Differentially Private Stochastic Gradient Descent (DPSGD) is widely used to train deep neural networks with formal privacy guarantees. However, the addition of differential privacy (DP) often degrades model accuracy by introducing both noise and bias. Existing techniques typically address only one of these issues, as reducing DP noise can exacerbate clipping bias and vice-versa. In this paper, we propose a novel method, DP-PMLF, which integrates per-sample momentum with a low-pass filtering strategy to simultaneously mitigate DP noise and clipping bias. Our approach uses per-sample momentum to smooth gradient estimates prior to clipping, thereby reducing sampling variance. It further employs a post-processing low-pass filter to attenuate high-frequency DP noise without consuming additional privacy budget. We provide a theoretical analysis demonstrating an improved convergence rate under rigorous DP guarantees, and our empirical evaluations reveal that DP-PMLF significantly enhances the privacy-utility trade-off compared to several state-of-the-art DPSGD variants.

Distributed-optimization-based approaches for electric vehicle (EV) charging coordination are becoming increasingly important to enable a massive-scale EV rollout without driving costly distribution network reinforcement. This article proposes an algorithm called Dis-Net-EVCD for distributedly coordinated, network-aware EV charging and discharging in unbalanced distribution grids, incorporating both EV customer economics (least cost charging) and distribution network awareness. The alternating direction method of multipliers underpins the development of Dis-Net-EVCD, wherein we seek to remove the need for central coordinating agents and allow EVs to iteratively determine their charge–discharge profiles locally via peer-to-peer communication. Numerical simulations carried out on the IEEE 13-node test feeder with 600 residential EVs demonstrate that EV customers implementing Dis-Net-EVCD yield a total operational cost reduction of 78% compared to uncoordinated EV charging, while conforming with the voltage regulatory requirements and fulfilling all of the EV charging demands ahead of their expected departure times. Moreover, Dis-Net-EVCD is shown to be approximately 60 times computationally faster than its centralized counterpart.

Many real-world interconnections among entities can be characterized as graphs. Collecting local graph information with balanced privacy and data utility has garnered notable interest recently. This paper delves into the problem of identifying and protecting critical information of entity connections for individual participants in a graph based on cohesive subgraph searches. This problem has not been addressed in the literature. To address the problem, we propose to extract the critical connections of a queried vertex using a fortress-like cohesive subgraph model known as$p$-cohesion. A user's connections within a fortress are obfuscated when being released, to protect critical information about the user. Novel merit and penalty score functions are designed to measure each participant's critical connections in the minimal$p$-cohesion., facilitating effective identification of the connections. We further propose to preserve the privacy of a vertex enquired by only protecting its critical connections when responding to queries raised by data collectors. We prove that, under the decentralized differential privacy (DDP) mechanism, one's response satisfies$(\varepsilon , \delta )$-DDP when its critical connections are protected while the rest remains unperturbed. The effectiveness of our proposed method is demonstrated through extensive experiments on real-life graph datasets.

Deep learning (DL) has become a powerful tool in many areas of research and industry, ranging from computer vision to natural language processing. Nonetheless, as DL models are trained on large amounts of sensitive data, concerns about data privacy have emerged. In light of this, differential privacy (DP) has emerged as a promising technique that provides strong privacy guarantees while allowing useful information to be extracted from the data. DP involves adding random noise to the training data or model parameters, which makes it difficult for an attacker to identify the contribution of any single data point to the final model. Despite the promising results, DP can significantly degrade the performance of DL models, especially when dealing with large datasets or complex models. To improve the balance between privacy and utility, this paper proposes a novel modification to the vanilla DP algorithm that uses a Haar wavelet transform. The proposed method achieves better utility while maintaining the same ($\varepsilon, \delta$) privacy guarantees as vanilla DP algorithms. The paper provides an analytical demonstration of the improved noise variance bounds compared to previous methods. The paper also provides a detailed analysis of the convergence performance of the proposed algorithm and shows that the Haar wavelet transform improves the accuracy and efficiency of the training process. The experimental evaluation demonstrates that the proposed method outperforms state-of-the-art algorithms on four widely used scientific benchmark datasets making this a significant contribution to DP techniques’ practical applications in DL.

Privacy has become an increasingly essential quality to consider in a software system. Privacy practices should be adopted from the first stage of software design to safeguard personal data from unintentional information leakage. Privacy patterns have been investigated by academic and industry practitioners to address different privacy issues. The presence of these patterns is both helpful and challenging for the designer. On one hand, the privacy patterns are valuable as reusable design solutions to solve common privacy problems. On the other hand, the multitude of privacy patterns makes the choice of patterns as solutions difficult for the designer. In this paper, we propose a selection model that can assist architects in deciding suitable patterns for a software system. The selection is based on the regulatory entities and architectural characteristics implicit in the patterns. We evaluate the proposed selection model through case studies and interviews with practitioners. Our evaluation accesses the applicability and usefulness of the selection model in guiding the pattern selection for architectural design and understanding the rationale of different design decisions.

This article develops a differential privacy (DP) model for short-term probabilistic energy forecasting at the low-aggregate level. The method first takes probabilistic forecasting elements from a predictor and injects noise into the mean of the forecast based on the Laplace mechanism to guarantee privacy on the mean. The standard deviation is then carefully perturbed (enlarged) to ensure the forecast to be released contains a desirable confidence interval (CI), here 95%. By doing so, customers' privacy is protected, while the user, such as grid operators or retail providers, receives the forecast containing 95% CI of the original forecast. The predictor used to capture the model and data uncertainties is based on a Bayesian neural network, which is also benchmarked against a Gaussian Process. The simulations are carried out for different levels of privacy,$\varepsilon$, and the obtained trend provides decision-makers with a clear idea of determining the appropriate$\varepsilon$. The study found that the forecasting error is smoothed out on the used dataset for$\varepsilon \geq 0.6$. The proposed model is an output perturbation approach. Accordingly, the obtained results are compared with an input perturbation approach, showing that the proposed DP model gives the best accuracy/privacy tradeoff for users. Furthermore, this study includes rooftop photovoltaic (PV) generation behind the meter, which is one of the recent transitioning challenges in energy forecasting. Detailed analysis reveals that forecasting is more challenging for periods before sunset when PV generation drop coincides with the common changing point of households' activities.

Federated learning (FL) is a machine learning framework for collaborative data mining in many scenarios (e.g. Internet of Things) due to its privacy-preserving feature. However, various attacks arise security concerns of FL, such as poisoning, backdoor, and DDoS attacks. Several blockchain-based FL schemes strengthen credibility and security without considering the increased communication overhead. Some existing work compresses local updated gradients to sign vectors to lower communication overhead at the expense of model accuracy. To address the above concerns, this paper offers a blockchain-based asynchronous SignSGD (BASS) scheme. A novel asynchronous sign aggregation algorithm is introduced to ensure model accuracy even if the local updated gradients are compressed to sign vectors. Considering the unstable network connection on IoT, a consensus algorithm that elects multiple leader nodes enables reliable global model aggregation. The introduced blockchain improves credibility and security without downgrading efficiency. Empirical studies show that BASS outperforms other schemes in efficiency, model accuracy, and security.

In this paper, we propose two decentralized Electric Vehicle (EV) charge scheduling schemes for shaping the load curve of residential communities connected to the electric grid. The first scheme is designed for Coordinated Valley-Filling (C-VF) of the load curve via only EV charging. The second scheme is designed for Coordinated Valley-Filling and Peak-Shaving (C-VF-PS) of the load curve via both EV charging and discharging. In both schemes, a set of grid-connected EVs referred to as an ‘EV Group’ (EVG) coordinates their charge (and discharge) schedules by means of an iterative routine. Specifically, at each iteration of the respective routine, each EV in the EVG updates its charge (and discharge) schedule using a water-filling based algorithm that is specifically tailored for load curve shaping. To accommodate heterogeneous EV arrival times, which are often non-deterministic, each of C-VF and C-VF-PS is implemented in two methods, which differ in the way the EVG is formed. The first method requires all the grid-connected EVs to reschedule at designated time intervals, whereas the second method requires each EV to schedule only once, yielding lower computation and communication overheads. Numerical simulation results confirm that, compared to uncoordinated EV charging, C-VF and C-VF-PS reduce the load variance (flattens the load curve) by 47% and 65%, respectively. Furthermore, Method 2 is shown to be more effective than Method 1, in terms of computation and communication overheads.

Recent advances in the Internet of Things (IoT) are reforming the health care industry by providing higher communication efficiency, lower costs, and higher mobility. Among the many IoT applications, wireless body area networks (BANs) are a remarkable solution caring for a rapidly growing aged population. Predictive transmit power control schemes improve BAN communications' reliability and energy efficiency through long-term optimal radio resources allocation that supports consistent pervasive healthcare services. Here, we propose LSTM-based neural network (NN) prediction methods that provide long-term accurate channel gain prediction of up to 2 s over nonstationary BAN on-body channels. An incremental learning scheme, which enables the LSTM predictor to operate online, is also developed for dynamic scenarios. Our main contribution is a lightweight NN predictor, “LiteLSTM,” that has a compact structure and higher computational efficiency than other variants. We show that LiteLSTM remains functional under an incremental learning scheme, with only marginal performance degradation when implemented on hand-held devices. For optimal power allocation, we develop an interquartile range (IQR)-based power control for our channel prediction. When extensively tested using empirical channel measurements at different sampling rates, our proposed methods outperform the existing state-of-the-art methods in terms of prediction accuracy, power consumption, level crossing rate (LCR), and outage probability and duration.

For future wireless networks, applications such as Industrial Internet of Things are strictly delay-sensitive. Meanwhile, millimeter-wave (mmWave) communication is a promising means to provide ultra-high data rate and ultra-low latency services to massive number of devices. In order to minimize uplink end-to-end delay in such machine-to-machine (M2M) mmWave communications, we investigate buffer-aided multi-hop relaying networks and formulate the problem as a multi-tier queueing system. We propose a Minimum-Delay relaying scheme, and by leveraging stochastic geometry, we present a tractable analytical framework to investigate the signal-to-interference-plus-noise-ratio (SINR) distribution of devices at each-tier, thereby computing the expected delay and delay outage probabilities using Lagrange optimization. A state-of-art max-SINR relaying scheme is analyzed for comparison, and the performance of Minimum-Delay relaying in 3-tier architecture is further analyzed. The derived average delay and delay outage probability are validated through simulations based on multiple cells in a dense urban scenario. Numerical results show that the proposed Minimum-Delay relaying scheme achieves significant lower average end-to-end delay than direct association or the max-SINR relaying scheme. Furthermore, results for Jain's fairness and spectral efficiency reveal that the Minimum-Delay relaying scheme has even greater performance improvement under high traffic loads.

In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets. We use noisy, differentially-private gradients to minimize the fitness cost of the machine learning model using stochastic gradient descent. We quantify the quality of the trained model, using the fitness cost, as a function of privacy budget and size of the distributed datasets to capture the trade-off between privacy and utility in machine learning. This way, we can predict the outcome of collaboration among privacy-aware data owners prior to executing potentially computationally-expensive machine learning algorithms. Particularly, we show that the difference between the fitness of the trained machine learning model using differentially-private gradient queries and the fitness of the trained machine model in the absence of any privacy concerns is inversely proportional to the size of the training datasets squared and the privacy budget squared. We successfully validate the performance prediction with the actual performance of the proposed privacy-aware learning algorithms, applied to: financial datasets for determining interest rates of loans using regression; and detecting credit card frauds using support vector machines.

Electric vehicles (EVs) are an eco-friendly alternative to vehicles with internal combustion engines. Despite their environmental benefits, the massive electricity demand imposed by the anticipated proliferation of EVs could jeopardize the secure and economic operation of the power grid. Hence, proper strategies for charging coordination will be indispensable to the future power grid. Coordinated EV charging schemes can be implemented as centralized, decentralized, and hierarchical systems, with the last two, referred to as distributed charging control systems. This paper reviews the recent literature of distributed charging control schemes, where the computations are distributed across multiple EVs and/or aggregators. First, we categorize optimization problems for EV charging in terms of operational aspects and cost aspects. Then under each category, we provide a comprehensive discussion on algorithms for distributed EV charge scheduling, considering the perspectives of the grid operator, the aggregator, and the EV user. We also discuss how certain algorithms proposed in the literature cope with various uncertainties inherent to distributed EV charging control problems. Finally, we outline several research directions that require further attention.

The general non-stationarity of the wireless body area network (WBAN) narrowband radio channel makes long-term prediction very challenging. However, long short-term memory (LSTM) is a deep learning recurrent neural network (RNN) architecture that is proposed here to learn these atypical radio channel dynamics and make channel predictions. Thus, here we propose an LSTM-based RNN channel prediction framework providing long-term channel prediction up to 2s with low error. To address practical scenarios where information packets are transmitted continuously, we outline a timing scheme, which enables the LSTM predictor to operate online. We employ the proposed method in transmit power control for everyday on-body, measured, WBAN channels. When compared with existing approaches, the proposed channel prediction reduces circuit power consumption significantly while improving communications reliability.

For current and future wireless networks, applications such as Industrial Internet of Things (IoT) and vehicle-to-vehicle (V2V) communications are strictly delay-sensitive, requiring low latency. In order to minimize end-to-end delay of each device in such machine-to-machine (M2M) communications, we propose buffer-aided multi-hop relaying networks in the uplink, for high data rates at mmWave carrier frequencies. We formulate the problem as a multi-tier queueing system, and by leveraging stochastic geometry, we present a tractable analytical framework to investigate the signal-to-interference-plus-noise-ratio (SINR) distribution of devices at each tier, thereby computing the expected delay and delay outage probabilities for each device, which are key metrics to characterize overall network delay performance. Numerical results based on multiple cells in a dense urban scenario validate analytical results, and show that our minimum-delay relaying scheme achieves significant lower average end-to- end delay and higher effective capacity than direct association or a state-of-art max-SINR relaying scheme.

Millimeter wave (mmWave) communications provide a very promising solution to bandwidth scarcity. However, mmWave signals suffer from high attenuation and are very susceptible to blockage, which makes traditional cellular network architectures unsuitable. Multi-tier mmWave network architectures leverage relay access points (APs) to shorten transmission distances and alleviate blockage, but such architectures pose challenges to the scheduling between each tier. To address this, here we analyze the downlink scheduling problem for a multi-tier mmWave network, and we propose a cross-tier scheduling scheme with polynomial complexity, which ensures every user is served by at least one access point while optimizing sum throughput. Numerical analysis shows that the proposed scheduling has major gains in downlink rate fairness and service coverage, with only a small reduction in spectral efficiency compared to the state-of-art.

In underlaid device-to-device (D2D) cellular networks, severe radio interference can be typical for cellular and D2D users, which causes each D2D user's quality-of-experience (QoE) to degrade significantly. Thus, in this paper, a dynamic Stackelberg game is formulated with a single-leader (base station) and multiple-followers (D2D pairs). The leader reduces interference within the network by charging a price to followers, whereas followers react to this price and compete to find optimal transmit power and resource block allocation. To enhance D2D user QoE, D2D users are categorized into one of three application classes with each class mapped to a different utility function. To address this, we propose a crucial innovation where the several different utility functions, available to followers, are solved using a non-scalarized approach, as a scalarized approach to this multi-criteria optimization problem is generally infeasible in real-time D2D cellular communications. To solve the game, a distributed algorithm is proposed, and it is shown to converge to a sub-game perfect Stackelberg equilibrium across all users. Simulation results show that the proposed approach reduces transmit power effectively and increases throughput, giving a Pareto-efficient outcome, while guaranteeing best social welfare and satisfaction across all D2D users.

In this paper, the problem of self-organizing, correlation-aware clustering is studied for a dense network of machine-type devices (MTDs) deployed over a cellular network. In dense machine-to-machine networks, MTDs are typically located within close proximity and gather correlated data, and, thus, clustering MTDs based on data correlation leads to a decrease in the number of redundant bits transmitted to the base station. The clustering problem is formulated as an evolutionary game, which models the interactions among a massive number of MTDs, in order to decrease MTD transmission power. A novel utility function that captures the tradeoff between minimizing the average MTD transmission power per cluster and maximizing cluster size (or minimizing signaling overhead) is proposed. To solve this game, a distributed algorithm is proposed to allow a massive number of MTDs to autonomously form clusters. It is shown that the proposed distributed algorithm converges to an evolutionary stable strategy (ESS) that is robust to a small portion of MTDs deviating, e.g., due to some stochastic changes in the M2M environment from the stable cluster formation at convergence. The maximum fraction of MTDs that can deviate from the ESS, while still maintaining a stable cluster formation, is derived. Simulation results show the efficiency of the proposed algorithm in clustering MTDs with highly correlated data: on average, the proposed approach yields reductions of up to 44.1% and 15.25% in terms of the transmit power per cluster, compared to forming clusters with the maximum possible size and uniformly selecting a cluster size, respectively.

This paper proposes a novel splitting (SPLIT) algorithm to achieve fairness in the multiterminal lossless data compression problem. It finds the egalitarian solution in the Slepian-Wolf region and completes in strongly polynomial time. We show that the SPLIT algorithm adaptively updates the source coding rates to the optimal solution, while recursively splitting the terminal set, enabling parallel and distributed computation. The result of an experiment demonstrates a significant reduction in computation time by the parallel implementation when the number of terminals becomes large. The achieved egalitarian solution is also shown to be superior to the Shapley value in distributed networks, e.g., wireless sensor networks, in that it best balances the nodes' energy consumption and is far less computationally complex to obtain.

Array spatial thinning is employed to select the most effective antenna elements in a large phased array for optimum performance concerning hardware and computational costs, in conjunction with managing element failure and radio interference mitigation. We formulate spatial array thinning under connectivity constraints to make the thinning applicable in large arrays. By introducing graph optimization, the problem is recast as a k-clique version of a generalized minimum clique problem. Furthermore, by studying optimum clustering for the proposed formulation, we show by an example that the unconstrained thinning performance is achievable, even with connectivity constraints.

In order to accommodate a massive number of machine-to-machine (M2M) communication devices (MTCDs) in cellular networks, we propose an efficient association and multi-hop relaying scheme, with rate control, between users and access points. Overall transmission rate is maximized and power consumption is minimized, while ensuring load balancing and proportional fairness among users. In this context a distributed algorithm is proposed that solves this joint association and relaying problem, only requiring limited control information exchange between nearby users. With formulation as a game, it is shown that the proposed algorithm converges to a social optimum, with a subgame perfect equilibrium. Numerical simulation results show significant improvements in the network data rate, by a factor of at least 6, and fairness, up to a factor of 14, among the MTCDs by the proposed association method when compared to other potential schemes.

One of the significant challenges for managing machine-to-machine (M2M) communication in cellular networks, such as LTE-A, is the overload of the radio access network due to very many machine type communication devices (MTCDs) requesting access in burst traffic. This problem can be addressed well by applying an access class barring (ACB) mechanism to regulate the number of MTCDs simultaneously participating in random access (RA). In this regard, here we present a novel deep reinforcement learning algorithm, first for dynamically adjusting the ACB factor in a uniform priority network. The algorithm is then further enhanced to accommodate heterogeneous MTCDs with different quality of service (QoS) requirements. Simulation results show that the ACB factor controlled by the proposed algorithm coincides with the theoretical optimum in a uniform priority network, and achieves higher access probability, as well as lower delay, for each priority class when there are heterogeneous QoS requirements.

The existence of wide-sense-stationarity (WSS) in narrowband wireless body-to-body networks is investigated for "everyday" scenarios using many hours of contiguous experimental data. We employ different parametric and non-parametric hypothesis tests for evaluating mean and variance stationarity, along with distribution consistency, of several body-to-body channels found from different on-body sensor locations. We also estimate the variation of power spectrum to evaluate the time independence of the auto-covariance function. Our results show that, with 95% confidence, the assumption of WSS is met for at most 90% of the cases with window lengths of 5 seconds for the channels between the hubs of different BANs. Additionally, in the best-case scenario, the hub-to-hub channel remains reasonably stationary (with more than 80% probability of satisfying the null hypothesis) for longer window lengths of more than 10 seconds. The short time power spectral variation for body-to-body channels is also shown to be negligible. Moreover, we show that body-to-body channels can be considered wide-sense-stationary over significantly longer periods than on-body channels.

In this paper, we propose a novel adaptive carrier sense multiple access scheme with collision avoidance (CSMA/CA) to perform efficient and reliable data transfer with increased throughput across multiple coexisting wireless body area networks (BANs) in a tiered architecture. We investigate the proposed scheme using two distributed cross-layer optimized dynamic routing techniques, i.e., shortest path routing (SPR) and cooperative multi-path routing (CMR). The channel state information from the physical layer is passed on to the network layer using an adaptive cross-layer carrier sensing mechanism between the physical and MAC layer, which adjusts the carrier sense threshold (e.g., RSSI) periodically based on the slowly-varying channel condition. An open-access experimental dataset of 'everyday' mixed-activities is used for analyzing the cross-layer optimization. Our proposed optimization using adaptive carrier sensing performs better than static carrier sensing with CSMA/CA as it reduces the continuous back-off duration and latency as well as significantly increases the throughput (in successful packets/s) by more than 50%. Adaptive CSMA/CA also shows 20% and 6% improvement over a coordinated TDMA approach with higher duty cycle for throughput and spectral efficiency, respectively, and provides acceptable packet delivery ratio and outage probability with respect to SINR.

We consider the problem of how to attain fairness in the multiterminal data compression problem by a game-theoretic approach and present a decomposition method for obtaining the Shapley value, a fair source coding rate vector in the Slepian-Wolf achievable region. We model a discrete memoryless multiple random source (DMMS) by a coalitional game where the entropy function quantifies the cost incurred by the source coding rates in each coalition. In the typical case for which the game is decomposable, we show that the Shapley value can be obtained separately for each subgame. The complexity of this decomposition method is determined by the maximum size of subgames, which is strictly smaller than the total number of terminals in the DMMS and contributes to a considerable reduction in computational complexity. An experimental result demonstrates large complexity reduction when the number of terminals in the DMMS becomes large.

Global warming, and the resultant climate change, has become an urgent global issue. One potential partial solution to this problem and the focus of this paper is to design energy management mechanisms for buildings that encourage users’ acceptance of the technology for increased environmental sustainability. Particularly, this paper focuses on the design of an energy management technique for the air-conditioning (AC) systems in residential buildings that considers users’ views on how they use such technology. Note that while a large number of energy management mechanisms are available in the literature for ACs, most of these studies, however, do not consider how well the users may accept their use in the building. To address this issue, this paper first synthesizes somemotivational psychologyliterature to understand users’ attitudes toward adopting such management techniques for the ACs within the building. Then, the obtained insights from various motivational models are incorporated into the design of an energy management scheme that encourages consumers to accept the technology that reduces electricity consumption, the cost of electricity, peak power from the grid; and the generation of CO2in the residential building. Finally, some experimental results are provided to illustrate how the designed energy management mechanism validates the motivational psychology models in terms of providing various benefits to the users, and thus shows the potential of being accepted by them.

Internet enabled body area networks (BANs) will form a core part of future remote health monitoring and ambient assisted living technology. In BAN applications, due to the dynamic nature of human activity, the off-body BAN channel can be prone to deep fading caused by body shadowing and multipath fading. Using this knowledge, we present some novel practical adaptive power control protocols based on the channel deviation to simultaneously prolong the lifetime of wearable devices and reduce outage probability. The proposed schemes are both flexible and relatively simple to implement on hardware platforms with constrained resources making them inherently suitable for BAN applications. We present the key algorithm parameters used to dynamically respond to the channel variation. This allows the algorithms to achieve a better energy efficiency and signal reliability in everyday usage scenarios such as those in which a person undertakes many different activities (e.g., sitting, walking, standing, etc.). We also profile their performance against traditional, optimal, and other existing schemes for which it is demonstrated that not only does the outage probability reduce significantly, but the proposed algorithms also save up to average transmit power compared to the competing schemes.

In this paper, an electric vehicle (EV) charging competition, among EV aggregators that perform coordinated EV charging, is explored while taking into consideration potential non-ideal actions of the aggregators. In the coordinated EV charging strategy presented in this paper, each aggregator determines EV charging start time and charging energy profiles to minimize overall EV charging energy cost by including consideration of the actions of the neighboring aggregators. The competitive interactions of the aggregators are modeled by developing a two-stage non-cooperative game among the aggregators. The game is then studied under prospect theory to examine the impacts of non-ideal actions of the aggregators in selecting EV charging start times according to subjectively evaluating their opponents' actions. It is shown that the noncooperative interactions among the aggregators lead to a subgame perfect E-Nash equilibrium when the game is played with either ideal, or non-ideal, actions of the aggregators. A case study presented demonstrates that the benefits of the coordinated EV charging strategy, in terms of energy cost savings and peakto-average ratio reductions, are significantly resilient to non-ideal actions of the aggregators.

Wireless Body Area Networks (WBANs) are seen as the enabling technology for developing new generations of medical applications, such as remote health monitoring. As such it is expected that WBANs will predominantly transport mission-critical and delay sensitive data. A key strategy towards building a reliable WBAN is to ensure such networks are highly immune to interference. To achieve this, new and intelligent wireless spectrum allocation strategies are required not only to avoid interference, but also to make best-use of the limited available spectrum. This article presents a new spectrum allocation scheme referred to as Smart Channel Assignment (SCA), which maximizes the resource usage and transmission speed by deploying a partially-orthogonal channel assignment scheme between coexisting WBANs as well as offering a convenient tradeoff among spectral reuse efficiency, transmission rate, and outage. Detailed analytical studies verify that the proposed SCA strategy is robust to variations in channel conditions, increase in sensor node-density within each WBAN, and an increase in number of coexisting WBANs.

Device-to-device communication (D2D) will be an integral part of 5G wireless networks. Device-to-Device (D2D) communication provide the additional resources to the cellular users for spatially reusing licensed/unlicensed spectrum by establishing direct communication. Although, D2D communication is gaining significant attention towards offloading traffic in heterogeneous networks in licensed band, no attention has been given to offload traffic in an unlicensed band in a centralized manner. However, a major challenge of D2D communication is managing resources in an efficient manner in a heterogeneous network. This paper will direct a new approach to D2D Communication and will present a scalable MAC protocol for D2D communications based on Point Coordination Function (PCF) access mechanism. The importance of PCF access mechanism is that it operates in a centralized manner and highly suitable for the dense environment, hence, can create a centralized control in a distributive manner. In this article, we propose an innovative three tier 5G architecture for D2D communication, which will offload cellular traffic from the cellular network to the WLAN in a dense environment. Moreover, we will present a new centralized scalable MAC protocol for D2D communication between WLAN users, based on the IEEE 802.11 Point Coordination Function (PCF) access mechanism. Our simulation results show that the proposed MAC scheme can increase the capacity of the network and perform better relative to the legacy Distributed coordination Function (DCF) defined in IEEE 802.11.

In this paper, the problem of correlation-aware clustering is studied for a dense network of machine-type devices (MTDs) deployed over a cellular network. In such dense networks, MTDs sense an environment and transmit their data to the base station (BS) via a cellular uplink. However, since MTDs are typically closely located to each other they will gather correlated data, and, thus, large amounts of redundant bits can be transmitted to the BS. To address this problem, an evolutionary coalitional (EC) game is proposed to cluster MTDs into coalitions in a fully distributed and autonomous manner, based on the correlation of their data. The proposed EC game allows a reduction in the number of redundant bits being sent to the BS, while also reducing the energy used for transmission by each MTD. To solve the EC game, a distributed coalition formation algorithm is proposed and shown to reach an evolutionary stable coalition structure, which is robust to a small portion of MTDs changing their strategy at the stable outcome. For this game, the maximum portion of MTDs that can deviate from the stable coalitional structure is derived. Simulation results show that the proposed approach can effectively cluster MTDs with highly correlated data which, in turn, enables those MTDs to eliminate a large number of redundant bits. Moreover, the results show that, for a given maximum correlation factor and network density, the transmission energy per MTD can be decreased by 19%, compared to a baseline merge-and-split algorithm. In addition, when a maximum correlation factor is considered, the number of redundant bits that can be eliminated per coalition is increased by 50%, compared to the merge-and-split algorithm.

The increase in the number of degrees of freedoms (DoF) that is afforded by multiple-input-multiple-output (MIMO) phased arrays is accompanied by an increase in hardware and computational costs. We mitigate this problem in a collocated MIMO phased array system by employing a selection strategy where a subset of K transmitter-receiver (Tx-Rx) pairs is chosen from the availableN pairs. We formulate the selection task as an optimization problem using the spatial correlation coefficient (SCC). Minimizing the SCC leads to an increase in the orthogonality of the signal and interference subspaces. We formulate and solve both the joint Tx-Rx selection problem and factored selection where the Tx and Rx are decoupled and treated separately. We show that both approaches can achieve excellent trade-off between performance and cost. While the factored problem compromises performance with respect to the joint Tx-Rx selection, it allows for better transmit power efficiency, thus increasing the received signal-to-noise ratio.

Device-to-Device (D2D) communications is a key aspect of future 5G cellular networks, as it can help improve spectral efficiency, system capacity, resource utilization, and quality-of-service (QoS), for the overall network. In this paper, a dynamic cross-layer leave-and-join based coalition formation game with non-transferable utility (NTU) is proposed. Our proposed game jointly optimizes mode selection (at the network layer), and resource allocation from the cell and cellular user/s, including power control (at the physical layer), in a distributed wireless network with D2D communications. Moreover, we aim to maximize channel rate and minimize transmit power for both cellular users and D2D pairs, while selecting optimal transmission mode for all D2D pairs. The coalition partition of the proposed game converges to a Nash stable outcome, which is socially efficient. The simulation results illustrate that once a Nash stable coalition partition is achieved, then optimal transmit power and channel rate are achieved for all D2D pairs and cellular users.

In this paper, we study the performance of two cross-layer optimized dynamic routing techniques for radio interference mitigation across multiple coexisting wireless body area networks (BANs), based on real-life measurements. At the network layer, the best route is selected according to channel state information from the physical layer, associated with low duty cycle TDMA at the MAC layer. The routing techniques (i.e., shortest path routing (SPR), and novel cooperative multi-path routing (CMR) incorporating 3-branch selection combining) perform real-time and reliable data transfer across BANs operating near the 2.4 GHz ISM band. An open-access experimental dataset of `everyday' mixed-activities is used for analyzing the proposed cross-layer optimization. We show that CMR gains up to 14 dB improvement with 8.3% TDMA duty cycle, and even 10 dB improvement with 0.2% TDMA duty cycle over SPR, at 10% outage probability at a realistic signal-to-interference-plus-noise ratio (SINR). Acceptable packet delivery ratios (PDR) and spectral efficiencies are obtained from SPR and CMR with reasonably sensitive receivers across a range of TDMA low duty cycles, with up to 9 dB improvement of CMR over SPR at 90% PDR. The distribution fits for received SINR through routing are also derived and validated with theoretical analysis.

Transmit power control is vital to wireless body area networks (BANs), where due to their prevalence many BANs may be required to coexist reliably, with a requirement for reduced power consumption to significantly increase sensor battery lifetime. In this paper, we propose a socially optimal finite repeated non-cooperative transmit power control game, in order to mitigate radio interference amongst coexisting BANs, improve throughput and reduce power consumption. The game is shown to have a unique Nash equilibrium. We also prove that the outcome of the game is socially efficient, given reasonable constraints, across all players at the unique Nash equilibrium. Using a realistic channel model, the game is shown to be very energy-efficient, significantly reducing power consumption and improving packet delivery ratio (PDR) with respect to other potential schemes, consuming 67% less circuit power than transmitting constantly at 0 dBm.

This paper presents a node-level self-organizing interference avoidance scheme (SIAC) between multiple coexisting wireless body area networks (WBANs) that incorporates self-organization and smart spectrum allocation. It follows a biologically inspired approach based on the theory of pulse-coupled oscillators for self-organization. The proposed scheme makes three major contributions as compared to the current literature. Firstly, it considers node-level interference for internetwork interference mitigation rather than considering each WBAN as a whole. Secondly, it allocates synchronous and parallel transmission intervals for interference avoidance in an optimal manner and dynamically adapts to changes in their coexistence. Finally, it achieves collision-free, self-organized communication with only information of the firing signal of each WBAN and does not require a global coordinator to manage its communications. It operates on a nodes traffic priority, signal strength, and density of sensors in a WBAN. Simulation results show that our proposal achieves a fast convergence time despite the little information it receives. Moreover, SIAC is shown to be robust to variations in signal strength, number of coexisting WBANs and number of sensor nodes within each WBAN.

Due to the rapid increase in data traffic, one of the solutions provided by mobile operators is to operate Long Term Evolution (LTE) in the unlicensed 5GHz band, as the licensed spectrum is becoming scarce. Mobile operators can expand their network capacity by operating LTE in the unlicensed band at lower cost when compared with using other licensed bands. Device to Device (D2D) communication, proven to be another effective way to enhance the capacity of a network, enables direct data exchange of localized traffic of users in proximity. Applying D2D communication to LTE unlicensed 5GHz band will further improve the network performance and user experience. In this article, we will discuss the new type of solutions that have been proposed for LTE operating in an unlicensed 5GHz band that includes; LTE-Unlicensed (LTE-U), LTE-License Assisted Access (LTE-LAA), LTE WiFi Link Aggregation (LWA), and MuLTEfire. We will discuss the important features along with their advantages and disadvantages and compare these technologies as well. We simulate LTE-LAA, LWA and MuLTEfire technologies in the presence of Wi-Fi hotspot and compare their results. Furthermore, we apply D2D communication to these technologies and from the results we conclude that MuLTEfire can increase the throughput drastically but network saturates quickly. Whereas, applying D2D communication with LWA is beneficial for a scalable network as it will not only increase the network throughput but will increase the network capacity as well.

Device-to-Device (D2D) communications underlaying cellular communications can increase resource utilization, system capacity, and interference, while increasing quality of service (QoS) for cellular and D2D users. In this paper, we design a non-cooperative cross-layer repeated game, which jointly optimizes mode selection (i.e. either operating in cellular mode or reuse mode) at the network layer and power control at the physical (PHY) layer for a distributed wireless network. The proposed game aims to minimize transmit power, and select optimal transmission mode, while achieving acceptable packet delivery ratio (PDR). The effects on PDR with respect to communication reliability and optimum mode switching point over time are determined. The game is constituted of a non-cooperative power control game and a two-armed bandit game, and by combining these two games we obtain rapid convergence to a Pareto-optimal outcome for both mode selection and transmit power.

Internet enabled body area networks (BANs) will form a core part of future Ambient Assisted Living (AAL) technology. In BAN based AAL applications, due to the dynamic nature of human behavior, the off-body BAN channel can be prone to deep fading phenomenon caused by body shadowing and multipath fading. This emphasizes the necessity of a power control mechanism, especially in AAL applications where battery replacement is difficult or undesirable. To that end, we present a novel practical adaptive power control protocol based on the knowledge of the channel deviation to simultaneously prolong the lifetime of wearable devices and reduce outage probability. The proposed scheme is both flexible and relatively simple to implement on hardware platforms with constrained resources making it inherently suitable for AAL applications. We also profile its performance against traditional, optimal and other existing power control schemes. It is demonstrated that not only does the outage probability reduce significantly, but the proposed algorithm also saves between 20-60% average energy consumption compared to the competing schemes.

This paper proposes a novel electric vehicle (EV) classification scheme for a photovoltaic (PV)-powered EV charging station (CS) that reduces the effect of intermittency of electricity supply and the cost of energy trading of the CS. Since not all EV drivers would like to be environmentally friendly, all vehicles in the CS are divided into three categories: 1) premium; 2) conservative; and 3) green, according to their charging behavior. Premium and conservative EVs are considered interested only in charging their batteries, with noticeably higher rates of charging for premium EVs. Green vehicles are more environmentally friendly and thus assist the CS to reduce its cost of energy trading by allowing the CS to use their batteries as distributed storage. A different charging scheme is proposed for each type of EV, which is adopted by the CS to encourage more EVs to be green. A basic mixed-integer programming (MIP) technique is used to facilitate the proposed classification scheme. It is shown that the uncertainty in PV generation can be effectively compensated, along with minimization of total cost of energy trading to the CS, by consolidating more green EVs. Real solar and pricing data are used for performance analysis of the system. It is demonstrated that the total cost to the CS reduces considerably as the percentage of green vehicles increases and that the contributions of green EVs in winter are greater than those in summer.

The pervasive use of wireless body area networks (BANs) has incurred potential inter-BAN interference, which can cause severe performance degradation. In this article, the coexistence of BANs is experimentally performed. A relay-assisted cooperative communications scheme is implemented in a real IEEE 802.15.4-based BAN system with a beacon-enabled mode and guaranteed time slot (GTS) scheduling. As far as we know, it is the first experimental work that enables real-time investigation of the effectiveness of cooperative communications in BANs for co-channel radio interference mitigation. First- and second-order statistics, including outage probability, level crossing rate (LCR), and average fade/nonfade duration, are calculated from the measured effective channel gains of the device-to-coordinator links across all superframes. Empirical results demonstrate significant advantages of using two-hop relay-assisted communications over traditional star topology BAN. Advantages include a maximum of a 10dB increase in channel gain threshold at an outage probability of 10%, which corresponds to a guideline for a 10% maximum packet error rate as specified in the IEEE BAN standard; a reduction in the level crossing rate by a factor of 5 at a channel gain threshold of − 100dB; and an average nonfade duration prolonged by a factor of 5 at the same threshold.

In this article, we enable the coexistence of multiple wireless body area networks (BANs) using a finite repeated non-cooperative game for transmit power control. With no coordination amongst these personal sensor networks, the proposed game maximizes each network’s packet delivery ratio (PDR) at low transmit power. In this context, we provide a novel utility function, which gives reduced benefit to players with higher transmission power, and a subsequent reduction in radio interference to other coexisting BANs. Considering the purpose of inter-BAN interference mitigation, PDR is expressed as a compressed exponential function of inverse signal-to-interference-and-noise ratio, so it is essentially a function of transmit powers of all coexisting BANs. It is shown that a unique Nash Equilibrium (NE) exists, and hence there is a subgame-perfect equilibrium, considering best response at each stage independent of history. In addition, the NE is conjectured to be the socially optimal solution according to all possible action profiles. Realistic and extensive on- and inter-body channel models are employed. Results confirm the effectiveness of the proposed scheme in better interference management, greater reliability, and reduced transmit power when compared with other schemes that can be applied in BANs.

In this paper, we propose a prediction algorithm for dynamic channel allocation amongst coexisting Wireless body area networks (WBANs). Variations in channel assignment due to mobility scenarios within each WBAN as well as the movement of WBANs towards each other is investigated. The proposed scheme is further optimized to allocate the optimum transmission time with synchronous and parallel transmissions such that interference is fully avoided. This reduces the number of interfering nodes and leads to better usage of the scarce limitation of resources in these networks, larger network lifetime, higher energy savings and higher throughput. In fact, the aim of this protocol is to mitigate interference along with maintaining minimum power consumption in order to maximize network lifetime and increase the spatial reuse and throughput of each WBAN. Simulation results show that our approach achieves a much higher spatial reuse using the smart spectrum allocation scheme for interference mitigation in collocated WBANs. We conduct extensive simulations for coexistence prediction in different mobility scenarios using the NS-2 simulator. Consequently, we demonstrate the efficiency of the proposed protocol in providing interference-free channel assignments and higher energy savings.

Opportunistic communication coupled with a sensing task enables the collection and spreading of sensory information in areas without global connectivity, providing useful information in challenging environments. In this paper, we consider an opportunistic sensor network where the mobility of users enables both the measurement and spreading of sensor data. We motivate user participation through a game theoretic approach, which is designed to ensure a fair and efficient exchange of sensor messages. The message exchange is modeled as a two-player game where sensor measurements are exchanged between nodes in a contrite tit-for-tat manner. The proposed game captures the nodes desire to limit energy consumption while at the same time obtaining messages containing useful information. We show that the best response in the game is a Pareto optimal subgame perfect equilibrium. The game is evaluated through simulation in a realistic scenario and compared with three other approaches, generating the best overall efficiency by striking a balance between size and content of messages.

This paper presents a novel primitive for self-organization amongst multiple coexisting Wireless Body Area Networks (WBANs). It follows a biologically inspired approach based on the theory of pulse-coupled oscillators. Our proposal allows for coexisting WBANs to use delayed information from previous transmissions to adjust to a collision-free TDMA schedule amongst each other for future communications. Most importantly, it does not require a global coordinator as all nodes achieve synchronization in a completely self-organized manner. Simulation results show that our protocol achieves a significantly fast convergence time despite little information from its coexisting networks. Moreover, the proposed approach is shown to be robust to variations in channel conditions, density of sensor nodes within each network and the number of coexisting WBANs. We conduct extensive simulations to evaluate the efficiency of the proposed protocol using the NS-2 simulator.

This paper proposes a novel adaptive internetwork interference mitigation scheme, namely AIM, for environments with multiple coexisting Wireless Body Area Networks (WBANs). The proposed scheme, operating on a nodes' traffic priority, packet length, signal strength and density of sensors in a WBAN, makes three major contributions as compared to the current literature. Firstly, it considers node-level interference for internetwork interference mitigation rather than considering each WBAN as a whole. Secondly, it allocates synchronous and parallel transmission intervals for interference avoidance in an optimal manner. Finally, it significantly reduces the number of orthogonal channels assigned to achieve a higher throughput as well as better usage of the scarce limitation of resources in WBANs. Simulation results show that our protocol achieves a significantly higher spatial reuse compared to existing approaches for interference mitigation in WBANs.

This paper investigates the feasibility of using a discriminate pricing scheme to offset the inconvenience that is experienced by an energy user (EU) in trading its energy with an energy controller in smart grid. The main objective is to encourage EUs with small distributed energy resources (DERs), or with high sensitivity to their inconvenience, to take part in the energy trading via providing incentive to them with relatively higher payment at the same time as reducing the total cost to the energy controller. The proposed scheme is modeled through a two-stage Stackelberg game that describes the energy trading between a shared facility authority (SFA) and EUs in a smart community. A suitable cost function is proposed for the SFA to leverage the generation of discriminate pricing according to the inconvenience experienced by each EU. It is shown that the game has a unique sub-game perfect equilibrium (SPE), under the certain condition at which the SFA's total cost is minimized, and that each EU receives its best utility according to its associated inconvenience for the given price. A backward induction technique is used to derive a closed form expression for the price function at SPE, and thus the dependency of price on an EU's different decision parameters is explained for the studied system. Numerical examples are provided to show the beneficial properties of the proposed scheme.

A scheme for two-hop relay-assisted cooperative communications integrated with transmit power control, based on simple channel prediction, is presented. A large set of empirical on- and inter-body channel data is employed to model various scenarios of wireless body area network (WBAN) communications, from one isolated WBAN up to 10 closely located WBANs coexisting. Our study shows that relay assisted power control can reduce approximately 60% circuit power consumption from that of constant transmission at 0 dBm, without much loss in reliability. Further, interference mitigation is significantly enhanced over constant transmission at -5 dBm, with similar power consumption. Such performance is maintained from 2 to 10 closely-located WBANs coexisting. And the joint algorithm works best for one isolated WBAN. A trade-off between power saving and interference mitigation is motivated, taking remaining sensor-battery level, amount of interference and on-body channel quality into account.

In this paper, a dynamic resource allocation scheme is proposed to avoid interference amongst coexisting Wireless Body Area Networks (WBAN). In the proposed scheme, each WBAN generates a table consisting of interfering nodes from coexisting WBANs in its vicinity. Then each WBAN broadcasts this table to its neighbors, which allows for efficient interpretation of an Interference Region (IR) between each pair of WBANs. The nodes in the IR are later allocated orthogonal sub-channels; whilst nodes that do not exist in the IR can potentially transmit in the same time interval. We further demonstrate a precise tradeoff between the minimum interference level and spatial reuse. Simulation results show that our proposed scheme has far better spectral efficiency compared to the conventional orthogonal schemes, whilst maintaining an acceptable interference level. We also provide mathematical analysis on the proposed scheme to validate its efficiency for increasing spectral efficiency and avoiding interference. To further reduce the interference level, we propose a probabilistic approach, and analytically show that the outage probability can be effectively reduced at the cost of very small change in the spatial reuse factor.

An improved form for Switched Combining (SwC) with on-body cooperative communications is presented, particularly suited to the `everyday' wireless body area network (BAN) wireless channel. Two and three-branch cooperative coherent SwC is analyzed theoretically and empirically and compared with cooperative coherent selection combining (SC), and a previous switched combining implementation. New expressions for outage probability and approximate switching rate are derived for the proposed SwC. Three locations for relays/hubs are considered, along with seven sensor locations, all using extensive on-body “open-access” channel measurements. The outage performance of SwC is not considerably degraded with respect to SC with large improvement over single-link communications, and significant improvement over the previous SwC. The benefit of the proposed SwC is shown as the two and three-branch switching rate for this SwC, is typically between 10% and 15% of that of SC. Further, compared to the previous switched combining method, there is only a small increase in switching rate at the same time as improved outage performance. The low switching rate here may allow for easier implementation in practical IEEE 802.15.6 BAN.

In this paper, a hybrid scheme incorporating graph coloring and cooperative scheduling schemes is proposed for interference mitigation amongst coexisting wireless body area networks (WBANs). The proposed approach pairs every two WBANs into a cluster and uses cooperative scheduling amongst the pairs in each cluster to minimize interference. A color based approach is used to allocate different colors to coexisting WBANs in a manner such that none of the two interfering WBANs have the same color. Simulation results show that our proposed scheme has far better spatial reuse when compared to the fully color-based scheme. We also provided theoretical analysis of our proposed scheme to validate its efficiency in avoiding interference and increasing spatial reuse.

Using narrowband wireless On-Body Area Network (BAN) channel measurements (50 million data points) in diverse environments with multiple subjects, we examine the stationarity of the channel. Wide-Sense Stationarity (WSS) tests and power spectral estimates show that the channel has a 50% probability of stationarity at 500 ms and the probability rapidly diminishes thereafter. We show that non-stationarity is inherent to on-body BANs and not an artifact of experimental setup.

In this paper, a cooperative two-hop communication scheme, together with opportunistic relaying (OR), is applied within a mobile wireless body area network (WBAN). Its effectiveness in interference mitigation is investigated in a scenario where there are multiple closely-located networks. Due to a typical WBAN's nature, no coordination is used among different WBANs. A suitable time-division-multiple-access (TDMA) scheme is adopted as both an intra-network and also an internetwork access scheme. Extensive on-body and off-body channel gain measurements are employed to gauge performance, which are overlaid to simulate a realistic WBAN working environment. It is found that opportunistic relaying is able to improve the signal-to-interference-plus-noise ratio (SINR) performance at an outage probability of 10% by an average of 5 dB, and it is also shown that it can reduce level crossing rate (LCR) significantly at low SINRs. Furthermore, this scheme is more efficient when on-body channels fade more rapidly.

This paper explores an idea of demand-supply balance for smart grids in which consumers are expected to play a significant role. The main objective is to motivate the consumer, by maximizing their benefit both as a seller and a buyer, to trade their surplus energy with the grid so as to balance the demand at the peak hour. To that end, a Stackelberg game is proposed to capture the interactions between the grid and consumers, and it is shown analytically that optimal energy trading parameters that maximize customers' utilities are obtained at the solution of the game. A novel distributed algorithm is proposed to reach the optimal solution of the game, and numerical examples are used to assess the properties and effectiveness of the proposed approach.

In this paper, we propose a practical network coding approach for wireless body are networks (WBANs) using decode-and-forward relays. In this scheme, namely decode and forward-network coding (DF-NC), each relay linearly combines different messages from different sources to generate one message, and then transmits that message to the destination. Each relay node in DF-NC requires only one transmission time slot to forward its message. Thus, in this approach, energy usage at each relay is minimized compared to existing cooperative schemes without network coding, which require Nstime slots per relay for relay transmissions; where Nsis the number of source nodes. Simulation results show that the proposed DF-NC scheme can achieve near optimal outage probability while minimizing the number of transmissions per node, maximizing the energy efficiency of WBANs, and minimizing the delay.

In this paper an efficient energy curtailment scheme is studied, which enables the power users of a smart grid network to decide on the reduction in energy supplied to them in the event of a power outage in the system. Considering the advantages of a two-way communications infrastructure for any future smart grid, a non-cooperative generalized Nash game is proposed where the players are users of power in the network. They adopt a strategy to choose the amount of reduction in energy supplied to them based on their energy requirements so as to minimize the total cost incurred to the system due to the power outage (i.e., social optimality). The game is modeled as a variational inequality problem, and it is shown that the socially optimum solution is obtained at the variational equilibrium of the energy curtailment game. An algorithm that enables the users to efficiently reach this equilibrium is proposed. Simulation results show that the proposed game yields an improvement of about 15% on average, in terms of average total cost reduction, compared to a standard equal power curtailment scheme.

This paper presents conditional probabilistic modeling suitable to characterize the temporal variation of links in wireless body area networks (BAN); according to short, medium and long term fading characteristics. The approach captures first and second order statistics appropriately; and using conditional probabilities, predicts what signal power levels can be expected from 10ms ahead to many seconds into the future in the context of typical channel coherence times. Hundreds of hours of link measurements, in "Everyday" mixed activity BAN, are used to generate these conditional models. We show that such modeling has an important effect to higher level simulation results (e.g., packets received at the application layer) for different simulation scenarios. Moreover we show that the model is computationally efficient as its introduction adds only 8% of simulation time on average. It is also shown that short and medium-term conditional modeling can vary considerably from long-term modeling, particularly given lower instantaneous path loss.

In this paper, coexistence of multiple mobile wireless body area networks (WBANs), where there is no coordination between WBANs, is investigated for the case where the WBAN-of-interest employs cooperative communications. A decode-and-forward protocol with two dual-hop links, two relays and selection combining (SC) at the hub (or gateway device) is chosen for the WBAN-of-interest. A suitable time-division-multiple-access (TDMA) scheme is used, enabling intra-network and inter-network operation, to allocate slots for each Tx/Rx link packet transmission. Realistic channel models are employed with various amounts of shadowing, small-scale fading and white noise introduced between WBANs. For the WBAN-of-interest, many hours of measured channel gain data is employed to emulate the channel for this WBAN. It is found that the chosen cooperative communications provides significantly better co-channel interference mitigation than single-link star topology WBAN communications in a mobile, dynamic, scenario, hence the signal-to-interference-plus-noise ratio (SINR) for 10% outage probability at the hub is greatly improved by up-to 12 dB. It is also demonstrated that the location of the hub, given three typical locations, has significant impact on the performance of the cooperative WBAN communications.

Characterization of cooperative switched combining (SwC), for the `everyday' wireless body area network (BAN) wireless channel, is presented. A decode-and-forward protocol is chosen for the on-body cooperative BAN. The outage probability of two and three-branch cooperative coherent SwC, where the three-branch cooperative SwC is implemented as a variant of switch-and-examine combining, is compared with that for cooperative coherent selection combining (SC). Three typical locations for relays/hubs are considered, along with seven typical sensor locations, all on-body. The outage performance of SwC is not considerably degraded when compared to SC, and there is still significant improvement over direct single-link communications. However the benefit of SwC with respect to SC is demonstrated in terms of the switching rate between branches, which for both two and three-branch SwC is considerably smaller, between 12% and 32% of that of SC for any hub and relay/s locations. This may allow for easier implementation in practical IEEE 802.15.6 BAN.

Channel prediction, which predicts a future channel gain based on current and past observations, is very useful for power control and resource optimization in wireless communication systems. However, a low-complexity predictor with trustworthy prediction accuracy is yet to be developed. This paper proposes a linear predictor and two linear Markov predictors, which achieve a good balance between complexity and accuracy.

We present a predictor for real Body-Area-Network (BAN) channels that is accurate for up to 2 seconds, even with a nominal channel coherence time of 500 ms. The predictor utilizes the partial-periodicity of measured BAN channels using the previous 4 seconds of channel gain values. We demonstrate use of this predictor for power control with open-access and private channel measurements. When used under a realistic setting for IEEE 802.15.6, with packet loss less than 10%, we show that the accurate channel predictor does not translate into substantial reduction in packet loss or power usage over a simple sample-and-hold method, even though it is a more accurate predictor than sample-and-hold.

Characterization of cooperative communications, for the `everyday' body-area-network (BAN) wireless channel is presented. Performance analysis is given based on many hours of empirical data measured on five adult subjects. A decode-and-forward protocol is chosen for the cooperative BAN. Three typical locations for on-body relays/hubs are considered, along with seven typical on-body sensor locations. Large improvements over single-link communications, in terms of outage probability and average fade duration, are demonstrated with the use of one and two on-body relays. Coherent selection combining (SC) and coherent maximum-ratio combining (MRC) are used in the analysis, with MRC only providing a marginal improvement over SC. The advantages of careful relay placement are also demonstrated.

Transmission of signals using multiple antennas can significantly improve the energy efficiency of a wireless network, and the proper alignment of the transmitted signals' phases at the receiver is one of the key factors so this efficiency improvement can be realized. In a time-varying channel, due to the relative motion between the transmitters and the receiver, the development of a scheme that guarantees such alignment is very challenging. In this paper, considering a distributed transmit beamforming scenario, an algorithm to achieve such phase alignment of signals in a time-varying channel is proposed. A simple formula is derived, which can be adopted by each transmitter to compute its beamforming weight's phase, and it is shown that the use of this perturbation results in a significant improvement in terms of speed of convergence at the receiver. With simulation it is shown that, using the proposed scheme, the transmitted signals' phase aligned at the receiver 33% faster than the one-bit feedback scheme. The average theoretical bit error rate is achieved 50% faster relative to the one-bit feedback scheme with on average 18% less number of antennas at the transmitter.

We investigate the impact of wireless channel temporal variations on the design of medium access control (MAC) protocols for body area networks (BANs). Our measurements-based channel model captures large and small time-scale signal correlations, giving an accurate picture of the signal variation, specifically, the deep fades which are the features that mostly affect the behavior of the MAC. We test the effect of the channel model on the performance of the 802.15.4 MAC both in contention access mode and TDMA access mode. We show that there are considerable differences in the performance of the MAC compared to simulations that do not model channel temporal variation. Furthermore, explaining the behavior of the MAC under a temporal varying channel, we can suggest specific design choices for the emerging BAN MAC standard.

Methods for transmit power control based on simple long-term channel prediction for the general body-area communications channel are presented. The power control methods are based on large sets of empirical every-day activity data. Numerous transmit-receive pair (Tx-Rx) locations on the human body, i.e. on-body, for a typical body-area-network (BAN) are considered. With the use of a simple prediction method based on held samples, and an enhanced held simple prediction method that uses short term mean path loss with the held sample, optimal power allocation for long-term transmit power control is described. When tested, according to the draft IEEE 802.15.6 BAN radio standard, on empirical data, both power allocation methods are shown to be more reliable, and also more energy efficient in terms of transmit circuit power consumption, than systems that use typical set Tx power levels for BAN.

A dynamic narrowband on-body area communications scenario is characterized with respect to link margin as a difference between system operating point, in terms of receive power, and receiver sensitivity. The characterization is based on an extensive measurement campaign near the 900 MHz ISM bands, with a number of different human subjects moving at a range of speeds in an indoor-office scenario. Key implications for operating reliability in terms of outages, meeting latency requirements, infeasibility of interleaving and limits upon packet duration are drawn from this link margin analysis pertinent to body-area-communications system design. The need for receive hardware with good receiver sensitivity is highlighted. Further to this context important second-order statistics are given, such as fade duration, as well as a novel measure: average fade magnitude. Distributions are given to accurately characterize these second order statistics. Along with link margin analysis, this modeling can be used to verify possible implementations, and help in system design.

Coexistence of multiple wireless body area networks (WBAN) is a very challenging problem because each piconet can have a large number of sensors and their movement is unpredictable. Moreover, suitable global coordination schemes do not exist as there is no natural choice of coordinator between piconets. Adaptive schemes that work well with low-occupancy channels, such as listen before transmit, are not a wise global solution because of the potential for high levels of traffic in any one area. In this paper we investigate the performance of three classic multiple-access schemes - namely TDMA, FDMA and CDMA - for (inter-network) piconet coexistence. We first consider a theoretical analysis of these schemes and then simulate each scheme using real-world interference measurements. It is found that co-channel interference could significantly degrade system performance if left unchecked, and that TDMA and FDMA are better choices than CDMA in terms of co-channel interference mitigation.

A characterization of the dynamic narrowband on- body to off-body area channel is presented based on realtime measurements of the time domain channel response at carrier frequencies near the 900 MHz and 2400 MHz Industrial, Scientific and Medical (ISM) bands. A statistical characterization is presented of received signal amplitude when the subject's body is standing and walking, transmitted from the body to a receiver, Rx, off the body, with various orientations of the subject's body with respect to the receiver, and various distances from the receiver. Two locations of the transmitter, Tx, on the human body are considered. The Lognormal distribution provides a good fitting model with and without movement. Further, the stability is characterized based on a measure of channel response variance, which is called here the channel variation factor and can characterize the channel coherence time. The on-body to off-body area channel is determined to be generally stable over a period of 25 ms, but the amount of stability is found to be dependent both on movement, Tx location on-body, and carrier frequency.

This paper proposes an efficient rate one space-frequency block code (SFBC) for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. The proposed SFBC incorporates concept of matched rotation precoding (MRP) to achieve full transmit diversity and optimal system performance for arbitrary number of transmit antennas, subcarrier interval and subcarrier grouping. The MRP exploits the inherent rotation property of SFBC and has relaxed restrictions on subcarrier interval and subcarrier grouping, making it ideal for adaptive/time varying systems. The lowerbound of the coding gain for MRP is derived and shown that it is useful when designing a SFBC for practical scenarios, e.g. when transmitters have only partial knowledge of power delay profile or when the power delay profile has only a few dominant delayed paths. Simulation results show that the MRP can achieve a similar or better performance than existing SFBCs.

A super-orthogonal co-ordinate interleaved orthogonal design (CIOD) space-time block code is introduced for three and four transmit antennas. Using unitary matrix transformations the constituent CIOD is expanded, with a performance improvement in terms of Eb/N0and an increased code rate in MIMO channels, and only a moderate increase in decoding complexity. Good performance is shown in comparison to other codes of comparable decoding complexity.

This paper presents a multirate space-time- frequency linear block coding scheme (STFBC) with full transmit diversity for a variety of transmission rates. The proposed multirate STFBC can achieve relatively smooth balance between the performance and the transmission rate for a given constellation size. Design of a space-time linear block coding (STBC) scheme is presented as a special case of the proposed multirate STFBC, which perform better than some of the existing STBCs. Moreover, optimized multirate STFBCs have also been compared with some of the existing STFBCs. Simulations results show that the design parameter has sufficient flexibility to achieve improved performance with reduced computational complexity.

A new differential unitary space-time code is presented that is a product of group diagonal matrices of different orders and a unitary Vandermonde matrix. Design is simplified through properties of the union bound on block error probability for the presented code. Detection is simplified via properties of the diagonal matrices which form a cyclic group. Superior performance to best known differential space-time codes is shown, particularly for 3, 5 and 6 transmit antennas.

This paper addresses the improved performance of linear orthogonal space-time block codes (OSTBC) for three and four antennas at two specific data rates. Considerable performance improvement is achieved through the application of different size constellations for the linear OSTBC to obtain data rates of 2 bits/s/Hz and 4 bits/s/Hz with the sporadic OSTBC designs of code rate 3/4 rather than the code rate 1/2 designs typically utilised to obtain these spectral efficiencies.

This paper addresses the design of high rate quasi-orthogonal space-time block codes, with rates of 1.5 and 13/8, to give improved performance. Three different quasi-orthogonal designs are presented which are variants of previous quasi-orthogonal designs to give higher code rates. The first two variants transform rate 1 designs to rate 1.5 designs, the third variant transforms a rate 9/8 design to a rate 13/8 design. The designs presented are shown to be particularly attractive in terms of greater effective throughput for medium-to-high signal-to-noise ratio with a 4-ary input constellation.

This paper addresses optimization of differential unitary space-time modulation (DUSTM) for more than two transmit antennas. We demonstrate that the union bound on the block error probability of DUSTM codes can be considerably simplified for various cases of non-group DUSTM constellations. We also demonstrate that rotation matrices applied to diagonal DUSTM codes can be considered as special cases of Alamouti style block codes. Utilizing these two results, and some simplified design criteria, various DUSTM codes are derived for more than two transmit antennas which give superior performance to previously proposed codes over a wide range of SNR.

This paper presents a novel unitary space-time signal design for two to six transmit antennas which is applicable to both coherent and differential space-time coding. In the context of differential modulation, this new space-time signal design demonstrates good bit-error performance when compared with previously designed codes both theoretically and by simulation. The design for two antennas is shown to subsume previous differential unitary space-time signal constellation with the property that the number of optimum codes scales with the size of the constellation. As part of analysis of this novel design an exact expression for the union bound on error probability is given for differential unitary space-time modulation. The calculation of the union bound on bit-error probability can also shown to be considerably simplified for these codes allowing for the simplified design of larger constellations.

For the purposes of macroscopic system design a space-time Rayleigh fading multiple-input multiple-output (MIMO) radio channel is modelled for an arbitrary transmit antenna configuration of three to six transmit antennas. A traditional ring of scatterers model is use to generate the space-time cross correlations and thence flat fading channel distortions. A general trend is demonstrated for a specific non-coherent modulation scheme where the optimality of different transmission frame lengths can be related to the well known autocorrelation functions; based on this trend two frame lengths are chosen for MIMO channel analysis. Several beneficial arbitrary transmit antenna configurations are obtained from this analysis using some typical arbitrary configurations, and some initial optimisation using a genetic algorithm, from which further possible optimisation is proposed.

A new performance evaluation of switched antenna diversity, for the switch-and-examine strategy, is described. Expressions are derived for three-branch switched diversity with unequal branch statistics for independent Rayleigh and Rician fading channels. Comparisons are made using two and three-branch diversity with equal and unequal branch statistics. New expressions are also derived for an N-branch diversity system with equal branch statistics. The results of an analysis of various higher order diversity systems with equal statistics are compared to each other and to a no-diversity system. It is clearly demonstrated that, as the order of the diversity system increases, performance improves below and just above the switching threshold.