive MIMO architecture, wherein a large number of access points (APs) is deployed in place of largeco-located massive MIMO arrays. We consider also a variation of the pure cell-free architecture by applyinga user-centric association approach, where each user is served only from a subset of APs in the network.Under the general assumption that the propagation channel between the mobile stations, either UAVs orground users (GUEs), and the APs follows a Ricean distribution, we derive closed form spectral efficiencylower bounds for uplink and downlink with linear minimum mean square error channel estimation. Weconsider several power allocation and user scheduling strategies for such a system, and, among these,also minimum-rate maximizing power allocation strategies to improve the system fairness. Our numericalresults reveal that cell-free massive MIMO architecture and its low-complexity user-centric alternativemay provide better performance than a traditional multi-cell massive MIMO network deployment.

Cell-free massive multiple-input multiple-output (CF-mMIMO) has attracted considerable attention due to its potential for delivering high data rates and energy efficiency (EE). In this paper, we investigate the resource allocation of downlink in CF-mMIMO systems. A hierarchical depth deterministic strategy gradient (H-DDPG) framework is proposed to jointly optimize the access point (AP) clustering and power allocation. The framework uses two-layer control networks operating on different timescales to enhance EE of downlinks in CF-mMIMO systems by cooperatively optimizing AP clustering and power allocation. In this framework, the high-level processing of system-level problems, namely AP clustering, enhances the wireless network configuration by utilizing DDPG on the large timescale while meeting the minimum spectral efficiency (SE) constraints for each user. The low layer solves the link-level sub-problem, that is, power allocation, and reduces interference between APs and improves transmission performance by utilizing DDPG on a small timescale while meeting the maximum transmit power constraint of each AP. Two corresponding DDPG agents are trained separately, allowing them to learn from the environment and gradually improve their policies to maximize the system EE. Numerical results validate the effectiveness of the proposed algorithm in term of its convergence speed, SE, and EE.

Cell-free massive MIMO (CF-mMIMO) provides wireless connectivity for a large number of user equipments (UEs) using access points (APs) distributed across a wide area with high spectral efficiency (SE). The energy efficiency (EE) of the uplink is determined by (i) the transmit power control (TPC) algorithms, (ii) the numbers, configurations, and locations of the APs and the UEs, and (iii) the propagation channels between the APs and the UEs. This paper investigates all three aspects, based on extensive (~30,000 possible AP locations and 128 possible UE locations) channel measurement data at 3.5 GHz. We compare three different TPC algorithms, namely maximization of transmit power (max-power), maximization of minimum SE (max-min SE), and maximization of minimum EE (max-min EE) while guaranteeing a target SE. We also compare various antenna arrangements including fully-distributed and semi-distributed systems, where APs can be located on a regular grid or randomly, and the UEs can be placed in clusters or far apart. Overall, we show that the max-min EE TPC is highly effective in improving the uplink EE, especially when no UE within a set of served UEs is in a bad channel condition and when the BS antennas are fully-distributed.

Cell-free Massive MIMO (mMIMO) is envisaged to be a next-generation technology beyond 5G with its high spectral efficiency and superior spatial diversity as compared to that of conventional MIMO technology. The main principle is that many distributed access points (APs) cooperate to simultaneously serve all the users within the network without creating cell boundaries. This paper considers the uplink of a cell-free mMIMO system utilizing the radio stripe network architecture. We propose a novel sequential processing algorithm with normalized linear minimum mean square error (N-LMMSE) combining at every AP. This algorithm enables interference suppression in cell-free mMIMO while keeping the cost and front-haul requirements low. The spectral efficiency of the proposed algorithm is computed and analyzed. We conclude that it provides an attractive trade-off between low front-haul requirements and high spectral efficiency.

Background: Cell Free massive MIMO, containing a very large number of distributed access points (APs), which is a promising technology to provide high data rate, spectral efficiency (SE), and energy efficiency (EE). The system performance of cell-free M-MIMO is maximum when selecting optimal access points (AP) from the large number of APs. The linear precoding methods of zeroforcing (ZF) and minimum mean square error (MMSE) are utilized in this study because they are devoid of self-interference and so improve the system capacity. Objective: The objective of this study is to maximize the system data rate in a cell-free M-MIMO network. Method: To maximize the system data rate, the maximum channel gain-based Access Point Selection (MCGAPS), Distance based Access Point Selection (DAPS), and Random-Access Point Selection (RAPS) algorithms are used to pick access points (APs) in a cell-free M-MIMO network. Because the MCGAPS algorithm selects those APs with the highest channel gain, the system’s rate is improved. Result & Discussion: The DAPS algorithm is used to choose the closest APs to the user. The APs were randomly chosen using RAPS. Random user selection (RUS) algorithm schedules the same number of users. Conclusion: It is observed that the DAPS and RUS algorithms jointly improve the system rate significantly in cell-free massive MIMO system compared to the other proposed algorithms.

We consider the cell-free massive multiple-input multiple-output (MIMO) downlink, where a very large number of distributed multiple-antenna access points (APs) serve many single-antenna users in the same time-frequency resource. A simple (distributed) conjugate beamforming scheme is applied at each AP via the use of local channel state information (CSI). This CSI is acquired through time-division duplex operation and the reception of uplink training signals transmitted by the users. We derive a closed-form expression for the spectral efficiency taking into account the effects of channel estimation errors and power control. This closed-form result enables us to analyze the effects of backhaul power consumption, the number of APs, and the number of antennas per AP on the total energy efficiency, as well as, to design an optimal power allocation algorithm. The optimal power allocation algorithm aims at maximizing the total energy efficiency, subject to a per-user spectral efficiency constraint and a per-AP power constraint. Compared with the equal power control, our proposed power allocation scheme can double the total energy efficiency. Furthermore, we propose AP selections schemes, in which each user chooses a subset of APs, to reduce the power consumption caused by the backhaul links. With our proposed AP selection schemes, the total energy efficiency increases significantly, especially for large numbers of APs. Moreover, under a requirement of good quality-of-service for all users, cell-free massive MIMO outperforms the colocated counterpart in terms of energy efficiency.

We consider a cell-free massive MIMO system with multi-antennas at both access points (APs) and users. Then, the uplink spectral efficiency (SE) of this system with data power control at users and zero-forcing (ZF) combining at the APs is exploited. The numerical results show that, the SE of cell-free massive MIMO with ZF combining outperforms the system with maximum ratio (MR) combining. Moreover, additional antennas at users improves the system's SE when there are some active users in the system.

In this paper we consider cell-free (CF) massive MIMO (MaMIMO) systems, which comprise a very large number of geographically distributed access points (APs) serving a much smaller number of users. We exploit channel sparsity to tackle pilot contamination, which originates from the reuse of pilot sequences. Specifically, we consider semi-blind methods for joint channel estimation and data detection. Under the challenging assumption of deterministic parameters, we determine sufficient conditions and necessary conditions for semi-blind identifiability, which guarantee the non-singularity of the Fisher Information Matrix (FIM) and the existence of the Cramer-Rao bound (CRB). We propose a message passing (MP) algorithm which determines the exact channel coefficients in the case of semiblind identifiability. We show that the system is identifiable if the Karp-Sipser algorithm yields an empty core. Additionally, we propose a Bayesian semi-blind approach which results in an effective algorithm for joint channel estimation and multi-user detection. This algorithm alternates between channel estimation and linear multi-user detection. Numerical simulations verify the analytical derivations.

Cell-free massive MIMO (CF-mMIMO) systems promise enhanced spectral efficiency and uniform service by deploying numerous distributed access points (APs) that jointly serve users. However, centralized precoding, which offers optimal performance by leveraging global channel state information (CSI), imposes significant fronthaul overhead due to the extensive CSI exchange between APs and the central processing unit (CPU). In contrast, distributed precoding reduces fronthaul demands, but undergoes performance degradation due to the lack of coordination between APs. This paper introduces a novel framework that strikes a balance between these two scenarios by employing channel charting (CC) as a lightweight CSI compression mechanism at each AP. CC maps high-dimensional CSI into a low-dimensional pseudo-location space, capturing essential channel characteristics. These pseudo-locations are transmitted to the CPU with minimal overhead, where advanced machine learning models are employed to infer effective downlink precoding vectors, thereby approximating centralized performance while reducing fronthaul signaling. Extensive simulations on realistic synthetic datasets demonstrate that our approach achieves very promising results. This work highlights the potential of integrating CC and machine learning to enhance scalability and efficiency in CF-mMIMO networks.

The sixth generation (6G) of wireless communication networks should deliver higher data rates and spectral efficiency for novel demands and utilizations. Cell free massive MIMO is one promising technique to achieve these goals. Distribution of base stations (BSs) in this system creates more efficiency and better coverage of users than the conventional cellular system. On the other hand, deep learning methods have been proven to be competitive frameworks in comparison of traditional optimization approaches in wireless communication applications. In this paper, power allocation in cell free massive MIMO system in uplink transmission is investigated. Firstly, we achieve lower bound of spectral efficiency, which is valid for all decoders. Besides, we evaluate MAX- MIN power allocation problem from the perspective of deep learning and common optimization methods. The simulation results confirm the superior performance of the deep learning compared to traditional optimization methods. Furthermore, computations complexity of power allocation in the deep learning case are extremely low.

In this article, we consider the problem of downlink power allocation in a cell-free massive multiple-input multiple-output (m-MIMO) communication system under spectral efficiency (SE) constraints for the users. From the perspective of green communications, the power allocation is formulated as an optimization problem where the aim is to maximize the sum SE as the objective function, while limiting the transmission power of each access point (AP) and imposing lower and upper bounds on the achievable SEs of different users. The resulting optimization problem is non-convex since the objective function is non-concave and the upper bounding constraints on user SEs are non-convex. To address these difficulties, we first derive a closed-form lower bound on the sum SE (objective function) and prove that it is a quasi-concave function. Then, we relax the unwieldy upper bounding constraints on the user SEs by replacing them with linear functions, which renders the optimization problem convex. An optimal solution to the relaxed problem is finally obtained by solving a sequence of convex feasibility programs. We evaluate the performance of the proposed downlink power allocation scheme through Monte Carlo simulations under the uncorrelated and correlated shadow fading models. The results show that for both models, the proposed algorithm can lead to a significant reduction in total power consumption compared to a benchmark approach, while accurately allocating power to the APs so that the SE constraint of each user is satisfied within the imposed bounds.

Cell-free massive MIMO technology holds significant promise as a pivotal enhancement for next-generation 5G and 6G networks. This technology features a network architecture where a single central processing unit oversees multiple access points, thereby enhancing wireless communication capacity and efficacy. By enabling broader connectivity beyond the confines of standard cellular networks, it minimizes signal interruptions and improves spectral efficiency. Despite these advancements, further research is necessary to define a comprehensive implementation strategy for deploying cell-free massive MIMO systems under real-world conditions. The Signal-to-Interference-plus-Noise Ratio (SINR) plays a crucial role in wireless communications. In this paper, the problem of power allocation is addressed under the assumption of pilot contamination. Pilot contamination occurs when multiple users share the same pilot signal. Specifically, two users were assumed to have the same pilot signal, which significantly decreased the SINR and increased the interference among these users. This reduction in SINR leads to potential degradation in achievable data rates and overall system performance. The proposed algorithm aims to reduce the pilot contamination by solving a max-min optimization problem that insure a fairness among all users. The proposed algorithm obtained the optimal power coefficient associated with each user by taking the advantage of uplink-downlink duality and considering all interference terms in the calculations. The proposed algorithm find out the optimal solution in an iterative manner until the feasible solution is achieved. The methodology introduced calculation of achievable rates based on SINR, which is essential for evaluating power allocation in cell-free massive MIMO systems. Specifically, we focus on downlink sum-rate maximization and compare the effectiveness of Zero-Forcing (ZF) precoder against Conjugate Beamforming (CB) precoder.

This paper proposes a cell-free massive multiple-input multiple-output (CF-mMIMO) architecture with joint list-based detection with soft interference cancellation (soft-IC) and access points (APs) selection. In particular, we derive a new closed-form expression for the minimum mean-square error receive filter while taking the uplink transmit powers and APs selection into account. This is achieved by optimizing the receive combining vector by minimizing the mean square error between the detected symbol estimate and transmitted symbol, after canceling the multi-user interference (MUI). By using low-density parity check (LDPC) codes, an iterative detection and decoding (IDD) scheme based on a message passing is devised. In order to perform joint detection at the central processing unit (CPU), the access points locally estimate the channel and send their received sample data to the CPU via the front haul links. In order to enhance the system's bit error rate performance, the detected symbols are iteratively exchanged between the joint detector and the LDPC decoder in log likelihood ratio form. Furthermore, we draw insights into the derived detector as the number of IDD iterations increase. Finally, the proposed list detector is compared with existing detection techniques.

Virtual multiple-input-multiple-output (MIMO) systems using multiple antennas at the transmitter and a single antenna at each of the receivers have recently emerged as an alternative to point-to-point MIMO systems. This paper investigates the relationship between energy efficiency (EE) and spectral efficiency (SE) for a virtual-MIMO system that has one destination and one relay using compress-and-forward (CF) cooperation. To capture the cost of cooperation, the power allocation (between the transmitter and the relay) and the bandwidth allocation (between the data and cooperation channels) are studied. This paper derives a tight upper bound for the overall system EE as a function of SE, which exhibits good accuracy for a wide range of SE values. The EE upper bound is used to formulate an EE optimization problem. Given a target SE, the optimal power and bandwidth allocation can be derived such that the overall EE is maximized. Results indicate that the EE performance of virtual-MIMO is sensitive to many factors, including resource-allocation schemes and channel characteristics. When an out-of-band cooperation channel is considered, the performance of virtual-MIMO is close to that of the MIMO case in terms of EE. Considering a shared-band cooperation channel, virtual-MIMO with optimal power and bandwidth allocation is more energy efficient than the noncooperation case under most SE values.

This paper deals with the problem of optimal association of stations (STAs) to access points (APs) for mulicast services in IEEE 802.11 WLAN. In a multicast session, all the subscribed STAs receive the multicast data packet at the same data rate (R min ) from their respective serving APs. A higher value of R min improves the multicast throughput by completing the ongoing multicast session in lesser time. This also improves the unicast throughput as the cycle duration is shared by the unicast and multicast sessions. To provide multicast services to the STAs, we need to select a minimum cardinality subset of APs as the system message overhead depends on this cardinality. However, such a minimum cardinality subset of APs may not be possible to activate simultaneously due to the limited number of available orthogonal frequency channels. In this paper, we develop a combined greedy algorithm that selects a subset of APs with minimum cardinality for which a conflict-free frequency assignment exists and finds an association between the STAs and the selected APs that maximizes the R min value. Through simulation we have shown that the proposed algorithm selects significantly less number of APs for different R min values in comparison to the well-known metrics for multicast association like RSSI, minimum hop-distance, normalized-cost and in-range STA number.