Multiuser, Multiple Input, Single Output (MU-MISO) systems are indispensable in next-generation wireless networks such as 5G and 6G. The spatial diversity of MISO has been leveraged in these wireless networks to improve the capacity. Furthermore, several recent studies have utilised redundancies in the content request along with this spatial diversity for further capacity improvements. In such systems, the MISO Max-Min Fair (MMF) Beamforming schemes based on SDMA, NOMA, OMA and Rate-Splitting are shown to improve the delivery rates. However, in these studies the key aspects such as the queueing delays in the downlink and the user dynamics have generally been ignored. In this work, we study how queueing, beamforming and user dynamics affect the Quality-of-Service (user delay) of downlink in MU-MISO content centric networks (CCNs). We also show that a Simple Multicast Queue (SMQ) developed for SISO, when adapted to MU-MISO provides good performance due to its inherent stability. However, we observe that MMF Beamforming coupled with SMQ can be unfair to users with good channels. Thus, we propose an alternative scheme, called Dual-SMQ to address this problem. We also provide theoretical approximations for the mean delay experienced by the users in such systems.

We consider a server connected to several users over a shared finite capacity link. Each user is equipped with a cache. File requests at the users are generated as independent Poisson processes according to a popularity profile from a fixed finite library of files. The server has access to all the files in the library. Users can store parts of the files or full files from the library in their local caches. The server should send missing parts of the files requested by the users. The server attempts to fulfill the pending requests with minimal transmissions exploiting multicasting and coding opportunities among the pending requests. We consider a queue in which requests for the same file from different users is merged and transmitted simultaneously to all the requested users. We study and compare the performance of this queuing system in terms of queuing delays when LRU caches are used and when coded caching schemes proposed in the literature are used. We provide approximate expressions for the mean queuing delay for these models and establish their effectiveness with simulations. We extend the analysis to the case when transmission errors are also taken into account.

We consider the problem of minimizing age in a multihop wireless network. There are multiple source-destination pairs, transmitting data through multiple wireless channels, over multiple hops. We propose a network control policy which consists of a distributed scheduling algorithm, utilizing channel state information and queue lengths at each link, in combination with a packet dropping rule. Dropping of older packets locally at queues is seen to reduce the average age of flows, even below what can be achieved by Last Come First Served (LCFS) scheduling. Dropping of older packets also allows us to use the network without congestion, irrespective of the rate at which updates are generated. Furthermore, exploiting system state information substantially improves performance. The proposed scheduling policy obtains average age values close to a theoretical lower bound as well.

In this work, we consider a Gaussian multiple access channel (GMAC) subject to block fading that is independent across users. We derive inner bounds on the capacity region for a given finite codeword length and non-vanishing average probability of error. We assume perfect channel state information (CSI) at the receiver (CSIR) and no CSI at the transmitters. The bounds are derived subject to a peak power constraint on the transmitted codewords. Our inner bounds are derived using Gaussian codebooks. These bounds characterize deviation from the ergodic capacity region upto second-order. Using numerical examples, we illustrate the back-off from the boundary of the ergodic capacity region.

We consider a block fading additive white Gaussian noise (AWGN) channel with perfect channel state information (CSI) at the transmitter (CSIT) and the receiver (CSIR). For a given codeword length and non-vanishing average probability of error, we obtain lower and upper bounds on the maximum transmission rate. First, we derive bounds when the transmitter has a time varying peak power constraint imposed by an energy harvesting device; such devices power many modern wireless devices. Next, using the same techniques we obtain bounds for the canonical peak and average power constraint cases. The bounds characterize the finite blocklength back-off from the channel capacity, which is in turn achieved by water-filling power allocation across time. Finally, we derive bounds on the moderate deviations constant for the block fading model. We compare the bounds numerically to bring out the efficacy of our results.

We consider a multihop wireless network. There are multiple source destination pairs. We propose a scheduling algorithm to provide end-to-end mean delay guarantees to the flows. We demonstrate that the algorithm is also throughput optimal. Under heavy traffic, we obtain a Brownian approximation of the network. Furthermore, we also show that the stationary distribution of the scaled process of the network converges to that of the Brownian limit, providing an approximation to the stationary distribution under heavy traffic. Finally simulations further verify our claims.

We consider a wireless system with one server and several users making requests from a fixed library of files. The request process from each user is assumed to be an independent Poisson process. The pending requests at the server are queued in a special queue called multicast queue. In this queue, the requests for the same file are merged. A transmission from the server is received by all the users who have requested the file. The channel from the server to each user experiences block fading. We propose several queuing and service strategies for this system and compare their mean response time. We show that our multicast queue significantly out-performs the usual first-in-first-out (FIFO) queue with no merging. We also propose a power control policy to further improve the performance.

Today an increasing number of consumer devices such as head phones, wearables, light bulbs and even baseball bats, are Bluetooth-enabled thanks to the widespread support of the technology by phone manufacturers and mobile operating system vendors. The ability for any device to seamlessly connect and exchange information with smartphones via Bluetooth Low Energy (BLE) protocol promises unlimited room for innovation. However, it also brings about new privacy challenges. We show that the BLE protocol together with the Bluetooth permission model implemented in the Android and iOS operating systems can be used for cross-app tracking unbeknownst to the individuals.

Random distortion testing (RDT) addresses the problem of testing whether or not a random signal, Ξ, deviates by more than a specified tolerance, τ, from a fixed value, ξ0[1]. The test is nonparametric in the sense that the distribution of the signal under each hypothesis is assumed to be unknown. The signal is observed in independent and identically distributed (i.i.d) additive noise. The need to control the probabilities of false alarm and missed detection while reducing the number of samples required to make a decision leads to the SeqRDT approach. We show that under mild assumptions on the signal, SeqRDT will follow the properties desired by a sequential test. Simulations show that the SeqRDT approach leads to faster decision making compared to its fixed sam-ple counterpart Block-RDT [2] and is robust to model mismatches compared to the Sequential Probability Ratio Test (SPRT) [3] when the actual signal is a distorted version of the assumed signal especially at low Signal-to-Noise Ratios (SNRs).

In this work, we obtain lower and upper bounds on the maximal transmission rate at a given codeword length n, average probability of error ϵ and power constraint P̅, over a finite valued, block fading additive white Gaussian noise (AWGN) channel with channel state information (CSI) at the transmitter and the receiver. These bounds characterize deviation of the finite blocklength coding rates from the channel capacity which is in turn achieved by the water filling power allocation across time. The bounds obtained also characterize the rate enhancement possible due to the CSI at the transmitter in the finite blocklength regime. The results are further elucidated via numerical examples.

Control of wireless multihop networks, while simultaneously meeting end-to-end mean delay requirements of different flows is a challenging problem. Additionally, distributed computation of control parameters adds to the complexity. Using the notion of discrete review used in fluid control of networks, a distributed algorithm is proposed for control of multihop wireless networks with interference constraints. The algorithm meets end-to-end mean delay requirements by solving an optimization problem at review instants. The optimization incorporates delay requirements as weights in the function being maximized. The weights are dynamic and vary depending on queue length information. The optimization is done in a distributed manner using an incremental gradient ascent algorithm. The stability of the network under the proposed policy is analytically studied and the policy is shown to be throughput optimal.

Control of multihop Wireless networks in a distributed manner while providing end-to-end delay requirements for different flows, is a challenging problem. Using the notions of Draining Time and Discrete Review from the theory of fluid limits of queues, an algorithm that meets delay requirements to various flows in a network is constructed. The algorithm involves an optimization which is implemented in a cyclic distributed manner across nodes by using the technique of iterative gradient ascent, with minimal information exchange between nodes. The algorithm uses time varying weights to give priority to flows. The performance of the algorithm is studied in a network with interference modelled by independent sets.

In this letter, we model a complete-information zero-sum game between a centralized detection network with a multiple access channel between the sensors and the fusion center (FC), and a jammer with multiple transmitting antennas. We choose error probability at the FC as the performance metric, and investigate pure strategy equilibria for this game, and show that the jammer has no impact on the FC's error probability by employing pure strategies at the Nash equilibrium. Furthermore, we also show that the jammer has an impact on the expected utility if it employs mixed strategies.

We consider an additive white Gaussian channel where the transmitter is powered by an energy harvesting source. For such a system, we provide a lower bound on the maximal codebook at finite code lengths that improves upon previously known bounds.

We study TCP performance over ON-OFF channels, i.e., channels which are busy/unavailable for transmissions intermittently. The data may also be subject to random errors. Such situations arise in cognitive radio networks when secondary users opportunistically use the channel when the licensed user is not using it. We use a Markov model to evaluate the TCP throughput and probability of retransmission timeout when the ON-OFF periods are exponentially distributed and then extend it to phase-type ON and phase-type OFF periods. We then generalize the Markovian model to more general ON-OFF duration distributions and also to multiple TCP connections.

We propose a distributed sequential algorithm for quick detection of spectral holes in a Cognitive Radio set up. Two or more local nodes make decisions and inform the fusion centre (FC) over a reporting Multiple Access Channel (MAC), which then makes the final decision. The local nodes use energy detection and the FC uses mean detection in the presence of fading, heavy-tailed electromagnetic interference (EMI) and outliers. The statistics of the primary signal, channel gain and the EMI is not known. Different nonparametric sequential algorithms are compared to choose appropriate algorithms to be used at the local nodes and the FC. Modification of a recently developed random walk test is selected for the local nodes for energy detection as well as at the fusion centre for mean detection. We show via simulations and analysis that the nonparametric distributed algorithm developed performs well in the presence of fading, EMI and outliers. The algorithm is iterative in nature making the computation and storage requirements minimal.

We consider a two user multiple-access channel with an eavesdropper at the receiving end. We use previously transmitted messages as a key in the next slot till we achieve the capacity region of the usual multiple access channel (MAC).

We consider a Gaussian interference channel with independent direct and cross link channel gains, each of which is independent and identically distributed across time. Each transmitter-receiver user pair aims to maximize its long-term average transmission rate subject to an average power constraint. We formulate a stochastic game for this system in three different scenarios. First, we assume that each user knows all direct and cross link channel gains. Later, we assume that each user knows channel gains of only the links that are incident on its receiver. Lastly, we assume that each user knows only its own direct link channel gain. In all cases, we formulate the problem of finding the Nash equilibrium as a variational inequality (VI) problem and present a novel heuristic for solving the VI. We also present a lower bound on the utility for each user at any Nash equilibrium in the case of the games with partial information. We obtain this lower bound using a water-filling like power allocation that requires only knowledge of the distribution of a user's own channel gains and average power constraints of all users.

We consider a multihop wireless network with multiple users. The channels may experience fading. The power is consumed only during transmission and is a general monotonically increasing function of rate. We provide low complexity algorithms for joint routing, scheduling and power control which ensure stability of the queues, certain minimum rates and/or upper bound on the end-to-end, mean delays.

We consider non parametric sequential hypothesis testing problem when the distribution under the null hypothesis is fully known but the alternate hypothesis corresponds to some other unknown distribution with some loose constraints. We propose a simple algorithm to address the problem. This is also generalized to the case when the distribution under the null hypothesis is not fully known. These problems are primarily motivated from wireless sensor networks and spectrum sensing in Cognitive Radios. A decentralized version utilizing spatial diversity is also proposed. Its performance is analysed and asymptotic properties are proved. The simulated and analysed performance of the algorithm are shown to be better than an earlier algorithm addressing the same problem with similar assumptions. We also modify the algorithm for optimizing performance when information about the prior probabilities of occurrence of the two hypotheses are known.

Energy harvesting sensor nodes are gaining popularity due to their ability to improve the network life time and are becoming a preferred choice supporting green communication. In this paper, we focus on communicating reliably over an additive white Gaussian noise channel using such an energy harvesting sensor node. An important part of this paper involves appropriate modeling of energy harvesting, as done via various practical architectures. Our main result is the characterization of the Shannon capacity of the communication system. The key technical challenge involves dealing with the dynamic (and stochastic) nature of the (quadratic) cost of the input to the channel. As a corollary, we find close connections between the capacity achieving energy management policies and the queueing theoretic throughput optimal policies.

We consider the problem of spectrum sensing in a Cognitive Radio (CR) system when the primaries can be occupying a few subbands in a wideband spectrum. Since the primary signal dimension is large, Nyquist rate can be very high. Compressive sensing (CS) can be useful in this setup. However a CR system needs to operate at a very low SNR(~ -20dB) where the compressive sensing techniques are usually not successful. Combining them with statistical techniques can be useful. But this has been difficult because the statistics of the parameters obtained from the recovery algorithms (e.g., OMP) are not available. We develop a suboptimal recovery algorithm COR for which the statistics can be easily approximated. This allows us to use Neyman Pearson technique as well as sequential detection techniques with CS. The resulting algorithms provide satisfactory performance at -20 dB SNR. In fact COR's recovery performance is better than OMP itself at low SNR. We also modify the algorithm for the scenario when the channel gains and the noise variance may also not be available.

We study a system of multiple routers traversed by multiple TCP connections using TCP New Reno, CUBIC and Compound. More than one router may be congested. To analyze this system we will use earlier theoretical models of TCP New Reno and CUBIC but develop a new model of TCP Compound experiencing random packet losses and queuing delays. We model the router queues as M/GI/1 queues with arrival rates controlled by TCP window flow control. We also look at an alternate approach assuming proportional fairness of TCP to find the throughputs of the multiple TCP connections. These approximations are validated through comparison with ns-2 simulations.

We consider a scheduler for the downlink of a wireless channel when only partial channel-state information is available at the scheduler. We characterize the network stability region and provide two throughput-optimal scheduling policies. We also derive a deterministic bound on the mean packet delay in the network. Finally, we provide a throughput-optimal policy for the network under QoS constraints when real-time and rate-guaranteed data traffic may be present.

We propose a Cooperative Opportunistic Automatic Repeat ReQuest (CoARQ) scheme to solve the HOL-blocking problem in infrastructure IEEE 802.11 WLANs. HOL blocking occurs when the head-of-the-line packet at the Access Point (AP) queue blocks the transmission of packets to other destinations resulting in severe throughput degradation. When the AP transmits a packet to a mobile station (STA), some of the nodes in the vicinity can overhear this packet transmission successfully. If the original transmission by the AP is unsuccessful, our CoARQ scheme chooses the station (STA or AP) with the best channel to the intended receiver as a relay and the chosen relay forwards the AP's packet to the receiver. This way, our scheme removes the bottleneck at the AP, thereby providing significant improvements in the throughput of the AP. We analyse the performance of our scheme in an infrastructure WLAN under a TCP controlled file download scenario and our analytical results are further validated by extensive simulations.

We consider nonparametric sequential hypothesis testing when the distribution under null hypothesis is fully known and the alternate hypothesis corresponds to some other unknown distribution. We use easily implementable universal lossless source codes to propose simple algorithms for such a setup. These algorithms are motivated from spectrum sensing application in Cognitive Radios. Universal sequential hypothesis testing using Lempel Ziv codes and Krichevsky-Trofimov estimator with Arithmetic Encoder are considered and compared for different distributions. Cooperative spectrum sensing with multiple Cognitive Radios using universal codes is also considered.

We consider a complex, additive, white Gaussian noise channel with flat fading. We study its diversity order vs transmission rate for some known power allocation schemes. The capacity region is divided into three regions. For one power allocation scheme, the diversity order is exponential throughout the capacity region. For selective channel inversion (SCI) scheme, the diversity order is exponential in low and high rate region but polynomial in mid rate region. For fast fading case we also provide a new upper bound on block error probability and a power allocation scheme that minimizes it. The diversity order behaviour of this scheme is same as for SCI but provides lower BER than the other policies.

We consider the problem of joint routing, scheduling and power control in a multihop wireless network when the nodes have multiple antennas. We focus on exploiting the multiple degrees-of-freedom available at each transmitter and receiver due to multiple antennas. Specifically we use multiple antennas at each node to form multiple access and broadcast links in the network rather than just point to point links. We show that such a generic transmission model improves the system performance significantly. Since the complexity of the resulting optimization problem is very high, we also develop efficient suboptimal solutions for joint routing, scheduling and power control in this setup.

This paper analyzes the error exponents in Bayesian decentralized spectrum sensing, i.e., the detection of occupancy of the primary spectrum by a cognitive radio, with probability of error as the performance metric. At the individual sensors, the error exponents of a Central Limit Theorem (CLT) based detection scheme are analyzed. At the fusion center, a K-out-of-N rule is employed to arrive at the overall decision. It is shown that, in the presence of fading, for a fixed number of sensors, the error exponents with respect to the number of observations at both the individual sensors as well as at the fusion center are zero. This motivates the development of the error exponent with a certain probability as a novel metric that can be used to compare different detection schemes in the presence of fading. The metric is useful, for example, in answering the question of whether to sense for a pilot tone in a narrow band (and suffer Rayleigh fading) or to sense the entire wide-band signal (and suffer log-normal shadowing), in terms of the error exponent performance. The error exponents with a certain probability at both the individual sensors and at the fusion center are derived, with both Rayleigh as well as log-normal shadow fading. Numerical results are used to illustrate and provide a visual feel for the theoretical expressions obtained.

We develop a Markov model for a TCP CUBIC connection. Next we use it to obtain approximate expressions for throughput when there may be queuing in the network. Finally we provide the throughputs different TCP CUBIC and TCP NewReno connections obtain while sharing a channel when they may have different round trip delays and packet loss probabilities.

Network life time maximization is becoming an important design goal in wireless sensor networks. Energy harvesting has recently become a preferred choice for achieving this goal as it provides near perpetual operation. We study such a sensor node with an energy harvesting source and compare various architectures by which the harvested energy is used. We find its Shannon capacity when it is transmitting its observations over a fading AWGN channel with perfect/no channel state information provided at the transmitter. We obtain an achievable rate when there are inefficiencies in energy storage and the capacity when energy is spent in activities other than transmission.

Sensor nodes with energy harvesting sources are gaining popularity due to their ability to improve the network life time and are becoming a preferred choice supporting `green communication'. We study such a sensor node with an energy harvesting source and compare various architectures by which the harvested energy is used. We find its Shannon capacity when it is transmitting its observations over an AWGN channel and show that the capacity achieving energy management policies are related to the throughput optimal policies. We also obtain the capacity when energy conserving sleep-wake modes are supported and an achievable rate for the system with inefficiencies in energy storage.

This paper considers cooperative spectrum sensing in Cognitive Radios. In our previous work we have developed DualSPRT, a distributed algorithm for cooperative spectrum sensing using Sequential Probability Ratio Test (SPRT) at the Cognitive Radios as well as at the fusion center. This algorithm works well, but is not optimal. In this paper we propose an improved algorithm- SPRT-CSPRT, which is motivated from Cumulative Sum Procedures (CUSUM). We analyse it theoretically. We also modify this algorithm to handle uncertainties in SNR's and fading.

We propose an energy efficient distributed cooperative Change Detection scheme called DualCUSUM based on Page's CUSUM algorithm. In the algorithm, each sensor runs a CUSUM and transmits only when the CUSUM is above some threshold. The transmissions from the sensors are fused at the physical layer. The channel is modeled as a Multiple Access Channel (MAC) corrupted with noise. The fusion center performs another CUSUM to detect the change. The algorithm performs better than several existing schemes when energy is at a premium. We generalize the algorithm to also include nonparametric CUSUM and provide a unified analysis. Our results show that while the false alarm probability is smaller for observation distribution with a lighter tail, the detection delay is asymptotically the same for any distribution. Consequently, we provide a new viewpoint on why parametric CUSUM performs better than nonparametric CUSUM. In the process, we also develop new results on a reflected random walk which can be of independent interest.

In a cooperative relay-assisted communication system that uses rateless codes, packets get transmitted from a source to a destination at a rate that depends on instantaneous channel states of the wireless links between nodes. When multiple relays are present, the relay with the highest channel gain to the source is the first to successfully decode a packet from the source and forward it to the destination. Thus, the unique properties of rateless codes ensure that both rate adaptation and relay selection occur without the transmitting source or relays acquiring instantaneous channel knowledge. In this paper, we show that in such cooperative systems, buffering packets at relays significantly increases throughput. We develop a novel analysis of these systems that combines the communication-theoretic aspects of cooperation over fading channels with the queuing-theoretic aspects associated with buffering. Closed-form expressions are derived for the throughput and end-to-end delay for the general case in which the channels between various nodes are not statistically identical. Corresponding results are also derived for benchmark systems that either do not exploit spatial diversity or do not buffer packets. Altogether, our results show that buffering - a capability that will be commonly available in practical deployments of relays - amplifies the benefits of cooperation.

Relay selection combined with buffering of packets of relays can substantially increase the throughput of a cooperative network that uses rateless codes. However, buffering also increases the end-to-end delays due to the additional queuing delays at the relay nodes. In this paper we propose a novel method that exploits a unique property of rateless codes that enables a receiver to decode a packet from non-contiguous and unordered portions of the received signal. In it, each relay, depending on its queue length, ignores its received coded bits with a given probability. We show that this substantially reduces the end-to-end delays while retaining almost all of the throughput gain achieved by buffering. In effect, the method increases the odds that the packet is first decoded by a relay with a smaller queue. Thus, the queuing load is balanced across the relays and traded off with transmission times. We derive explicit necessary and sufficient conditions for the stability of this system when the various channels undergo fading. Despite encountering analytically intractable G/GI/1 queues in our system, we also gain insights about the method by analyzing a similar system with a simpler model for the relay-to-destination transmission times.

This paper considers the problem of spectrum sensing in cognitive radio networks when the primary user is using Orthogonal Frequency Division Multiplexing (OFDM). For this we develop cooperative sequential detection algorithms that use the autocorrelation property of cyclic prefix (CP) used in OFDM systems. We study the effect of timing and frequency offset, IQ-imbalance and uncertainty in noise and transmit power. We also modify the detector to mitigate the effects of these impairments. The performance of the proposed algorithms is studied via simulations. We show that sequential detection can significantly improve the performance over a fixed sample size detector.

Most theoretical research on routing games in telecommunication networks has so far dealt with reciprocal congestion effects between routed entities. Yet in networks that support differentiation between flows, the congestion experienced by a packet depends on its priority level. Another differentiation is made by compressing the packets in the low priority flow while leaving the high priority flow intact. In this paper we study such kind of routing scenarios for the case of non-atomic users and we establish conditions for the existence and uniqueness of equilibrium.

We study a sensor node with an energy harvesting source. The generated energy can be stored in a buffer. The sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time. We obtain energy management policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue. We also compare performance of several easily implementable sub-optimal energy management policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay.

In this paper we provide sufficient conditions for lossy transmission of functions of correlated data over a multiple access channel (MAC). The conditions obtained can be shown as generalized version of Yamamoto's result [28]. We also obtain efficient joint source-channel coding schemes for transmission of discrete and continuous alphabet sources to recover the function values.

In this paper we address the problem of transmission of correlated sources over a fast fading multiple access channel (MAC) with partial channel state information available at both the encoders and the decoder. We provide sufficient conditions for transmission with given distortions. Next these conditions are specialized to a Gaussian MAC (GMAC). We provide the optimal power allocation strategy and compare the strategy with various levels of channel state information.

We study a sensor node with an energy harvesting source. In any slot, the sensor node is in one of two modes: Wake or Sleep. The generated energy is stored in a buffer. The sensor node senses a random field and generates a packet when it is awake. These packets are stored in a queue and transmitted in the wake mode using the energy available in the energy buffer. We obtain energy management policies which minimize a linear combination of the mean queue length and the mean data loss rate. Then, we obtain two easily implementable suboptimal policies and compare their performance to that of the optimal policy. Next, we extend the Throughput Optimal policy developed in our previous work to sensors with two modes. Via this policy, we can increase the throughput substantially and stabilize the data queue by allowing the node to sleep in some slots and to drop some generated packets. This policy requires minimal statistical knowledge of the system. We also modify this policy to decrease the switching costs.

An energy efficient distributed Change Detection scheme based on Page's CUSUM algorithm was presented in [2]. In this paper we consider a nonparametric version of this algorithm. In the algorithm in [2], each sensor runs CUSUM and transmits only when the CUSUM is above some threshold. The transmissions from the sensors are fused at the physical layer. The channel is modeled as a Multiple Access Channel (MAC) corrupted with noise. The fusion center performs another CUSUM to detect the change. In this paper, we generalize the algorithm to also include nonparametric CUSUM and provide a unified analysis.

We address the problem of estimating a random field via a wireless sensor network. We use a Multiple Access Channel (MAC) as the basic building block for such a network. For Gaussian sources over Gaussian MACs, we show that Amplify and Forward scheme (AF) performs well in such sensor network scenarios where the battery power is at a premium. We then extend this result to the hierarchical network scenario and show that it can perform favourably to the Slepian-Wolf based source coding and independent channel coding scheme. Since AF is simple and scalable, a good performance makes it an attractive scheme.

Cooperative communication using rateless codes, in which the source transmits an infinite number of parity bits to the destination until the receipt of an acknowledgment, has recently attracted considerable interest. It provides a natural and efficient mechanism for accumulating mutual information from multiple transmitting relays. We develop an analysis of queued cooperative relay systems that combines the communication-theoretic transmission aspects of cooperative communication using rateless codes over Rayleigh fading channels with the queuing-theoretic aspects associated with buffering messages at the relays. Relay cooperation combined with queuing reduces the message transmission times and also helps distribute the traffic load in the network, which improves throughput significantly.

We propose a simple and energy efficient distributed change detection scheme for sensor networks based on Page's parametric CUSUM algorithm. The sensor observations are IID over time and across the sensors conditioned on the change variable. Each sensor runs CUSUM and transmits only when the CUSUM is above some threshold. The transmissions from the sensors are fused at the physical layer. The channel is modeled as a multiple access channel (MAC) corrupted with IID noise. The fusion center which is the global decision maker, performs another CUSUM to detect the change. We provide the analysis and simulation results for our scheme and compare the performance with an existing scheme which ensures energy efficiency via optimal power selection.

We consider the problem of transmission of several discrete sources over a multiple access channel (MAC) with side information at the sources and the decoder. Source-channel separation does not hold for this channel. Sufficient conditions are provided for transmission of sources with a given distortion. The channel could have continuous alphabets (Gaussian MAC is a special case). Various previous results are obtained as special cases.

We compute the throughput obtained by a TCP connection in a UMTS environment. For downloading data at a mobile terminal, the packets of each TCP connection are stored in separate queues at the base station (node B). Also due to fragmentation of the TCP packets into Protocol Data Units (PDU) and link layer retransmissions of PDUs there can be significant delays at the queue of the node B. In such a scenario the existing models of TCP may not be sufficient. Thus, we provide a new approximate TCP model and also obtain new closed-form expressions of mean window size. Using these we obtain the throughput of a TCP connection which matches with simulations quite well.

We consider the problem of centralized routing and scheduling for IEEE 802.16 mesh networks so as to provide Quality of Service (QoS) to individual real and interactive data applications. We first obtain an optimal and fair routing and scheduling policy for aggregate demands for different source- destination pairs. We then present scheduling algorithms which provide per flow QoS guarantees while utilizing the network resources efficiently. Our algorithms are also scalable: they do not require per flow processing and queueing and the computational requirements are modest. We have verified our algorithms via extensive simulations.

We consider the problem of several users transmitting packets to a base station, and study an optimal scheduling formulation involving three communication layers, namely, the medium access control, link, and physical layers. We assume Markov models for the packet arrival processes and the channel gain processes. Perfect channel state information is assumed to be available at the transmitter and the receiver. The transmissions are subject to a long-run average transmitter power constraint. The control problem is to assign power and rate dynamically as a function of the fading and the queue lengths so as to minimize a weighted sum of long run average packet transmission delays.

In this paper, we analyze optimal (in space and time) adaptive power transmission policies for fading channels when the channel-state information (CSI) at the transmitter (CSIT) and the receiver (CSIR) is available. The transmitter has a long-term (time) average power constraint. There can be multiple antennas at the transmitter and at the receiver. The channel experiences Rayleigh fading. We consider beamforming and space-time coded systems with perfect/imperfect CSIT and CSIR. The performance measure is the bit error rate (BER). We show that in both coded and uncoded systems, our power allocation policy provides exponential diversity order if perfect CSIT is available. We also show that, if the quality of CSIT degrades then the exponential diversity is retained in the low SNR region but we get only polynomial diversity in the high SNR region. Another interesting conclusion is that in case of imperfect CSIT and CSIR, knowledge of CSIT at the receiver is very important. Finally, for the optimal power control policy of the uncoded system we find the error-exponents which provide the rate versus diversity-order tradeoff for this policy. This tradeoff is of an entirely different nature than the well-known Zheng-Tse tradeoff.

In this paper we develop and numerically explore the modeling heuristic of using saturation attempt probabilities as state dependent attempt probabilities in an IEEE 802.11e infrastructure network carrying packet telephone calls and TCP controlled file downloads, using enhanced distributed channel access (EDCA). We build upon the fixed point analysis and performance insights. When there are a certain number of nodes of each class contending for the channel (i.e., have nonempty queues), then their attempt probabilities are taken to be those obtained from saturation analysis for that number of nodes. Then we model the system queue dynamics at the network nodes. With the proposed heuristic, the system evolution at channel slot boundaries becomes a Markov renewal process, and regenerative analysis yields the desired performance measures. The results obtained from this approach match well with ns2 simulations. We find that, with the default IEEE 802.11e EDCA parameters for AC 1 and AC 3, the voice call capacity decreases if even one file download is initiated by some station. Subsequently, reducing the voice calls increases the file download capacity almost linearly (by 1/3 Mbps per voice call for the 11 Mbps PHY)

IEEE 802.16 standards for wireless metropolitan area networks (WMANs) include a mesh mode of operation for improving the coverage and throughput of the network. In this paper, we consider the problem of routing and centralized scheduling for such networks. We first fix the routing, which reduces the network to a tree. We then present a finite horizon dynamic programming framework. Using it we obtain various scheduling algorithms depending upon the cost function. Next we consider simpler suboptimal algorithms and compare their performances

We develop new scheduling algorithms for the IEEE 802.16d OFDMA/TDD based broadband wireless access system, in which radio resources of both time and frequency slots are dynamically shared by all users. Our objective is to provide a fair and efficient allocation to all the users to satisfy their quality of service

In a typical sensor network scenario a goal is to monitor a spatio-temporal process through a number of inexpensive sensing nodes, the key parameter being the fidelity at which the process has to be estimated at distant locations. We study such a scenario in which multiple encoders transmit their correlated data at finite rates to a distant, common decoder over a discrete time multiple access channel under various side information assumptions. In particular, we derive an achievable rate region for this communication problem

Semi blind/blind equalizers are believed to work unsatisfactorily in fading MIMO channels compared to training based methods, due to slow convergence or high computational complexity. We revisit this issue. Defining a 'composite' channel for each equalizer, we compare the three algorithms based on the capacity of this channel. We show that, in a Rician (with line of sight, LOS) environment, semi blind/blind algorithms outperform training equalizers, but in Rayleigh channels, it is better to use training based methods. We also find the optimum training size in training and semi blind methods.

We consider an optimal power and rate scheduling problem for a multiaccess fading wireless channel with the objective of minimising a weighted sum of mean packet transmission delay subject to a peak power constraint. The base station acts as a controller which, depending upon the buffer lengths and the channel state of each user, allocates transmission rate and power to individual users. We assume perfect channel state information at the transmitter and the receiver. We also assume a Markov model for the fading and packet arrival processes. The policy obtained represents a form of indexability

We analyze optimum (space-time) adaptive power transmission policies for Rayleigh fading MIMO channels when CSIT and CSIR are available. We show that our power allocation policy provides exponential diversity gain/sup 2/ (BER/spl les//spl alpha/e/sup -f(n(t),n(r))/ where /spl alpha/>0 is a constant, and f>0 is an increasing function of n/sub t/&n/sub r/) if perfect CSIT is available. Exponential diversity is lost at high SNR if the quality of CSIT degrades.

We consider a problem of optimal source-channel coding rate pair allocation over a wireless link based on the channel state and the queue length at the transmitter. We show that by this scheme, one can gain substantially in performance as compared to only source or channel rate optimization

We obtain the convergence of the Godard family (including SATO and CM algorithms) in a unified way. Our assumptions are quite realistic: the channel input can be asymptotically stationary and ergodic, the channel impulse response is finite and can be stationary, ergodic (this models fading channels) and the equalizer length is finite. The noise is i.i.d. The channel input can be discrete or continuous. Our approach allows us to approximate the whole trajectory of the equalizer coefficients. This provides estimates of the rate of convergence and the system performance (symbol error rate) can be evaluated under transience and steady state.

An optimal power and rate scheduling problem for a single user transmitting to a base station on a fading wireless link with the objective of minimizing the mean delay subject to an average power constraint is considered. The base station acts as a controller which, depending upon the transmitter buffer lengths and the signal power to interference ratio (SIR) on the uplink pilot channel, allocates transmission rate and power to the user. We provide structural results for an average cost optimal stationary policy under a long run average transmitter power constraint. We obtain a closed form expression relating the optimal policy when the SIR is the best, to the optimal policy for any other SIR value. We also obtain lower and upper bounds for the optimal policy.

We consider the problem of optimal power allocation to a user transmitting on a fading channel. The our Is using some specific, convolutional or turbo code. The cost function to be minimized is the bit error rate. The user needs to satisfy an average power constraint We And that the optimal power policy is code and fading distribution dependent and can be significantly different from the commonly considered policies e.g, water filling and (truncated) channel inversion. The gain In BER can be substantial.

We consider an optimal power and rate scheduling problem for a single user transmitting to a base station on a fading wireless link with the objective of providing transmission delay guarantees. The base station acts as a controller which, depending upon the transmitter buffer lengths and the signal power to interference ratio (SIR) on the uplink pilot channel, allocates transmission rate and power to the user. We provide structural results for an average cost optimal policy under a long run average transmitter power constraint. We obtain a closed form expression relating the optimal policy when the SIR is the best, to the optimal policy for any other SIR value.

Consider a finite state, irreducible Markov channel {S/sub k/}. At time k the channel output Y /sub k/ depends only on the channel input X/sub k/ and the channel state S/sub k/. Let U/sub k/ and V/sub k/ be the CSIT (channel state information at transmitter) and the CSIR (CSI at receiver) respectively. In this paper we provide algorithms to compute the capacity of the Markov channel under various assumptions on CSIT and CSIR where the assumption P(S /sub k/ = s|U/sub 1//sup k/) = P(S/sub k/|U/sub k/) is not necessarily satisfied. Our algorithms are based on the extension of ideas developed for channels with feedback.

In this paper, we analyze the performance of battery power management schemes in wireless mobile devices using a queueing theory approach. We model the battery as a server with finite service capacity and data packets as customers to be served. With an intent to exploit the recharging capability of the battery when left idle, we allow the battery to go on intentional vacations during which the battery can recharge itself. The recharge thus built up can effectively increase the number of customers served (in other words, battery life can be extended). Such improved battery life performance would, however, come at the expense of increased packet delay performance. We quantify the battery life gain versus delay performance trade-off in this approach through analysis and simulations. By considering a continuous recharge model of the battery, we derive expressions for the number of customers served and the mean delay for an M/GI/1 queueing system without and with server vacations. We show that allowing intentional vacations during busy periods helps to increase battery life, and that this approach can be beneficial when applied to the traffic of delay-tolerant applications. We also propose a packet delay constrained power saving algorithm that will exploit the recharge phenomenon when packet delay constraints are imposed.

We study the stability and performance of a system involving several TCP connections passing through a tandem of RED controlled queues each of which has an incoming exogenous stream. The exogenous stream, which represents the superposition of all incoming UDP connections into a queue, has been modeled as an MMPP stream. We consider both the TCP Tahoe and the TCP Reno versions. We provide the conditions for stability of the system and closed form expressions for the throughput of the TCP connections and the mean sojourn times of the TCP and the exogenous streams. A decomposition approach has been used to simplify computations. These results have implications for bandwidth reservation and providing QoS in the Internet.

We develop a scheme which can help provide certain QoS via active measurement techniques. We send a probing stream, using TCP or UDP, on a path with one bottleneck router. Several TCP and UDP connections (cross traffic) may be sharing the link on the router. We make measurements of the RTT (round trip time) and the throughput of the probing stream and do not assume any knowledge of the cross traffic. Then we obtain estimates of the mean delay and the throughput a new TCP or UDP stream will obtain through that router. We also obtain the total traffic intensity of the UDP streams and the total work load in the queue by the TCP connections in the cross traffic. The router may or may not employ RED (random early detection) control. We find that using TCP for a probing stream provides better estimates than using UDP.