LTE Licensed Assisted Access (LTE-LAA) is a promising solution for harmonious coexistence with WiFi in unlicensed spectrum. Although LTE-LAA employs a listen-before-talk approach similar to the distributed coordination function in IEEE 802.11, it uses different parameters and varying transmission durations. As a result, achieving fair coexistence between LTE-LAA and WiFi (by any definition) remains an open question, which in a pragmatic framework, devolves to: how LTE-LAA should select its parameters. To address this issue, a multi-group model is proposed for LTE-LAA and WiFi coexistence, as a function of respective initial backoff window sizes, sensing durations, maximum backoff stages, retry limits and transmission opportunities. The network steady-state point in saturated conditions is obtained, based on which the node airtime and total network airtime are derived as functions of system parameters of LTE-LAA and WiFi networks. The analysis shows that LTE-LAA can maintain proportional fairness with WiFi by either tuning its initial backoff window size or sensing duration. In particular, the optimal initial backoff window size and number of sensing slots of LTE-LAA are both derived and verified by simulation. The significance of our analysis is two-fold: a) it exposes the result that the current standard-proposed parameter settings will not generically achieve fairness and thereafter b) suggests optimal settings whereby LTE-LAA and WiFi nodes can achieve equal per-node airtime. It is further revealed that the initial backoff window size tuning of LTE-LAA could be a preferable option for achieving fairness between LTE-LAA and WiFi in practical scenarios as it requires less system information and achieves better precision.

Multi-link operation is regarded as a crucial feature in the upcoming WiFi 7 networks, which allows a single multi-link device (MLD) to make concurrent data transmissions over multiple links. To facilitate synchronous multi-link channel access, IEEE 802.11 Task Group BE has proposed various channel access schemes, such as Longest Backoff (LB) access and Shortest Backoff (SB) access. However, the coexisting performance of WiFi 7 networks with multiple channel access schemes remains largely unexplored. In this paper, we develop an analytical model to evaluate the data rate and mean access delay performance of a multi-link WiFi 7 network with two types of devices adopting LB and SB, respectively, each employing different initial backoff window sizes. The ratio of device data rates between LB-MLDs and SB-MLDs is inversely correlated with the number of links, and the ratio of their initial backoff window sizes, indicating potential unfairness if the backoff parameters are not appropriately chosen. The optimal initial backoff window sizes to maximize the network sum rate and minimize the mean access delay under a given data rate ratio are further derived and verified by simulation results. The maximum network sum rate scales with the number of links, and is independent of the target fairness requirement or number of devices. Conversely, the minimum mean access delay for each type of devices, is strongly influenced by the target fairness requirement, and shows a linear increase with the network size.

Optical design plays an important role in improving the performance of opto-electronic devices. However, conventional design processes using finite difference time domain (FDTD) or finite element methods are usually time and computing resource consuming, and often result in sub-optimal solutions due to an incomplete search of the parameter state space. In this paper, we propose a deep learning approach to predict and optimize the cell performance of perovskite/crystalline-silicon (c-Si) tandem solar cells. In particular, a deep neural network is established to predict the achievable short-circuit current for tandem solar cells with a given cell structure. After training on a FDTD numerical simulation data set, the proposed deep neural network achieves an accuracy of 98.3% and micro-second grade simulation time, which is an ultra-fast, highly accurate and computing resource-saving solution to investigate the current properties of tandem solar cells. Heuristic algorithms are further adopted to inversely optimize the device structure, where the optimal set of layer thicknesses is obtained to maximize the achievable short-circuit current. According to the calculated projected efficiency, the expected experimental short-circuit current and power conversion efficiency of tandem solar cells with the optimal selection of layer thickness can reach 15.79 mA cm⁻² and 23.24%, which is improved by 14.42% and 28.4%, respectively, compared to the benchmark cells.

Different from the conventional packet-based random access schemes where each data packet needs to contend for channel access, with connection-based random access, a connection is first established before data packet transmission. Despite the consensus that there exists a critical threshold of the data packet transmission time, only above which establishing a connection is beneficial, characterization of such a threshold has received little attention. In this paper, a comparative study will be presented on the optimal throughput performance of packet-based random access and connection-based random access to characterize criteria for beneficial connection establishment. Based on a unified channel-centric model, explicit expressions of the maximum effective throughput are obtained for both packet-based and connection-based Aloha and Carrier Sense Multiple Access (CSMA). The analysis shows that whether connection establishment is beneficial crucially depends on the sensing capability of nodes. The threshold of data packet transmission time with Aloha is found to be much lower than that with CSMA, indicating that the throughput gain brought by connection establishment is more significant when sensing is absent. The analysis sheds important light on the access design of machine-to-machine (M2M) communications.

For IEEE 802.11 networks with multiple basic service sets (BSSs), most studies have focused on how to allocate different frequency sub-channels to BSSs for minimizing the co-channel interference. With the significant increase of the sub-channel bandwidth, however, it becomes increasingly important to study the network performance with universal frequency reuse. In this paper, we focus on an uplink M-BSS IEEE 802.11 network, where all the BSSs share the frequency band rather than operate at different sub-channels. By dividing the nodes in each BSS into multiple groups according to the set of access points (APs) they can be heard by, the steady-state points of M BSSs in saturated conditions are obtained as the functions of the number of nodes in each group and the initial backoff window size of nodes of each BSS. The maximum network throughput is further characterized by optimally choosing the initial backoff window sizes of all the nodes and shown to be closely dependent on the percentage of nodes that can be heard by multiple APs. The comparison with orthogonal frequency division reveals that although the maximum network throughput is degraded due to interference among BSSs, a higher network data rate can still be achieved by universal frequency reuse, which makes it a preferable option for multi-BSS IEEE 802.11 networks.

In the IEEE 802.11ax Wireless Local Area Networks (WLANs), Orthogonal Frequency Division Multiple Access (OFDMA) has been applied to enable the high-throughput WLAN amendment. However, with the growth of the number of devices, it is difficult for the Access Point (AP) to schedule uplink transmissions, which calls for an efficient access mechanism in the OFDMA uplink system. Based on Multi-Agent Proximal Policy Optimization (MAPPO), we propose a Mean-Field Multi-Agent Proximal Policy Optimization (MFMAPPO) algorithm to improve the throughput and guarantee the fairness. Motivated by the Mean-Field games (MFGs) theory, a novel global state and action design are proposed to ensure the convergence of MFMAPPO in the massive access scenario. The Multi-Critic Single-Policy (MCSP) architecture is deployed in the proposed MFMAPPO so that each agent can learn the optimal channel access strategy to improve the throughput while satisfying fairness requirement. Extensive simulation experiments are performed to show that the MFMAPPO algorithm 1) has low computational complexity that increases linearly with respect to the number of stations 2) achieves nearly optimal throughput and fairness performance in the massive access scenario, 3) can adapt to various diverse and dynamic traffic conditions without retraining, as well as the traffic condition different from training traffic.