In order to achieve the ubiquitous connectivity expected by 6G wireless networks, in this paper, an artificial intelligence (AI) framework is proposed for integrating intelligent omni-surface (IOS) into the cell-free (CF) network while leveraging millimeter wave frequency bands. The proposed AI framework allows the wireless network to allocate the necessary power for beamforming from the required base stations (BSs) to serve the users positioned on both sides of the IOS, thereby extending the wireless system's coverage area. An optimization problem is formulated to maximize the achievable rates of the users, thereby maximizing the signal-to-interference-plus-noise ratio (SINR) while saving power to support the maximum number of users in the proposed CF network. To solve the formulated NP-hard problem, a novel AI-based framework is proposed that allocates power to users on both sides of the IOS, enabling fulldimensional coverage. In particular, a deep deterministic policy gradient (DDPG)-based deep reinforcement learning approach is employed to allocate the required power for beamforming from the necessary BSs in the CF network, effectively serving users on both sides of the IOS. Simulation results show that the proposed DDPG-based framework outperforms state-of-the-art baselines such as advantage actor-critic and deep Q-network methods, achieving cumulative SINR improvements ranging from 5.21 dB to 5.23 dB and 6.43 dB to 6.96 dB, and cumulative achievable rate enhancements ranging from 5.45 bps/Hz to 5.47 bps/Hz and 6.67 bps/Hz to 7.20 bps/Hz, correspondingly, considering users only on one side and on both sides of the IOS.

The 6G wireless communication networks need an intelligent networking system to meet the ever-increasing demands of various applications and mobile devices to ensure power savings, energy efficiency (EE), high integration of devices, and mass connection. To achieve these aims, an artificial intelligence (AI)-based holographic MIMO (HMIMO)-aided cell-free (CF) network is suggested to allocate desired power for beamforming by activating the required number of grids from the serving HMIMOs for serving the users. An optimization problem is developed to ensure effective power allocation that maximizes the EE of the system. A Transformer-based AI framework is proposed to solve the formulated NP-hard problem that distributes desired power for serving the users by activating the required number of grids from the required number of serving HMIMOs in the CF network. Finally, simulation results represent that the proposed power allocation framework outperforms the gated recurrent unit and long short-term memory-based mechanisms, achieving a combined power savings of 12.5% and 4.06%, and a combined EE improvement of 14.68% and 8.93%, correspondingly. Therefore, our suggested AI-based framework guarantees effective power allocation for beamforming to serve the users.

The sixth-generation wireless networks are required to satisfy the ever-increasing demands of diverse applications to guarantee power savings, energy efficiency, mass connectivity, and higher integration of devices. To accomplish these goals, in this paper, an artificial intelligence (AI)-based holographic MIMO (HMIMO)-empowered cell-free (CF) network is proposed while leveraging integrated sensing and communication (ISAC). The proposed AI-based framework allocates the desired power for beamforming by activating the required number of grids from the serving HMIMO base stations (BSs) in the CF network to serve the users. An optimization problem is formulated that maximizes the sensing utility function, which in turn maximizes the signal-to-interference-plus-noise ratio (SINR) of the received signal, the sensing SINR of the reflected echo signal, as well as energy efficiency, ensuring efficient power allocation. To solve the optimization problem, an AI-based framework is proposed to enable a decomposition of the NP-hard problem into two subproblems: a sensing subproblem and a power allocation subproblem. Initially, a variational autoencoder (VAE)-based scheme is utilized to solve the sensing subproblem that identifies the current location of the users with the sensing information. Then, a transformer-based mechanism is devised to allocate the desired power to users by activating the required grids from the serving HMIMO BSs in the CF network based on the sensing information achieved with the VAE-based scheme. Simulation results demonstrate that the proposed AI-based framework performs better than the long short-term memory, gated recurrent unit-based mechanisms, with cumulative power savings of 8.64%, and 16.02%, and cumulative energy efficiency of 14.49%, and 16.61%, accordingly, taking the ground truth values into consideration. Therefore, the proposed AI-based framework ensures efficient power allocation for beamforming using ISAC to serve heterogeneous users.

This paper proposes an artificial intelligence (AI) framework that leverages integrated sensing and communication (ISAC), aided by the age of sensing (AoS) to ensure the timely location updates of the users for a holographic MIMO (HMIMO)- enabled wireless network. The AI-driven framework guarantees optimal power allocation for efficient beamforming by activating the minimal number of grids from the HMIMO base station. An optimization problem is formulated to maximize the sensing utility function, aiming to maximize the signal-to-interference-plus-noise ratio (SINR) of the received signal, beam-pattern gains to improve the sensing SINR of reflected echo signals and maximizing the evidence lower bound minus loss function, which in turn minimizes the losses of the ISAC process, and maximizes achievable rate for efficient power allocation. A novel AI-driven framework is presented to tackle the formulated NP-hard problem by decomposing it into two problems: a sensing problem and a power allocation problem. The sensing problem is solved by employing a variational autoencoder (VAE)-based mechanism that obtains the sensing information leveraging AoS, which is used for the location update. Subsequently, a deep deterministic policy gradient-based deep reinforcement learning scheme is devised to allocate the desired power by activating the required grids based on the findings achieved with the VAE-based mechanism. Simulation results demonstrate the superior performance of the proposed AI framework compared to advantage actor-critic and deep Q-network-based methods, achieving a cumulative average SINR improvement of 8.5 dB and 10.27 dB, and a cumulative average achievable rate improvement of 21.59 bps/Hz and 4.22 bps/Hz, respectively.

The impending sixth-generation wireless communication networks are anticipated to guarantee mass connectivity, high integration, and lower power consumption for generating the required beamforming. To achieve these goals, an artificial intelligence (AI) framework is proposed by utilizing holographic MIMO-assisted integrated sensing, localization, and communication. The proposed AI framework ensures lower power consumption to activate the minimum number of grids from the holographic grid array for the generation of holographic beamforming. An optimization problem is formulated to maximize the signal-to-interference-plus-noise ratio received by the users, which in turn maximizes the utility function for sensing considering the user distances, beampattern gains, sensingcommunication loss, and dense locations controlling parameter. A novel AI-based framework is proposed to solve the formulated NP-hard optimization problem by decomposing it into two subproblems: the sensing problem and the communication resource allocation problem. First, a variational autoencoder (VAE) based mechanism is devised to solve the sensing problem mitigating the disputes to obtain the users’ exact location. Second, a sequential neural network-based scheme is utilized to allocate the communication resources to the heterogeneous users for generating the desired beamforming based on the findings of the VAE-based mechanism. Moreover, an extreme case power allocation strategy is presented once a large number of users enter the system. The extreme case power allocation strategy applies when the total power prediction exceeds the total system power for allocating the communication resources to the users. Finally, simulation results validate that the proposed AI-based framework outperforms the long short-term memory method with a cumulative power savings of 34.02% taking the ground truth power into account. Therefore, the proposed AI framework generates effective beamforming to serve the communication users.