Compression techniques are essential in distributed optimization and learning algorithms with high-dimensional model parameters, particularly in scenarios with tight communication constraints such as limited bandwidth. This article presents a communication-efficient second-order distributed optimization algorithm, termed as CoNet-GIANT, equipped with a compression module, designed to minimize the average of local strongly convex functions. CoNet-GIANT incorporates two consensus-based averaging steps at each node: gradient tracking and approximate Newton-type iterations, inspired by the recently proposed Network-GIANT. Under certain sufficient conditions on the step size, CoNet-GIANT achieves significantly faster linear convergence, comparable to that of its first-order counterparts, both in the compressed and uncompressed settings. CoNet-GIANT is efficient in terms of data usage, communication cost, and run-time, making it a suitable choice for distributed optimization over a wide range of wireless networks. Extensive experiments on synthetic data and the widely used CovType dataset demonstrate its superior performance.

This paper investigates remote state estimation in cyber-physical systems (CPS) where direct and reliable communication between distributed sensors and the control center is unavailable due to the lack of infrastructure. In such scenarios, unmanned aerial vehicles (UAVs) can be deployed as mobile data collectors to facilitate information transfer. While joint UAV trajectory and resource allocation problems have been widely studied in various contexts, adapting these to minimize estimation error in remote state estimation of linear CPS—accounting for the nonlinear evolution of error covariances and spatio-temporal coupling—presents distinct challenges. To tackle this, we formulate a joint optimization problem and decompose it into UAV path planning and UAV-sensor resource allocation. The path planning problem is modeled as a Markov Decision Process, where we establish the existence and periodicity of the optimal policy, and the resource allocation is formulated as a many-to-one matching game. We jointly solve these sub-problems via an alternating optimization algorithm that combines dynamic programming with a deferred acceptance-based matching scheme, and we prove the stability of the resulting matching. Simulation results confirm the periodic structure of the optimal path and demonstrate the effectiveness of the proposed method in reducing estimation error compared with other representative matching-based schemes.

In-body communication is a key enabler for next-generation healthcare applications, allowing seamless networking of implants. Fat tissue, with its lower water content and reduced signal attenuation compared to other body tissues at microwave frequencies, has emerged as a promising medium for radio-based in-body networks. Despite this advantage, signal leakage through the body can compromise privacy, exposing sensitive data and the mere presence of implants to external adversaries. This paper investigates the feasibility of covert communication in fat tissue-based in-body networks by leveraging the previously unexplored signal attenuation properties of human tissue to transmit data undetectable to adversaries, ensuring privacy beyond encryption. We develop a system in which an implanted transmitter communicates discreetly with an implanted receiver, shielded from external passive eavesdroppers. Our theoretical analysis and experimental results demonstrate that the attenuation properties of human tissues enable covert communication at reduced transmit power levels without requiring friendly jamming, unlike over-the-air systems. To further enhance covertness, we explore the use of an external friendly jammer and show its significant benefits. Experimental results show a 500% increase in the maximum channel capacity of covert communication, from 2.86 bps/Hz at -56 dBm transmit power without jamming, to 17 bps/Hz with no bit errors at 0 dBm transmit power with a friendly jammer, using the IEEE 802.15.4 standard for communication in the 2.45 GHz frequency band. These findings highlight that covert communication is achievable in fat tissue-based in-body networks at low data rates without additional infrastructure such as an external jammer. For applications requiring higher data rates, a friendly jammer offers a scalable solution, making this approach practical for a wide range of implant communication scenarios.

This paper considers observer-based detection of sensor bias injection attacks (BIAs) on linear cyber-physical systems with single output driven by white Gaussian noise. Despite their simplicity, BIAs pose a severe risk to systems with integrators, which we refer to as integrator vulnerability. Specifically, the residual generated by any linear observer is indistinguishable under attack and normal operation at steady state, making BIAs detectable only during transients. To address this, we propose a principled method based on Kullback-Leibler divergence to design a residual generator that significantly increases the signal-to-noise ratio against BIAs. For systems without integrator vulnerability, our method also enables a trade-off between transient and steady-state detectability. The effectiveness of the proposed method is demonstrated through numerical comparisons with three state-of-the-art residual generators.

In recent years, over-the-air aggregation has been widely considered in large-scale distributed learning, optimization, and sensing. In this paper, we propose an over-the-air federated policy gradient algorithm, where all agents simulta-neously broadcast an analog signal carrying local information to a common wireless channel, and a central controller uses the received aggregated waveform to update the policy parameters. We investigate the effect of noise and channel distortion on the convergence of the proposed algorithm, and establish the complexities of communication and sampling for finding an E-approximate stationary point. Finally, we present some simulation results to show the effectiveness of the algorithm.

The development of effective closed-loop control algorithms is one of the main challenges in Additive Man-ufacturing (AM). Many parameters of AM processes need continuous monitoring and regulation, with temperature being one of the most important. We investigate the design of an output-feedback controller of the temperature process within the extruder of a Fused Deposition Modelling (FDM) AM system. Based on a state space approach, and using input-output measurements, we first design a model-based linear quadratic tracking controller, followed by an equivalent model-free, data-driven version. We demonstrate these approaches using a simulator of the temperature evolution in the extruder of the AM system, based on a model validated and identified in recent literature. Our findings show that a comparable performance to the model-based case is possible using only measured data, generated through probing control explorations during the simulations.

Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms. These are, in fact, desirable qualities for DO frameworks, such as federated edge learning techniques, in the presence of data and system heterogeneity, and in scenarios where inter-node communication is the main bottleneck. Although computationally demanding, Newton-type (NT) methods have been recently advocated as enablers of robust convergence rates in challenging DO problems where edge devices have sufficient computational power. Along these lines, in this work$w$e propose Q-SHED, an original NT algorithm for DO featuring a novel bit-allocation scheme based on incremental Hessian eigenvectors quantization. The proposed technique is integrated with the recent SHED algorithm, from which it inherits appealing features like the small number of required Hessian computations, while being bandwidth-versatile at a bit-resolution level. Our empirical evaluation against competing approaches shows that Q-SHED can reduce by up to 60% the number of communication rounds required for convergence.

We study the denial-of-service (DoS) attack power allocation optimization in a multiprocess cyber–physical system (CPS), where sensors observe different dynamic processes and send the local estimated states to a remote estimator through wireless channels, while a DoS attacker allocates its attack power on different channels as interference to reduce the wireless transmission rates, and thus degrading the estimation accuracy of the remote estimator. We consider two attack optimization problems. One is to maximize the average estimation error of different processes, and the other is to maximize the minimal one. We formulate these problems as Markov decision processes (MDPs). Unlike the majority of existing works where the attacker is assumed to have complete knowledge of the CPS, we consider an attacker with no prior knowledge of the wireless channel model and the sensor information. To address this uncertainty issue and the curse of dimensionality, we provide a learning-based attack power allocation algorithm stemming from the double deep Q-network (DDQN) method. First, with a defined partial order, the maximal elements of the action space are determined. By investigating the characteristic of the MDP, we prove that the optimal attack allocations of both problems belong to the set of these elements. This property reduces the entire action space to a smaller subset and speeds up the learning algorithm. In addition, to further improve the data efficiency and learning performance, we propose two enhanced attack power allocation algorithms which add two auxiliary tasks of MDP transition estimation inspired by model-based reinforcement learning, i.e., the next state prediction and the current action estimation. Experimental results demonstrate the versatility and efficiency of the proposed algorithms in different system settings compared with other algorithms, such as the conventional value iteration, double Q-learning, and deep Q-network.

This paper considers the problem of communication and computation-efficient distributed learning via a wireless fading Multiple Access Channel (MAC). The distributed learning task is performed over a large network of nodes containing local data with the help of an edge server coordinating between the nodes. The information from each distributed node is transmitted as an analog signal through a noisy fading wireless MAC, using a common shaping waveform. The edge server receives a superposition of the analog signals, computes a new parameter estimate and communicates it back to the nodes, a process which continues until an appropriate convergence criterion is met. Unlike typical Federated learning approaches based on communication of local gradients and averaging at the edge server, in this paper, we investigate a scenario where the local nodes implement a second order optimization technique known as Determinantal Averaging. The communication complexity at each iteration per node of this method is the same as any gradient based method, i.e.$O(d)$, where$d$is the number of parameters. To reduce the computational load at each node, we also employ an approximate Newton method to compute the local Hessians. Under the usual assumptions of convexity and double differentiability on the local objective functions, we propose an algorithm titled Distributed Approximate Newton with Determinantal Averaging (DANDA). The state-of-art first and second-order distributed optimization algorithms are numerically compared with DANDA on a standard dataset with least squares based local objective functions (linear regression). Simulation results illustrate that DANDA not only displays faster convergence compared to gradient-based methods, but also compares favourably with exact distributed Newton methods, such as LocalNewton.

In this paper, we address the problem of covert communication under the presence of multiple wardens with a finite blocklength. The system consists of Alice, who aims to covertly transmit to Bob with the help of a jammer. The system also consists of a Fusion Center (FC), which combines all the wardens' information and decides on the presence or absence of Alice. Both Alice and jammer vary their signal power randomly to confuse the FC. In contrast, the FC randomly changes its threshold to confuse Alice. The main focus of the paper is to study the impact of employing multiple wardens on the trade-off between the probability of error at the FC and the outage probability at Bob. Hence, we formulate the probability of error and the outage probability under the assumption that the channels from Alice and jammer to Bob are subject to Rayleigh fading, while we assume that the channels from Alice and jammer to the wardens are not subject to fading. Then, we utilize a two-player zero-sum game approach to model the interaction between joint Alice and jammer as one player and the FC as the second player. We derive the pay-off function that can be efficiently computed using linear programming to find the optimal distributions of transmitting and jamming powers as well as thresholds used by the FC. The benefit of using a cooperative jammer is shown by means of analytical results and numerical simulations to neutralize the advantage of using multiple wardens at the FC.

In this paper, we consider non-Bayesian sequential change detection based on the Cumulative Sum (CUSUM) algorithm employed by an energy harvesting sensor where the distributions before and after the change are assumed to be known. In a slotted discrete-time model, the sensor, exclusively powered by randomly available harvested energy, obtains a sample and computes the log-likelihood ratio of the two distributions if it has enough energy to sense and process a sample. If it does not have enough energy in a given slot, it waits until it harvests enough energy to perform the task in a future time slot. We derive asymptotic expressions for the expected detection delay (when a change actually occurs), and the asymptotic tail distribution of the run-length to a false alarm (when a change never happens). We show that when the average harvested energy (H̅) is greater than or equal to the energy required to sense and process a sample (Es), standard existing asymptotic results for the CUSUM test apply since the energy storage level at the sensor is greater than E after a sufficiently long time. However, when the H̅s, the energy storage level can be modelled by a positive Harris recurrent Markov chain with a unique stationary distribution. Using asymptotic results from Markov random walk theory and associated nonlinear Markov renewal theory, we establish asymptotic expressions for the expected detection delay and asymptotic exponentiality of the tail distribution of the run-length to a false alarm in this non-trivial case. Numerical results are provided to support the theoretical results.

This paper considers a game-theoretic formulation of the covert communications problem with finite blocklength, where the transmitter (Alice) can randomly vary her transmit power in different blocks, while the warden (Willie) can randomly vary his detection threshold in different blocks. In this two-player game, the payoff for Alice is a combination of the coding rate to the receiver (Bob) and the detection error probability at Willie, while the payoff for Willie is the negative of his detection error probability. Nash equilibrium solutions to the game are obtained and shown to be efficiently computable using linear programming. For less covert requirements, our game-theoretic approach can achieve significantly higher coding rates than uniformly distributed transmit powers. We then consider the situation with an additional jammer, where Alice and the jammer can both vary their powers and jointly comprise one player, with Willie as the other player. The use of a jammer is shown in numerical simulations to lead to further significant performance improvements.

Synaptic plasticity depends on the gliotransmitters' concentration in the synaptic channel. And, an abnormal concentration of gliotransmitters is linked to neurodegenerative diseases, including Alzheimer's, Parkinson's, and epilepsy. In this paper, a theoretical investigation of the cause of the abnormal concentration of gliotransmitters and how to achieve its control is presented through a Cat+-signalling-based molecular communications framework. A feed-forward and feedback control technique is used to manipulate IP3 values to stabilize the concentration of Cat+ inside the astrocytes. The theoretical analysis of the given model aims i) to stabilize the Cat+ concentration around a particular desired level in order to prevent abnormal gliotransmitters' concentration (extremely high or low concentration can result in neurodegeneration), ii) to improve the molecular communication performance that utilizes Cat+ signalling, and maintain gliotransmitters' regulation remotely. It shows that the refractory periods from Cat+ can be maintained to lower the noise propagation resulting in smaller time-slots for bit transmission, which can also improve the delay and gain performances. The proposed approach can potentially lead to novel nanomedicine solutions for the treatment of neurodegenerative diseases, where a combination of nanotechnology and gene therapy approaches can be used to elicit the regulated Cat+ signalling in astrocytes, ultimately improving neuronal activity.

Directional wireless power transfer (WPT) technology provides a promising energy solution to remotely recharge the Internet of things sensors using directional antennas. Under a harvest-then-transmit protocol, the access point can adaptively allocate the transmit power among multiple energy directions to maximize the social welfare of the sensors, i.e., downlink sum received energy or uplink sum rate, based on full or quantized channel gains reported from the sensors. However, such power allocation can be challenged if each sensor belongs to a different agent and works in a competitive way. In order to maximize their own utilities, the sensors have the incentives to falsely report their channel gains, which unfortunately reduces the social welfare. To tackle this problem, we design the strategy-proof mechanisms to ensure that each sensor’s dominant strategy is to truthfully reveal its channel gain regardless of other sensors’ strategies. Under the benchmark full channel gain reporting (CGR) scheme, we adopt the Vickrey-Clarke-Groves (VCG) mechanism to derive the price functions for both downlink and uplink, where the truthfulness is guaranteed by asking each sensor to pay the social welfare loss of all other sensors attributable to its presence. For the 1-bit CGR scheme, the problem is more challenging due to the severe information asymmetry, where each sensor has true valuation of full channel gain but may report the false information of quantized channel gain. We prove that the classic VCG mechanism is no longer truthful and then propose two threshold-based price functions for both downlink and uplink, where the truthfulness is ensured by letting each sensor pay its own achievable utility improvement due to its participation. The numerical results validate the truthfulness of the proposed mechanism designs.

This paper focuses on the analysis of an optimal sensing and quantization strategy in a multi-sensor network where each individual sensor sends its quantized log-likelihood information to the fusion center (FC) for non-Bayesian quickest change detection. It is assumed that the sensors are equipped with a battery/energy storage device of finite capacity, capable of harvesting energy from the environment. The FC is assumed to have access to either non-causal or causal channel state information (CSI) and energy state information (ESI) from all the sensors while performing the quickest change detection. The primary observations are assumed to be generated from a sequence of random variables whose probability distribution function changes at an unknown time point. The objective of the detection problem is to minimize the average detection delay of the change point with respect to a lower bound on the rate of false alarm. In this framework, the optimal sensing decision and number of quantization bits for information transmission can be determined with the constraint of limited available energy due to finite battery capacity. This optimization is formulated as a stochastic control problem and is solved using dynamic programming algorithms for both non-causal and causal CSI and ESI scenario. A set of non-linear equations is also derived to determine the optimal quantization thresholds for the sensor log-likelihood ratios, by maximizing an appropriate Kullback-Leibler (KL) divergence measure between the distributions before and after the change. A uniform threshold quantization strategy is also proposed as a simple sub-optimal policy. The simulation results indicate that the optimal quantization is preferable when the number of quantization bits is low as its performance is significantly better compared to its uniform counterpart in terms of average detection delay. For the case of a large number of quantization bits, the performance benefits of using the optimal quantization as compared to its uniform counterpart diminish, as expected.

We consider a distributed detection system for a wireless sensor network over slow-fading channels. Each sensor only has knowledge of quantized channel state information (CSI) which is received from the fusion center via a limited feedback channel. We then consider transmit power allocation at each sensor in order to maximize a J-divergence based detection metric subject to a total and individual transmit power constraints. Our aim is to jointly design the quantization regions of all sensors CSI and the corresponding power allocations. A locally optimum solution is obtained by applying the generalized Lloyd algorithm (GLA). To overcome the high computational complexity of the GLA, we then propose a low-complexity near-optimal scheme which performs very close to its GLA based counterpart. This enables us to explicitly formulate the problem and to find the unique solution despite the non-convexity of the optimization problem. An asymptotic analysis is also provided when the number of feedback bits becomes large. Numerical results illustrate that only a small amount of feedback is needed to achieve a detection performance close to the full CSI case.

Designing decentralized policies for wireless communication networks is a crucial problem, which has only been partially solved in the literature so far. In this paper, we propose a Decentralized Markov Decision Process (Dec-MDP) framework to analyze a wireless sensor network with multiple users which access a common wireless channel. We consider devices with energy harvesting capabilities, that aim at balancing the energy arrivals with the data departures and with the probability of colliding with other nodes. Over time, an access point triggers a SYNC slot, wherein it recomputes the optimal transmission parameters of the whole network, and distributes this information. Every node receives its own policy, which specifies how it should access the channel in the future, and, thereafter, proceeds in a fully decentralized fashion, with no interactions with other entities in the network. We propose a multi-layer Markov model, where an external MDP manages the jumps between SYNC slots, and an internal Dec-MDP computes the optimal policy in the short term. We numerically show that, because of the harvesting, stationary policies are suboptimal in energy harvesting scenarios, and the optimal trade-off lies between an orthogonal and a random access system.

This paper studies the achievable throughput performance of the cognitive uplink under a limited primary cooperation scenario wherein the primary base station cannot feed back all interference channel gains to the secondary base station. To cope with the limited primary cooperation, we propose a feedback protocol called K-out-of-N feedback protocol, in which the primary base station feeds back only the KN smallest interference channel gains, out of N of them, to the secondary base station. We characterize the throughput performance under the K-out-of-N feedback protocol by analyzing the achievable multiuser diversity gains (MDGs) in cognitive uplinks for three different network types. Our results show that the proposed feedback mechanism is asymptotically optimum for interference-limited (IL) and individual-power-and-interference-limited (IPIL) networks for a fixed positive KN. It is further shown that the secondary network throughput in the IL and IPIL networks (under both the full and limited cooperation scenarios) logarithmically scales with the number of users in the network. In total-power-and-interference-limited (TPIL) networks, on the other hand, the K-out-of-N feedback protocol is asymptotically optimum for KN= Nδ, where δ ∈ (0, 1). We also show that, in TPIL networks, the secondary network throughput under both the limited and full cooperation scales logarithmically double with the number of users in the network. These results indicate that the cognitive uplink can achieve the optimum MDG even with limited cooperation from the primary network. They also establish the dependence of pre-log throughput scaling factors on the distribution of fading channel gains for different network types.

This paper investigates an optimal energy allocation problem for multi sensor estimation of a random source where sensors communicate their measurements to a remote fusion centre (FC) over orthogonal fading wireless channels using uncoded analog transmissions. The FC reconstructs the source using the best linear unbiased estimator (BLUE). The sensors have limited batteries but can harvest energy and also transfer energy to other sensors in the network. A distortion minimization problem over a finite-time horizon with causal and non-causal information is studied and the optimal energy allocation policy for transmission and sharing is derived. Several structural necessary conditions for optimality are presented for the two sensor problem with non-causal information and a horizon of two time steps. Numerical simulations are included to illustrate the theoretical results.

We investigate a power-constrained sensing matrix design problem for a compressed sensing framework. We adopt a mean square error (MSE) performance criterion for sparse source reconstruction in a system where the source-to-sensor channel and the sensor-to-decoder communication channel are noisy. Our proposed sensing matrix design procedure relies upon minimizing a lower-bound on the MSE. Under certain conditions, we derive closed-form solutions to the optimization problem. Through numerical experiments, by applying practical sparse reconstruction algorithms, we show the strength of the proposed scheme by comparing it with other relevant methods. We discuss the computational complexity of our design method, and develop an equivalent stochastic optimization method to the problem of interest that can be solved approximately with a significantly less computational burden. We illustrate that the low-complexity method still outperforms the popular competing methods.

This paper presents a design methodology for optimal energy allocation to estimate a random source using multiple wireless sensors equipped with energy harvesting technology. In this framework, multiple sensors observe a random process and then transmit an amplified uncoded analog version of the observed signal through Markovian fading wireless channels to a remote station. The sensors have access to an energy harvesting source, which is an everlasting but unreliable random energy source compared to conventional batteries with fixed energy storage. The remote station or so-called fusion centre estimates the realization of the random process by using a best linear unbiased estimator. The objective is to design optimal energy allocation policies at the sensor transmitters for minimizing total distortion over a finite-time horizon or a long term average distortion over an infinite-time horizon subject to energy harvesting constraints. This problem is formulated as a Markov decision process (MDP) based stochastic control problem and the optimal energy allocation policies are obtained by the use of dynamic programming techniques. Using the concept of submodularity, the structure of the optimal energy allocation policies is studied, which leads to an optimal threshold policy for binary energy allocation levels. Motivated by the excessive communication burden for the optimal control solutions where each sensor needs to know the channel gains and harvested energies of all other sensors, suboptimal decentralized strategies are developed where only statistical information about all other sensors' channel gains and harvested energies is required. Numerical simulation results are presented illustrating the performance of the optimal and suboptimal algorithms.

We consider physical-layer security in a novel MISO cooperative overlay cognitive radio network (CRN) with a single eavesdropper. We aim to design an artificial noise (AN) aided secondary transmit strategy to maximize the joint achievable secrecy rate of both primary and secondary links, subject to a global secondary transmit power constraint and guaranteeing any transmission of secondary should at least not degrade the receive quality of primary network, under the assumption that global CSI is available. The resulting optimization problem is challenging to solve due to its non-convexity in general. A computationally efficient approximation methodology is proposed based on the semidefinite relaxation (SDR) technique and followed by a two-step alternating optimization algorithm for obtaining a local optimum for the corresponding SDR problem. This optimization algorithm consists of a one-dimensional line search and a non-convex optimization problem, which, however, through a novel reformulation, can be approximated as a convex semidefinite program (SDP). Analysis on the extension to multiple eavesdroppers scenario is also provided. Simulation results show that the proposed AN-aided joint secrecy rate maximization design (JSRMD) can significantly boost the secrecy performance over JSRMD without AN.

This paper studies optimum power control and sum-rate scaling laws for the distributed cognitive uplink. It is first shown that the optimum distributed power control policy is in the form of a threshold based water-filling power control. Each secondary user executes the derived power control policy in a distributed fashion by using local knowledge of its direct and interference channel gains such that the resulting aggregate (average) interference does not disrupt primary's communication. Then, the tight sum-rate scaling laws are derived as a function of the number of secondary users N under the optimum distributed power control policy. The fading models considered to derive sum-rate scaling laws are general enough to include Rayleigh, Rician and Nakagami fading models as special cases. When transmissions of secondary users are limited by both transmission and interference power constraints, it is shown that the secondary network sum-rate scales according to 1/enhlog log (N), where n_h is a parameter obtained from the distribution of direct channel power gains. For the case of transmissions limited only by interference constraints, on the other hand, the secondary network sum-rate scales according to 1/eγglog (N), where γgis a parameter obtained from the distribution of interference channel power gains. These results indicate that the distributed cognitive uplink is able to achieve throughput scaling behavior similar to that of the centralized cognitive uplink up to a pre-log multiplier 1/e, whilst primary's quality-of-service requirements are met. The factor 1/e can be interpreted as the cost of distributed implementation of the cognitive uplink.

This paper considers the ergodic weighted sum rate maximization (WSRMax) problem for an underlay cognitive radio multiple input single output (MISO) broadcast channel. In this setting, a secondary network, consisting of a base-station with M transmit antennas and K single-antenna secondary users (SUs), is allowed to share the same spectrum with a primary user (PU), under an average total transmit power (ATTP) constraint and an average interference power (AIP) constraint at the PU receiver. We show that the ATTP constraint always remains active, and as the maximum ATTP Pav→ ∞, the ergodic WSR approaches infinity similar to conventional non-CR networks. We propose a novel low-complexity suboptimal beamforming scheme termed "Partially-Projected & Regularized Zero-Forcing Beamforming" (PP-RZFBF) with a close-form beamformer, by combining the regularized zero-forcing (RZF) with the channel projection idea, to achieve a tradeoff between maximizing secondary throughput and suppressing secondary multiuser interference as well as the interference on PU. In order to analyze and optimize the performance of this scheme, we employ the large system analysis technique, in the limit as M and K approach infinity with a fixed ratio r=K\M. This allows us to derive deterministic limiting approximations for the PP-RZFBF problem which enables us to determine asymptotically optimal beamformers for PP-RZFBF. In the large system limit, for the PP-RZFBF scheme, we also find that as Pav→ ∞, the interference on PU caused by the secondary transmission is asymptotically removed. A special suboptimal beamforming scheme called "CZFBF" is also considered, which involves zero forcing all the interference, including the secondary multiuser interference as well as the interference imposed on PU. Various interesting comparisons between PP-RZFBF and CZFBF are provided. Numerical simulations illustrate that the asymptotically optimal beamformers for the PP-RZFBF scheme provide an excellent performance even for finite-sized systems.

This paper considers the ergodic weighted sum rate (WSR) maximization problem for an underlay cognitive radio MISO broadcast channel, where a secondary network, consisting of a base-station with M transmit antennas and K single-antenna secondary users (SUs), is allowed to share the same spectrum with a primary user (PU), under an average transmit sum power (ATTP) constraint Pavand an average interference power (AIP) constraint on the PU. We show that the ATTP constraint is always active, and as Pav→ ∞, the ergodic WSR approaches infinity similar to the conventional non-CR network case. A low-complexity suboptimal beamforming scheme (called partially-projected regularized zero-forcing beamforming `PP-RZFBF') with a closed-form beamformer is proposed. Due to the non-convexity of PP-RZFBF scheme, a large system analysis is conducted in the limit as M and K approach infinity with a fixed finite ratio r = K/M. We derive deterministic limiting approximations for the PP-RZFBF problem which enables us to determine asymptotically optimal beamformers for PP-RZFBF. Numerical simulations illustrate that the asymptotically optimal beamformers turn out to be quite effective even for small M, K.

This paper studies optimal distributed power allocation and scheduling policies (DPASPs) for distributed total power and interference limited (DTPIL) cognitive multiple access networks in which secondary users (SU) independently perform power allocation and scheduling tasks using their local knowledge of secondary transmitter secondary base-station (STSB) and secondary transmitter primary base-station (STPB) channel gains. In such networks, transmission powers of SUs are limited by an average total transmission power constraint and by a constraint on the average interference power that SUs cause to the primary base-station. We first establish the joint optimality of water-filling power allocation and threshold-based scheduling policies for DTPIL networks. We then show that the secondary network throughput under the optimal DPASP scales according to 1/enhlog log (N), where nhis a parameter obtained from the distribution of STSB channel power gains and N is the total number of SUs. From a practical point of view, our results signify the fact that distributed cognitive multiple access networks are capable of harvesting multiuser diversity gains without employing centralized schedulers and feedback links as well as without disrupting primary's quality-of-service (QoS).

We address an optimal transmit power allocation problem that minimizes the outage probability of a secondary user (SU) who is allowed to coexist with a primary user (PU) in a narrowband spectrum sharing cognitive radio network, under a long term average transmit power constraint at the secondary transmitter (SU-TX) and an average interference power constraint at the primary receiver (PU-RX), with quantized channel state information (CSI) (including both the channels from SU-TX to SU-RX, denoted as g1and the channel from SU-TX to PU-RX, denoted as g0) at the SU-TX. The optimal quantization regions in the vector channel space is shown to have a "stepwise" structure. With this structure, the above outage minimization problem can be explicitly formulated and solved by employing the Karush-Kuhn-Tucker (KKT) necessary optimality conditions to obtain a locally optimal quantized power codebook. A low-complexity near-optimal quantized power allocation algorithm is derived for the case of large number of feedback bits. More interestingly, we show that as the number of partition regions approaches infinity, the length of interval between any two adjacent quantization thresholds on the g0axis is asymptotically equal when the average interference power constraint is active. Similarly, we show that when the average interference power constraint is inactive, the ratio between any two adjacent quantization thresholds on the g1axis becomes asymptotically identical. Using these results, an explicit expression for the asymptotic SU outage probability at high rate quantization (as the number of feedback bits goes to infinity) is also provided, and is shown to approximate the optimal outage behavior extremely well for large number of bits of feedback via numerical simulations. Analysis on the extension to multiple secondary users case (cognitive multiple-access network) is also discussed. Numerical results illustrate that with only a few bits of feedback, the derived algorithms provide secondary outage performance very close to that with full CSI at the SU-TX.

A shot-noise limited single-user single-input single-output (SISO) Poisson fading channel with partial channel state information (CSI) at the transmitter and perfect CSI at the receiver is considered. We address an optimal transmit power allocation problem that maximizes the ergodic capacity of the SISO Poisson fading channel subject to peak and average power constraints with only quantized CSI available at the transmitter, acquired via a no-delay and error-free feedback link with finite-rate from the receiver to the transmitter. Due to the non-convexity of the proposed optimization problem, a globally optimum solution is difficult to obtain. However, we manage to obtain a locally optimal quantized power allocation (QPA) scheme by solving its dual Lagrangian optimization problem. We develop two efficient optimal QPA algorithms for solving the dual optimization problem and show that both of these algorithms converge to the globally optimal solution of the dual problem. A low-complexity near-optimal QPA algorithm is also derived for the case of large number of feedback bits. The results are then extended to the high peak signal-to-noise ratio (SNR) regime and an explicit expression for the approximate asymptotic ergodic capacity behavior in the high SNR regime with high rate quantization (as the number of feedback bits goes to infinity) is also provided. It is seen via numerical simulations that this asymptotic capacity expression correctly approaches the capacity of the corresponding full CSI case as the number of feedback bits becomes large. Finally, the effectiveness of the derived algorithms is examined through numerical simulations.

This paper focuses on a cognitive radio network consisting of a secondary user (SU) equipped with orthogonal frequency-division multiplexing (OFDM) technology able to access N randomly fading frequency bands for transmitting delay-insensitive (e.g. data) as well as delay-sensitive (e.g. voice or video) data. Each band is licensed to a distinct delay-sensitive primary user (PU) interested in meeting a minimum rate guarantee for delay-sensitive services with a maximum allowable primary outage probability or a primary outage constraint (POC) . Typically, a PU is oblivious to the SU's existence and has its own power policy based on the channel side information (CSI) of its direct gain between the PU transmitter and the PU receiver only. Under the assumption that the SU knows PUs' power policies and CSI of the entire network, we solve the SU's ergodic capacity maximization problem subject to SU's average transmit power and outage probability constraints (SOC) and all POCs or the so-called service-outage based capacity maximization for SU with POCs. We use a rigorous probabilistic power allocation technique that allows us to derive optimal power policies applicable to both continuous and discrete fading channels. Also, a suboptimal power control policy is proposed in order to avoid the high computational complexity of the optimal policy when N is large. Numerical results are presented to illustrate the performance of the power allocation algorithms.

In cognitive multiple access networks, primary-secondary feedback links are needed to convey secondary transmitter primary base station (STPB) channel gains from the primary base station (PBS) to the secondary base station (SBS). To reduce the amount of feedback exchange between PBS and SBS, this paper proposes a feedback control protocol called K-smallest channel gains (K-SCG) feedback protocol in which the PBS feeds back the KNsmallest STPB channel gains, out of N of them, to the SBS. We study the performance of K-SCG feedback protocol for total power and interference limited (TPIL) networks when transmit powers of secondary users (SUs) are optimally allocated. In TPIL networks, transmit powers of SUs are limited by an average total power constraint as well as a constraint on the average total interference power that they cause to the PBS. It is shown that for KN= Nδwith δ ∈ (0, 1), K-SCG feedback protocol is asymptotically optimal, i.e., secondary network throughput under K-SCG and full feedback protocols scales according to 1/nhlog log (N) where nhis a parameter obtained from the distribution of secondary transmitter secondary base station (STSB) channel power gains, and N is the number of SUs. It is also shown that for KN= o(N), the interference power at the PBS converges to zero almost surely and in mean as N becomes large. This result implies that for N large enough, the secondary network just requires the indices of SUs corresponding to the KNsmallest STPB channel gains for performing jointly optimal user scheduling and power allocation rather than the actual realizations of STPB channel gains.

Multi-User MIMO (MU-MIMO) systems have gained numerous attention from researchers in the past decade due to its substantial gains in the system throughput. Most of the initial research assumes the knowledge of perfect channel state information at the transmitter (CSIT). However, this is considered impractical and research over the past few years has been focused on receivers feeding back limited information to the transmitter. In this work, we consider a transparent MU-MIMO system model with limited feedback. We propose a general framework for dynamic MU-MIMO scheduling with the capability to switch between Single-User MIMO mode and (Multi-Rank) Multi-User MIMO mode without users feeding back additional multi-user information. Specifically, this is done by each user carefully estimating its CQI under the hypothesis of Multi-Rank MultiUser MIMO transmission and taking advantage of the codebook structure. We consider the sum rate of the system assuming each user uses ML and LMMSE receiver and show that our proposed scheduler, with significantly reduced feedback load, outperforms the best-companion user pairing.

In spectrum sharing based cognitive radio networks, unlicensed users (secondary users) are allowed to communicate over the same frequency band as the licensed users (primary users) as long as the required quality-of-service (QoS) of the licensed users is guaranteed. This paper focuses on a cognitive radio network, where a secondary user (SU) sharing the same frequency band with a primary user (PU) wishes to transmit delay-insensitive as well as delay-sensitive data while the PU is interested in meeting a minimum rate guarantee for delay-sensitive services. Typically, PU's are oblivious to the SU's existence and has its own power policy based on channel side information (CSI) of its direct gain between PU transmitter and PU receiver. Under the assumption that SU knows PU's power policy and CSI of the entire network, we solve the optimal power allocation problem of maximizing SU's ergodic capacity subject to PU's outage probability constraint (POC), SU's outage probability constraint (SOC), and SU's average power constraint. We generalize earlier results which considered either ergodic capacity maximization or outage probability minimization for SU with POC, to the so-called service-outage based capacity optimization for SU with POC. We use a rigorous probabilistic power allocation technique that allows us to derive optimal power policies that are applicable to both continuous and discrete fading channels.

Abstract-This paper focuses on the secondary network throughput scaling in cognitive radio networks when secondary users' transmission powers are optimally allocated. Throughput scaling laws are obtained for two different cognitive radio networks under two different communication scenarios. In the first network type called power-interference limited networks, secondary users' transmission powers are limited by both average total power constraint and the constraint on the average interference that they cause to primary users. In the second network type called interference limited networks, secondary users' transmission powers are only limited by average interference constraint. For both network types, an asymmetric communication scenario, in which the channels between secondary users and the secondary base station experience Rayleigh fading and those between secondary users and the primary base station experience Rician fading, and a symmetric communication scenario, in which both types of channels experience Rayleigh fading, are considered. It is shown that the secondary network throughput scales like log log ((K+1/eK)N) and log ((K+1/eK)N) for power-interference limited and interference limited networks, respectively, under the asymmetric communication scenario, where N is the number of secondary users and K >; 0 is the Rician factor. For the symmetric communication scenario, these scaling laws are given by log log (N) and log(N) for power-interference limited and interference limited networks, respectively.

In this paper we investigate the distortion outage performance of distributed estimation schemes in wireless sensor networks, where a distortion outage is defined as the event that the estimation error or distortion exceeds a pre-determined threshold. The sensors transmit their observation signals using analog amplify and forward through coherent multi-access channels to the fusion center, which reconstructs a minimum mean squared error (MMSE) estimate of the physical quantity observed. We consider three power allocation schemes - 1) equal power allocation (EPA), 2) short-term optimal power allocation (ST-OPA), and 3) long-term optimal power allocation (LT-OPA). We study their diversity orders of distortion outage in terms of increasing numbers of sensors, and show that under Rayleigh fading EPA and ST-OPA achieve the same diversity order of N logN, where N is the number of sensors. On the other hand, in LT-OPA, we find that for N >; 1 the outage probability can be driven to zero with a finite amount of total power.

We consider a wideband spectrum sharing system where a secondary user can access a number of orthogonal frequency bands each licensed to a distinct primary user. We address the problem of optimum secondary transmit power allocation for its ergodic capacity maximization subject to an average sum (across the bands) transmit power constraint and individual average interference constraints on the primary users. The major contribution of our work lies in considering quantized channel state information (CSI) (for the vector channel space consisting of all secondary-to-secondary and secondary-to-primary channels) at the secondary transmitter as opposed to the prevalent assumption of full CSI in most existing work. It is assumed that a central entity called a cognitive radio network manager has access to the full CSI information from the secondary and primary receivers and designs (offline) an optimal power codebook based on the statistical information (channel distributions) of the channels and feeds back the index of the codebook to the secondary transmitter for every channel realization in real-time, via a delay-free noiseless limited feedback channel. A modified Generalized Lloyds-type algorithm (GLA) is designed for deriving the optimal power codebook, which is proved to be globally convergent and empirically consistent. An approximate quantized power allocation (AQPA) algorithm is presented, that performs very close to its GLA based counterpart for large number of feedback bits and is significantly faster. We also present an extension of the modified GLA based quantized power codebook design algorithm for the case when the feedback channel is noisy. Numerical studies illustrate that with only 3-4 bits of feedback per band, the modified GLA based algorithms provide secondary ergodic capacity very close to that achieved by full CSI and with only as little as 4 bits of feedback per band, AQPA provides a comparable performance, thus making it an attractive choice for practical implementation.

This paper is concerned with decentralized estimation of a Gaussian source using multiple sensors. We consider a diversity scheme where only the sensor with the best channel sends their measurements over a fading channel to a fusion center, using the analog amplify and forwarding technique. The fusion centre reconstructs a minimum mean squared error (MMSE) estimate of the source based on the received measurements. A distributed version of the diversity scheme where sensors decide whether to transmit based only on their local channel information is also considered. We derive asymptotic expressions for the expected distortion (of the MMSE estimate at the fusion centre) of these schemes as the number of sensors becomes large. For comparison, asymptotic expressions for the expected distortion for a coherent multiaccess scheme and an orthogonal access scheme are derived. It is seen that as opposed to the coherent multiaccess scheme and the orthogonal scheme (where the expected distortion decays as 1/M, M being the number of sensors), the expected distortion decays only as 1/ln(M) for the diversity schemes. This reduction of the decay rate can be seen as a tradeoff between the simplicity of the diversity schemes and the strict synchronization and large bandwidth requirements for the coherent multiaccess and the orthogonal schemes, respectively. We study for the diversity schemes, the optimal power allocation for minimizing the expected distortion subject to average power constraints. The effect of optimizing the probability of transmission on the expected distortion in the distributed scenario is also studied. It is proved that for Rayleigh fading optimal sensor transmit power allocation achieves the same asymptotic scaling law as the constant power allocation scheme, whereas it is observed that optimizing the sensor transmission probability (with or without optimal power allocation) in the distributed case makes very little difference to the asymptotic scaling laws.

In this paper we investigate the problem of distortion outage minimization in a clustered sensor network where sensors within each cluster send their noisy measurements of a random Gaussian source to their respective clusterheads (CH) using analog forwarding and a non-orthogonal multi-access scheme under the assumption of perfect distributed beamforming. The CHs then amplify and forward their measurements to a remote fusion center over orthogonal Rayleigh distributed block-fading channels. Due to fading, the distortion between the true value of the random source and its reconstructed estimate at the fusion center becomes a random process. Motivated by delay-limited applications, we seek to minimize the probability that the distortion exceeds a certain threshold (called the "distortion outage" probability) by optimally allocating transmit powers to the CHs. In general, the outage minimizing optimal power allocation for the CH transmitters requires full instantaneous channel state information (CSI) at the transmitters, which is difficult to obtain in practice. The novelty of this paper lies in designing locally optimal and sub-optimal power allocation algorithms which are simple to implement, using limited channel feedback where the fusion center broadcasts only a few bits of feedback to the CHs. Numerical results illustrate that a few bits of feedback provide significant improvement over no CSI and only 6-8 bits of feedback result in outages that are reasonably close to the full CSI performance for a 6-cluster sensor network. We also present results using a simultaneous perturbation stochastic approximation (SPSA) based optimization algorithm that provides further improvements in outage performance but at the cost of a much greater computational complexity.

In this paper, we study an optimum power allocation problem for expected achievable rate maximization in a typical three node cooperative relay network with slow block fading channels. A long term average power and an outage probability threshold serve as the constraints on the problem. This is motivated by the fact that in many applications, a mixture of delay-sensitive and -insensitive data are transmitted and either maximizing expected achievable rate or minimizing the outage probability would probably not provide the desired solution. The problem considered in this paper (known as the "service outage based rate and power allocation" problem in literature) thus achieves a tradeoff between the two extremes of ergodic capacity and outage capacity. We show that the optimum power allocation scheme is a switched policy between two deterministic policies. Extensive numerical results are presented to demonstrate the benefits of cooperation as opposed to that of non-cooperation or direct transmission. We study the performance of the two simple but popular relaying schemes, namely, the amplify-and-forward and decode-and- forward protocols. It is seen that these relaying protocols are extremely resilient against demanding outage probability constraints over a range of basic rate requirements and average power constraints.

A single-input-single-output (SISO) Poisson fading channel with perfect channel state information (CSI) at the transmitter and the receiver is considered. For a fixed basic rater0, a service outage occurs when the instantaneous transmission rate falls below the rater0. The objective of this paper is to maximize the expected transmission rate subject to peak and average transmitter power constraints and a constraint on the service outage probability. The optimal power allocation scheme is shown to be a combination of the ergodic capacity-achieving power allocation and the outage capacity-achieving power allocation schemes with a randomization between the two deterministic schemes in a boundary set. This randomization is not necessary when the channel fade distribution is continuous. By combining the concepts of ergodic and outage capacity, the proposed optimal scheme judiciously resolves the conflicting objectives of high expected transmission rate and low outage probability.

We consider a service outage based power and rate allocation problem for the Poisson fading channel with perfect channel state information at the transmitter and the receiver. A service outage occurs when the instantaneous transmission rate falls below the basic rate r0. The objective of the allocation problem is to maximize the expected transmission rate subject to peak and average transmitter power constraints and a constraint on the outage probability epsiv. A general class of probabilistic power allocation schemes are considered, and the optimum power allocation scheme is shown to be deterministic except for channel fades in a boundary set. When the problem is feasible, the optimum scheme is a combination of ergodic capacity-achieving power allocation and outage capacity-achieving power allocation schemes with a randomization between the two deterministic schemes in the boundary set. This randomization is not necessary when the channel fade distribution is continuous.

The information outage probability of a shot-noise limited direct detection multiple-input–multiple-output (MIMO) optical channel subject to block fading is considered. Information is transmitted over this channel by modulating the intensity of a number of optical signals, one corresponding to each transmit aperture, and individual photon arrivals are observed at multiple receive photodetector apertures. The transmitted signals undergo multiplicative fading. The fading occurs in coherence intervals of fixed duration in each of which the channel fade matrix remains constant, and changes across successive such intervals in an independent and identically distributed fashion. The transmitter and the receiver are assumed to be provided with perfect channel state information (CSI). An optimization formulation for the outage probability problem is outlined and an exact characterization of the optimal average conditional duty cycles is provided.

We address a lifetime maximization problem for a single-hop wireless sensor network where multiple sensors encode and communicate their measurements of a Gaussian random source to a fusion centre (FC). The FC is required to reconstruct the source within a prescribed distortion threshold. The lifetime optimization problem is formulated as a joint power, rate and timeslot (for TDMA) allocation problem under the constraints of the well known rate distortion constraints for the Gaussian CEO problem, the capacity constraints of the wireless links, the energy constraints of the sensor nodes and the strict delay constraint within which the encoded sensor data must arrive at the FC. We study the performances of TDMA and an interference limited non-orthogonal multiple access (NOMA) (with single user decoding) based protocols and compare them against the upper bound provided by the optimal lifetime performance where the capacity constraints are given by the Gaussian multiaccess capacity region. While the constrained non-linear optimization problems for the TDMA and the Gaussian multiaccess cases are convex, the NOMA case results in a non-linear nonconvex D.C. (difference of convex functions) programming problem. We provide a simple successive convex approximation based algorithm for the NOMA case that converges fast to a suboptimal lifetime performance that compares favourably against the upper bound provided by the Gaussian multiaccess case. Extensive numerical studies are presented for both static and slow fading wireless environments with full channel state information at the fusion centre.

The information outage probability of a shot-noise limited direct detection multiple-input multiple-output (MIMO) optical channel subject to block fading is considered. Information is transmitted over this channel by modulating the intensity of a number of optical signals, one corresponding to each transmit aperture, and individual photon arrivals are observed at multiple receive photodetector apertures. The transmitted signals undergo multiplicative fading, and the fading occurs in coherence intervals of fixed duration in each of which the channel fade matrix remains constant. The channel fade matrix varies across successive coherence intervals in an independent and identically distributed fashion. The transmitter and the receiver are assumed to have perfect channel state information (CSI). The main contributions are a formulation of the outage probability problem as an optimization problem and an exact characterization of the optimal solution for the special case of the MIMO Poisson fading channel with two transmit apertures.

The implementation of practical adaptive resource allocation scheme remains a key criterion to be satisfied for realising spectrally efficient multitone wireless communications. The ever-increasing demand for spectrally efficient broadband wireless transmission technologies has spurred intensive research leading towards the implementation of adaptive OFDM and adaptive MIMO systems. Efforts in this direction have been frustrated however by the lack of a clear and accurate description of the fading behaviour typically encountered in the broadband wireless transmission environment. This has been partially been overcome by the use of mathematical modelling which captures certain large-scale characteristics of the channel and facilitates theoretical research. The "average" channel parameters gleaned from these processes is typically then used to inform the design and configuration of wireless networking equipment after the broad application of generous safety margins. The resulting solution is therefore quite robust to certain transient channel quality degradation yet the generous safety tolerances render it unable to exploit other transient transmission quality improvements We seek to overcome the problems associated with this approach by applying a theoretically sound novel adaptive resource allocation framework to actual broadband wireless channel development data. The allocation framework is derived from the optimal OFDM allocation scheme for a known channel: the channel development data is obtained from actual measurement of a broadband wireless mobile environment, Prediction techniques are employed to overcome the time lag between channel assessment and symbol transmission. We present the details of the predictive resource allocation scheme used and include a broad characterisation of the transmission environment in terms of the time-varying fading processes observed. We provide some results of the application of this scheme as typical performance levels that may be achieved in an actual transmission environment.

In this paper, we address the optimal power allocation problem for minimizing capacity outage probability in multiple time-scale parallel fading channels. Extending ideas from the work of Dey and Evans (2005), we derive the optimal power allocation scheme for parallel fading channels with fast Rayleigh fading, as a function of the slow fading gains. Numerical results are presented to demonstrate the outage performance of this scheme for lognormal slow fading on two parallel channels.

Cross-layer design can significantly improve the performance of mobile ad-hoc networks (MANETs), as indicated by the flurry of recent results in the literature. Much of this work stems from the Kelly network utility maximization (NUM) framework, where convexity is crucial for developing algorithms that reach the global optimum. Unfortunately many problems are nonconvex in nature, so convex approximations are abundant. In this paper, we consider the joint optimization of source data-rates and link transmitter powers in a MANET, specifically dealing with the statistical variations of the wireless channel. In this paradigm we show that the commonly applied high-SIR convex approximation is unrealistic, so we seek to find solutions of the unmodified NUM problem. Our first result shows that the canonical formulation (previously thought to be nonconvex) is indeed a convex problem for logarithmic TCP-Vegas utilities; we then derive an algorithm reaching the global optimum. Our main result caters for the general case of strictly concave utilities, where we derive an algorithm that provably converges to the global solution of the underlying nonconvex NUM problem.

We address the problem of achieving outage probability constraints on the uplink of a code-division multiple-access (CDMA) system employing power control and linear multiuser detection, where we aim to minimize the total expended power. We propose a generalized framework for solving such problems under modest assumptions on the underlying channel fading distribution. Unlike previous work, which dealt with a Rayleigh fast-fading model, we allow each user to have a different fading distribution. We show how this problem can be formed as an optimization over user transmit powers and linear receivers, and, where the problem is feasible, we provide conceptually simple iterative algorithms that find the minimum power solution while achieving outage specifications with equality. We further generalize a mapping from outage probability specifications to average signal-to-interference-ratio constraints that was previously applicable only to Rayleigh-faded channels. This mapping allows us to develop suboptimal, computationally efficient algorithms to solve the original problem. Numerical results are provided that validate the iterative schemes, showing the closeness of the optimal and mapped solutions, even under circumstances where the map does not guarantee that constraints will be achieved.

We present a game-theoretic treatment of distributed power control in CDMA wireless systems using outage probabilities. We first prove that the noncooperative power control game considered admits a unique Nash equilibrium (NE) for uniformly strictly convex pricing functions and under some technical assumptions on the SIR threshold levels. We then analyze global convergence of continuous-time as well as discrete-time synchronous and asynchronous iterative power update algorithms to the unique NE of the game. Furthermore, we show that a stochastic version of the discrete-time update scheme, which models the uncertainty due to quantization and estimation errors, converges almost surely to the unique NE point. We finally investigate and demonstrate the convergence and robustness properties of these update schemes through simulation studies.

We propose ad framework to optimally allocate transmission resources for a digital-user multichannel transmission link operating a dynamically-fading wireless environment. Beginning with the statistical properties of fading phenomena observed in such channels, we demonstrate how convex optimisation theory may be harnessed for this purpose. Examples are presented to illustrate how service quality criteria may be used to dynamically identify operating points for the allocation algorithm.

We derive the optimal power control strategy to maximize the sum rate of a multiple access channel with two time-scale fading, where transmitters have access to each of the other users' 'slow' fading information and the statistics of the 'fast' fading, but no knowledge of the instantaneous fast fading states. Assuming identical fast fading distributions for all users, it is found that the optimal strategy is to let at most one user transmit, with the user transmitting the one with the 'best' slow fading condition. An example with users undergoing lognormal shadowing and Rayleigh fast fading is considered, and capacity comparisons are made. Simple sub-optimal power control schemes which provide close to optimal performance in certain favorable channel conditions are also proposed and analysed.

This paper considers the power-control problem for a fading channel in an information-theoretic framework. We derive power-control schemes to optimize ergodic capacity, outage capacity, and capacity with a service outage constraint. The novelty in the paper lies in the use of a two-time-scale fading process and its implications for the channel-state information available at the transmitter.

How can we achieve the conflicting goals of reduced transmission power and increased capacity in a wireless network, without attempting to follow the instantaneous state of a fading channel? In this paper, we address this problem by jointly considering power control and multiuser detection (MUD) with outage-probability constraints in a Rayleigh fast-fading environment. The resulting power-control algorithms (PCAs) utilize the statistics of the channel and operate on a much slower timescale than traditional schemes. We propose an optimal iterative solution that is conceptually simple and finds the minimum sum power of all users while meeting their outage targets. Using a derived bound on outage probability, we introduce a mapping from outage to average signal-to-interference ratio (SIR) constraints. This allows us to propose a suboptimal iterative scheme that is a variation of an existing solution to a joint power control and MUD problem involving SIR constraints. We further use a recent result that transforms complex SIR expressions into a compact and decoupled form, to develop a noniterative and computationally inexpensive PCA for large systems of users. Simulation results are presented showing the closeness of the optimal and mapped schemes, speed of convergence, and performance comparisons.

We consider an uplink power control problem with constraints on outage probability, for cellular CDMA systems where allocation decisions are made on a slow time-scale. A generalized framework to solve such problems for a wide range of fading distributions is proposed, including an extension that couples power control with a minimum outage probability multiuser receiver. The resulting algorithms are simple and iterative in nature that yield the optimal minimum sum-power solution. Deriving a general upper bound on outage probability, we map these problems to equivalent, sub-optimal and computationally efficient iterative algorithms. We give numerical results to validate the methods developed for a variety of Nakagami-m fading figures.

Quantiser design for a nonlinear filter is considered in the context of a decentralised estimation system with communication constraints. The filter is based on the quantised outputs of a discrete-time, two-state hidden Markov model (HMM) as measured by two remote sensor nodes. The optimal quantisation scheme is obtained by maximising the mutual information between the quantised measurements and the hidden Markov states. Filter performance is measured in terms of the probability of estimation error and is investigated through simulation for HMMs with both independent and correlated white Gaussian noise in the measurements. The performance of the filter based on continuous, unquantised signals provides a benchmark for the performance of the filter based on quantised measurements. Therefore, a method for computing the probability of estimation error directly for the continuous filter is also presented.

This paper proposes a new scheme coupling power control with a minimum outage probability multiuser detector. The resultant iterative algorithm is conceptually simple and finds the minimum sum transmission power of all users with a set of outage probability constraints. Bound on the outage probability expression are found that extend a previous result that did not include receiver noise. These bounds are used to create a suboptimal scheme coupling power control and a MMSE multiuser detector. This new problem becomes a variant of an existing problem where outage probability constraints are first mapped to average SIR threshold constraints. Simulation results are presented showing the closeness of the two schemes and speed of convergence.

Combined compression and classification problems are becoming increasingly important in many applications with large amounts of sensory data and large sets of classes. These applications range from automatic target recognition (ATR) to medical diagnosis, speech recognition, and fault detection and identification in manufacturing systems. In this paper, we develop and analyze a learning vector quantization (LVQ) based algorithm for combined compression and classification. We show convergence of the algorithm using the ODE method from stochastic approximation. We illustrate the performance of the algorithm with some examples.

We consider the estimation of various Markov-modulated time series. We obtain maximum likelihood estimates of the time-series parameters including the Markov chain transition probabilities and the time-series coefficients using the expectation maximization (EM) algorithm. In addition, the recursive EM algorithm is used to obtain on-line parameter estimates. Simulation studies show that both algorithms yield satisfactory results.>