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Distributed nature of wireless sensor network raises a number of design challenges, especially when energy-efficiency and Quality of Service requirements are to be taken into consideration. These challenges can only be met by allowing closer cooperation and mutual adaptation between the protocol layers, referred to as a cross-layer design paradigm. In this paper, we explain the operating stages for adaptive sleep with adaptive modulation based on the MAC layer protocol. By using adaptive sleep with adaptive modulation the total time for completing one packet is adaptively reduced. Therefore, not only the transmission time is adapted by adaptive modulation, but also the sleep time is varied by adaptive sleep. A cross-layer, optimization scheme, based on adaptive sleep with adaptive modulation along with constellation rearrangement and power control, is proposed in this paper for minimizing energy cost and enhancing the network longevity. The adaptive sleep with adaptive modulation along with constellation rearrangement algorithm changes the modulation scheme dynamically by using constellation rearrangement while adjusting the node sleep periods and power levels. The paper considers several variations of these schemes and analyzes and compares their performance under various traffic intensity based on extensive computer simulations. Finally the proposed scheme is evaluated through NS2 simulations in terms of throughput.

The performance analysis of a space-time coded multiple-input multiple-output (MIMO) system with variable-rate adaptive modulation over flat Rayleigh fading channels for both perfect and imperfect channel state information (CSI) is presented. In this study, the optimum fading gain switching thresholds for attaining maximum spectrum efficiency (SE) subject to an average bit error rate (BER) constraint are derived. The existence and uniqueness of the Lagrange multiplier in the constrained SE optimisation is studied. It is shown that the Lagrange multiplier does exist and is unique for imperfect CSI. On the other hand, the Lagrange multiplier will be unique if the existence condition for MIMO under perfect CSI is satisfied. A practical iterative algorithm based on Newton's method for finding the Lagrange multiplier is proposed. By the switching thresholds,  closed-form expressions of the SE and average BER are obtained. Simulation results for SE and BER are in good agreement with the theoretical analysis. The results show that the space-time block coded MIMO system using adaptive modulation (AM-STBC-MIMO) with average BER constraint provides SE better than AM-STBC-MIMO with fixed thresholds, and AM-STBC-MIMO using a BER upper bound, but it has performance degradation in SE for imperfect CSI.

The impact of outdated channel state information (CSI) on the performance of variable-rate adaptive M-ary quadrature amplitude modulation in amplify-and-forward (AF) opportunistic relaying systems over time-variant Rayleigh fading channels is analysed. Two rate-adaptive modulation techniques are considered. In the first scheme, the relay and the transmission rate are selected according to the CSI at the receiver side. In the second scheme, whereas selection of the relay is based on the receiver's CSI, the transmission rate is selected based on the predicted CSI at the transmitter after the feedback delay. The impact of imperfect CSI prediction on the system performance is also evaluated. For each scheme, analytical expressions are derived for the average spectral efficiency, outage probability and the average bit-error rate under outdated CSI. Using numerical evaluations the performances of the considered rate-adaptive modulation schemes are analysed to illustrate the impact of outdated CSI on AF opportunistic relaying systems.

Rate-adaptive modulation and transmit antenna selection require channel knowledge at the transmitter, typically achieved using a feedback communications path from receiver to transmitter. Feedback delay in such systems causes outdated channel knowledge at the receiver and thus suboptimal switching decisions. This study evaluates the effect of degraded switching and proposes a channel prediction scheme to mitigate against delay-induced performance degradation, specifically when deployed in Rayleigh fading channels using maximum ratio combining at the receiver. Shannon capacity expressions are found and then closed form bit-error-rate and average spectral efficiency expressions derived – all derivations supporting arbitrary number of antennas at both receiver and transmitter. These expressions are developed to allow optimal switching boundaries to be determined for M-quadrature amplitude modulation rate adaptation under different degrees of channel prediction error, system arrangement and number of antennas.

This article studies adaptive modulation for QR decomposition and successive interference cancellation receivers in multiple-input multiple-output systems over slow- and fast-fading channels. In slowly fading channels, adaptive modulation schemes with finite-rate feedback under the constraints of a constant total transmit power, discrete-rate and a target bit-error-rate (BER) are proposed. Specifically, the authors first develop adaptive modulation with modulation order feedback only. For power allocation and modulation order feedback, the authors establish an optimisation scheme that can be solved easily and analyse the effect of power-level quantisation on the achievable data rates. For the fast fading channel case, the authors analyse the probability distribution of the ‘R’ matrix and derive a scheme that only requires feedback of the statistical information of the channel for adaptive modulation with optimal bit loading to guarantee that the BER target is met.

This paper focuses on improving the efficiency of multiple-input multiple-output hybrid radio frequency/free-space optical (RF/FSO) communication systems. This is achieved by employing a combination of hybrid on-off keying (OOK) modulation, M-ary digital pulse position modulation (M-ary DPPM), and M-pulse amplitude and position modulation (M-PAPM). The study aims to analyze and enhance bit-error-rate performance using techniques such as the moment generating function, the modified Chernoff bound, and the Gaussian approximation, while accounting for challenges like amplified spontaneous emission noise, atmospheric turbulence (AT), pointing errors (PEs), and interchannel crosstalk. The proposed system model is based on a passive optical network (PON) that utilizes wavelength division multiplexing (WDM) for dense WDM (DWDM). By implementing eight DWDM channels in the C-band, each transmitting 2.5 Gbps data streams across eight spatial paths, the system achieves an aggregate throughput of 160 Gbps while maintaining compatibility with standard RF/FSO PON fiber networks. The integration of adaptive optics is also suggested to mitigate the effects of AT and PE, thereby improving modulation efficiency. The study reveals that the proposed M-ary hybrid DPPM-M-PAPM solution increases receiver sensitivity compared to OOK, ensuring greater reliability. It achieves a lower power penalty of 0.2–3.0 dB at a low coding level (M) of 2 under weak turbulence conditions.

Prediction error expansion (PEE) is an attractive approach for reversible data hiding (RDH). The key issue for PEE-based RDH is to improve the prediction accuracy and design a better modulation mapping. This paper proposes a novel prediction error modulation (PEM) scheme, which comprises a prediction neural network and a modulation mapping rule generation algorithm. We use the multi-scale feature extraction (MSFE) module and the residual dense networks (RDN) to construct the prediction neural network. In the proposed RDH scheme, the cover image is divided into two parts, the reference pixel set and the cover pixel set, based on the chessboard pattern. The prediction network serves to predict the values of the cover pixel set using the reference pixel set. An optimization model is set up to adaptively determine the optimal modulation mapping for data hiding based on the specific prediction-error histogram and payload constraint. Experimental results show the superiority of the proposed prediction neural network and the optimized modulation mapping compared with related works.

Underwater acoustic (UWA) adaptive modulation (AM) requires feedback about channel state information (CSI) but the long propagation delays and time-varying features of UWA channels can cause the CSI feedback to be outdated. When the AM mode is selected by outdated CSI, the mismatch between the outdated CSI and the actual CSI during transmission degrades the performance and can even lead to communication failure. Reinforcement learning has the ability to learn the relationships between adaptive systems and the environment. This paper proposes a deep Q-network (DQN)-based AM method for UWA communication that uses a series of outdated CSI as the system input. Our study showed that it could extract channel information and select appropriate modulation modes in the expected channels more effectively than single Q-learning (QL) without needing a deep neural network structure. Furthermore, to mitigate any decision bias that was caused by partial observations of UWA channels, we improved the DQN-based AM by integrating a long short-term memory (LSTM) neural network, named LSTM-DQN-AM. The proposed scheme could enhance the DQN’s ability to remember and process historical input channel information, thus strengthening its relationship mapping ability for state-action pairs and rewards. The pool and sea experimental results demonstrated that the proposed LSTM-DQN-AM outperformed DQN-, QL- and threshold-based AM methods.

With the development of the underwater acoustic (UWA) adaptive communication system, energy-efficient transmission has become a critical topic in underwater acoustic (UWA) communications. Due to the unique characteristics of the underwater environment, the transmitter node will almost always have outdated channel state information (CSI), which results in low energy efficiency. In this paper, we take full advantage of bidirectional links and propose an adaptive modulation and coding (AMC) scheme that aims to maximize the long-term energy efficiency of a single link by jointly scheduling the coding rate, modulation order, and transmission power. Considering the complexity characteristics of UWA channels, we proposed a bit error ratio (BER) estimation method based on deep neural networks (DNN). The proposed network could realize channel estimation, feature extraction, and BER estimation by using a fixed pilot of the feedback link. Then, we design a channel classification method based on the estimated BERs of the modulation and coding scheme (MCS) and further model the UWA channels as a finite-state Markov chain (FSMC) with an unknown transition probability. Thus, we formulate the AMC problem as a Markov Decision Process (MDP) and solve it through a reinforcement learning framework. Considering the large state-action pairs, a double deep Q-network (DDQN) based scheme is proposed. Simulation results demonstrate that the proposed AMC scheme outperforms the fixed MCS with a perfect channel information state, and achieves near-optimal energy efficiency.

For the satellite to ground data transmission in the space-based satellite network, the worst channel conditions shall be considered, that is, the link budget shall be carried out according to the lowest elevation angle and the farthest transmission distance. It is extremely urgent to make full use of the limited link power resources. An efficient adaptive data transmission method is proposed to make the satellite data transmission rate increase with the increase of elevation angle, so as to maximize the amount of data transmitted from the satellite transit window, the reliability and throughput of information transmission are improved. And the effectiveness of this method is verified by computer simulation.