The charging loads of electric vehicles (EVs) at residential premises are controlled through a tariff system based on fixed timing. The conventional tariff system presents the herding issue, such as with many connected EVs, all of them are directed to charge during the same off-peak period, which results in overloading the power grid and high charging costs. Besides, the random nature of EV users restricts them from following fixed charging times. Consequently, the real-time pricing scenarios are natural and can support optimizing the charging load and cost for EV users. This paper aims to develop charging cost optimization algorithm (CCOA) for residential charging of EVs. The proposed CCOA coordinates the charging of EVs by heuristically learning the real-time price pattern and the EV’s information, such as the battery size, current state-of-charge, and arrival & departure times. In contrast to the holistic price, the CCOA determines a threshold price value for each arrival and departure sequence of EVs and accordingly coordinates the charging process with optimizing the cost at each scheduling period. The charging cost is captured at the end of each charging activity and the cumulative cost is calculated until the battery’s desired capacity. Various charging scenarios for individual and aggregated EVs with random arrival sequences of EVs against the real-time price pattern are simulated through MATLAB. The simulation results show that the proposed algorithm outperforms with a low charging cost while avoiding the overloading of the grid compared to the conventional uncoordinated, flat-rate, and time-of-use systems.

Deploying caches at the macro base station (MBS), unmanned aerial vehicle (UAV), and mobile user caches can effectively reduce the retransmission of duplicate content in the 5G cellular wireless hotspot network. As the storage capacity of MBS is much higher than UAVs and other hotspot cache nodes, the MBS advertises its vacant storage space so that the participating nodes can rent it. In this article, we proposed an efficient load-balancing scheme by using the Stackelberg equilibrium game model. The proposed scheme sets a unit price ( $\xi$ ) based on constraints to avoid data traffic uncertainty caused by participation nodes and rent vacant space of MBS. Furthermore, we proposed an efficient scheme for the placement and delivery of hotspot content by using Knapsack and Zipf. Moreover, ensuring the device-to-device link support also minimizes transportation costs. The results validate that considering the above-mentioned techniques significantly improves the overall hotspot network performance.

Due to the design principle of parallel processing, belief propagation (BP) decoding is attractive, and it provides good error-correction performance compared with successive cancellation (SC) decoding. However, its error-correction performance is still inferior to that of successive cancellation list (SCL) decoding. Consequently, this paper proposes a novel flip-list- (FL)-enabled belief propagation (BP) method to improve the error-correction performance of BP decoding for polar codes with low computational complexity. The proposed technique identifies the vulnerable channel log-likelihood ratio (LLR) that deteriorates the BP decoding result. The FL is utilized to efficiently identify the erroneous channel LLRs and correct them for the next BP decoding attempt. The preprocessed channel LLR through FL improves the error-correction performance with minimal flipping attempts and reduces the computational complexity. The proposed technique was compared with the state-of-the-art BP, i.e., BP bit-flip (BP-BF), generalized BP-flip (GBPF), cyclic redundancy check (CRC)-aided (CA-SCL) decoding, and ordered statistic decoding (OSD), algorithms. Simulation results showed that the FL-BP had an excellent block error rate (BLER) performance gain up to 0.7 dB compared with BP, BP-BF, and GBPF decoder. Besides, the computational complexity was reduced considerably in the high signal-to-noise ratio (SNR) regime compared with the BP-BF and GBPF decoding methods.

Successive cancellation flip decoding requires a large number of extra successive cancellation decoding attempts at low signal-to-noise ratios (SNRs), resulting in high decoding complexity. In addition, it has a long decoding latency. Although modifications have been proposed in successive cancellation flip decoding, these still have high computational complexity at low SNRs due to a huge number of additional successive cancellation decoding attempts. It is desirable to detect the unsuccessful successive cancellation decoding process at an early stage in the additional successive cancellation flip attempts and stop it that can reduce the decoding complexity. This paper combines the parity-check-CRC concatenated polar codes with the low-latency simplified successive cancellation decoding and proposes a parity-check-CRC concatenated polar codes simplified successive cancellation flip (PC-CRC-SSCFlip) decoder. It further employs the parity-check vector to identify the unsuccessful simplified successive cancellation flip decoding at an early stage and terminates so that it can minimize the decoding complexity on average. Additionally, this work proposes an error-prone flipping list by incorporating the empirically observed indices based on channel-induced error distribution along with the first bit of each Rate-1 node. The proposed technique can identify more than one error-prone bit through a flipping list and correct them. In addition, the parity-check vector further narrows down the search space for the identification of erroneous decisions. Simulation results show that 60% of unsuccessful additional successive cancellation decoding attempts terminate early rather than decode the whole codeword. The proposed PC-CRC-SSCFlip decoder has approximately 0.7 dB and 0.3 dB gains over successive cancellation and successive cancellation flip decoders, respectively, at a fixed block error rate (BLER) = ${10}^{-3}$. Additionally, it reduces the average computational complexity and decoding latency of the successive cancellation flip decoder at low-to-medium SNRs while approaching successive cancellation decoding complexity at medium-to-high SNRs.

Precoding is a technique employed to enhance transmission rates in various communication systems, including massive multiple-input multiple-output (MIMO) and optical wireless communication (OWC). In this study, we focus on network massive MIMO OWC (NM-MIMO-OWC) systems and investigate the precoder design to enhance the sum rate and improve the system performance. We present the network’s massive MIMO OWC framework. By utilizing this framework, we are able to calculate the achievable sum rate. Subsequently, we consider the precoding design for maximizing the sum rate while adhering to the total power constraint. To solve this optimization problem, we provide a necessary condition of the optimal solution based on the Karush–Kuhn–Tucker (KKT) conditions, and propose a low-complexity algorithm to further enhance the efficiency of the proposed precoding technique. The numerical results demonstrate that the proposed precoder design significantly improves the transmission rate and effectively maximizes the sum rate.

Multiple-input multiple-output (MIMO) optical wireless communications (OWC) is a key technology to meet the growing demand for high data rates and reliable connectivity in sixth-generation (6G) wireless communications. This paper proposes a novel precoding framework for a user-centric network massive MIMO OWC system, using both the iterative weighted minimum mean squared error (WMMSE) and the deep learning-based Transformer model. The objective of the proposed precoding technique is to maximize the achievable sum rate under a total power constraint. In user-centric network MIMO OWC, each user is dynamically operated by multiple distributed access points (APs), thereby improving coverage and reducing inter-user interference. The design of optimal precoding in large-scale and condensed networks remains a crucial challenge due to the large number of transmitters and receivers. Traditional optimization-based precoding algorithms can achieve near-optimal performance, but, their computational complexity is high due to large-scale matrix operations. To tackle this problem, we first propose an iterative WMMSE precoding algorithm that achieves the sub-optimal solution and serves as a benchmark. Following this, we present a Transformer-based precoding approach to achieve near sub-optimal performance. The Transformer model is trained through the available channel information to predict precoding matrices efficiently with low computational complexity. The numerical results show that the proposed Transformer network model achieves better performance with low computation complexity compared to the existing conventional precoding techniques.