In wireless channels and many other channels, interference is a central phenomenon. Mitigating interference is a key to improving system performance. To find the limit of the achievable rates for these channels in the presence of interference, the two-user Gaussian interference channel has been the subject of intensive study in network information theory. However, most current results have been obtained by assuming Gaussian input distributions. While optimal in single-user Gaussian channels, the issue with this assumption is that the Gaussian noise becomes the worst noise when the input distribution is also Gaussian. In this thesis, we propose a class of discrete constellations. We show that this class of constellations can automatically achieve the same sum rates as schemes that treat interference as noise or perform time sharing.

In wireless channels and many other channels, interference is a central phenomenon. Mitigating interference is a key to improving system performance. To find the limit of the achievable rates for these channels in the presence of interference, the two-user Gaussian interference channel has been the subject of intensive study in network information theory. However, most current results have been obtained by assuming Gaussian input distributions. While optimal in single-user Gaussian channels, the issue with this assumption is that the Gaussian noise becomes the worst noise when the input distribution is also Gaussian. In this thesis, we propose a class of discrete constellations. We show that this class of constellations can automatically achieve the same sum rates as schemes that treat interference as noise or perform time sharing.

The implications of non-Gaussian interference for various communication systemsare explored. The focus is on the Kappa distribution, Generalized Gaussian distribu-tions, and the distribution of the interference in molecular communication systems.A review of how dynamic systems that are not in equilibrium are modeled by theKappa distribution and how this distribution models interference in HF communica-tions systems at sunrise is provided. The channel model, bit error rate for single andmultiple antennas, channel capacity, and polar code performance are shown.<div><br><div>Next, a review of the Generalized Gaussian distribution that has been found tomodel the interference resulting from surface activities is provided. This modeling isextended to find the secrecy capacity so that information cannot be obtained by theeavesdropper.</div><div><br></div><div>Finally, future nanomachnines are examined. The vulnerability to a receptorantagonist of a ligand-based molecule receiver is explored. These effects are consideredto be interference as in other wireless systems and the damage to signal reception isquantified.</div></div>

Dans cette thèse, le canal Gaussien à interférence à deux utilisateurs avec voie de retour dégradée par un bruit additif (GIC-NOF) est étudié sous deux perspectives : les réseaux centralisés et décentralisés. Du point de vue des réseaux centralisés, les limites fondamentales du GIC-NOF sont caractérisées par la région de capacité. L’une des principales contributions de cette thèse est une approximation à un nombre constant de bits près de la région de capacité du GIC-NOF. Ce résultat est obtenu grâce à l’analyse d’un modèle de canal plus simple, le canal linéaire déterministe à interférence à deux utilisateurs avec voie de retour dégradée par un bruit additif (LDIC-NOF). L’analyse pour obtenir la région de capacité du LDIC-NOF fournit les idées principales pour l’analyse du GIC-NOF. Du point de vue des réseaux décentralisés, les limites fondamentales du GIC-NOF sont caractérisées par la région d’η-équilibre de Nash (η-EN). Une autre contribution de cette thèse est une approximation de la région η-EN du GIC-NOF, avec η > 1. Comme dans le cas centralisé, le cas décentralisé LDIC-NOF (D-LDIC-NOF) est étudié en premier et les observations sont appliquées dans le cas décentralisé GIC-NOF (D-GIC-NOF). La contribution finale de cette thèse répond à la question suivante : “À quelles conditions la voie de retour permet d’agrandir la région de capacité, la région η-EN du GIC-NOF ou du D-GIC-NOF ? ”. La réponse obtenue est de la forme : L’implémentation de la voie de retour de la sortie du canal dans l’émetteur-récepteur i agrandit la région de capacité ou la région η-EN si le rapport signal sur bruit de la voie de retour est supérieure à SNRi* , avec i ∈ {1, 2}. La valeur approximative de SNRi* est une fonction de tous les autres paramètres du GIC-NOF ou du D-GIC-NOF. / In this thesis, the two-user Gaussian interference channel with noisy channel-output feedback (GIC-NOF) is studied from two perspectives: centralized and decentralized networks. From the perspective of centralized networks, the fundamental limits of the two-user GICNOF are characterized by the capacity region. One of the main contributions of this thesis is an approximation to within a constant number of bits of the capacity region of the two-user GIC-NOF. This result is obtained through the analysis of a simpler channel model, i.e., a two-user linear deterministic interference channel with noisy channel-output feedback (LDIC-NOF). The analysis to obtain the capacity region of the two-user LDIC-NOF provides the main insights required to analyze the two-user GIC-NOF. From the perspective of decentralized networks, the fundamental limits of the two-user decentralized GIC-NOF (D-GIC-NOF) are characterized by the η-Nash equilibrium (η-NE) region. Another contribution of this thesis is an approximation of the η-NE region of the two-user GIC-NOF, with η> 1. As in the centralized case, the two-user decentralized LDIC-NOF (D-LDIC-NOF) is studied first and the lessons learnt are applied in the two-user D-GIC-NOF. The final contribution of this thesis consists in a closed-form answer to the question: “When does channel-output feedback enlarge the capacity or η-NE regions of the two-user GIC-NOF or two-user D-GIC-NOF?”. This answer is of the form: Implementing channel-output feedback in transmitter-receiver i enlarges the capacity or η-NE regions if the feedback SNR is beyond SNRi* , with i ∈ {1, 2}. The approximate value of SNRi* is shown to be a function of all the other parameters of the two-user GIC-NOF or two-user D-GIC-NOF.

The security of information is an indispensable element of a communication system when transmitted signals are vulnerable to eavesdropping. This issue is a challenging problem in a wireless network as propagated signals can be easily captured by unauthorized receivers, and so achieving a perfectly secure communication is a desire in such a wiretap channel. On the other hand, cryptographic algorithms usually lack to attain this goal due to the following restrictive assumptions made for their design. First, wiretappers basically have limited computational power and time. Second, each authorized party has often access to a reasonably large sequence of uniform random bits concealed from wiretappers. To guarantee the security of information, Information Theory (IT) offers the following two approaches based on physical-layer security. First, IT suggests using wiretap (block) codes to securely and reliably transmit messages over a noisy wiretap channel. No confidential common key is usually required for the wiretap codes. The secrecy problem investigates an optimum wiretap code that achieves the secrecy capacity of a given wiretap channel. Second, IT introduces key agreement (block) codes to exchange keys between legitimate parties over a wiretap model. The agreed keys are to be reliable, secure, and (uniformly) random, at least in an asymptotic sense, such that they can be finally employed in symmetric key cryptography for data transmission. The key agreement problem investigates an optimum key agreement code that obtains the key capacity of a given wiretap model. In this thesis, we study the key agreement problem for two wiretap models: a Discrete Memoryless (DM) model and a Gaussian model. Each model consists of a wiretap channel paralleled with an authenticated public channel. The wiretap channel is from a transmitter, called Alice, to an authorized receiver, called Bob, and to a wiretapper, called Eve. The Probability Transition Function (PTF) of the wiretap channel is controlled by a random sequence of Channel State Information (CSI), which is assumed to be non-causally available at Alice. The capacity of the public channel is C_P₁∈[0,∞) in the forward direction from Alice to Bob and C_P₂∈[0,∞) in the backward direction from Bob to Alice. For each model, the key capacity as a function of the pair (C_P₁, C_P₂) is denoted by C_K(C_P₁, C_P₂). We investigate the forward key capacity of each model, i.e., C_K(C_P₁, 0) in this thesis. We also study the key generation over the Gaussian model when Eve's channel is less noisy than Bob's. In the DM model, the wiretap channel is a Discrete Memoryless State-dependent Wiretap Channel (DM-SWC) in which Bob and Eve each may also have access to a sequence of Side Information (SI) dependent on the CSI. We establish a Lower Bound (LB) and an Upper Bound (UB) on the forward key capacity of the DM model. When the model is less noisy in Bob's favor, another UB on the forward key capacity is derived. The achievable key agreement code is asymptotically optimum as C_P₁→ ∞. For any given DM model, there also exists a finite capacity C⁰_P₁, which is determined by the DM-SWC, such that the forward key capacity is achievable if C_P₁≥ C⁰_P₁. Moreover, the key generation is saturated at capacity C_P₁= C⁰_P₁, and thus increasing the public channel capacity beyond C⁰_P₁ makes no improvement on the forward key capacity of the DM model. If the CSI is fully known at Bob in addition to Alice, C⁰_P₁=0, and so the public channel has no contribution in key generation when the public channel is in the forward direction. The achievable key agreement code of the DM model exploits both a random generator and the CSI as resources for key generation at Alice. The randomness property of channel states can be employed for key generation, and so the agreed keys depend on the CSI in general. However, a message is independent of the CSI in a secrecy problem. Hence, we justify that the forward key capacity can exceed both the main channel capacity and the secrecy capacity of the DM-SWC. In the Gaussian model, the wiretap channel is a Gaussian State-dependent Wiretap Channel (G-SWC) with Additive White Gaussian Interference (AWGI) having average power Λ. For simplicity, no side information is assumed at Bob and Eve. Bob's channel and Eve's channel suffer from Additive White Gaussian Noise (AWGN), where the correlation coefficient between noise of Bob's channel and that of Eve's channel is given by ϱ. We prove that the forward key capacity of the Gaussian model is independent of ϱ. Moreover, we establish that the forward key capacity is positive unless Eve's channel is less noisy than Bob's. We also prove that the key capacity of the Gaussian model vanishes if the G-SWC is physically degraded in Eve's favor. However, we justify that obtaining a positive key capacity is feasible even if Eve's channel is less noisy than Bob's according to our achieved LB on the key capacity for case (C_P₁, C_P₂)→ (∞, ∞). Hence, the key capacity of the Gaussian model is a function of ϱ. In this thesis, an LB on the forward key capacity of the Gaussian model is achieved. For a fixed Λ, the achievable key agreement code is optimum for any C_P₁∈[0,∞) in both low Signal-to-Interference Ratio (SIR) and high SIR regimes. We show that the forward key capacity is asymptotically independent of C_P₁ and Λ as the SIR goes to infinity, and thus the public channel and the interference have negligible contributions in key generation in the high SIR regime. On the other hand, the forward key capacity is a function of C_P₁ and Λ in the low SIR regime. Contributions of the interference and the public channel in key generation are significant in the low SIR regime that will be illustrated by simulations. The proposed key agreement code asymptotically achieves the forward key capacity of the Gaussian model for any SIR as C_P₁→ ∞. Hence, C_K(∞,0) is calculated, and it is suggested as a UB on C_K(C_P₁,0). Using simulations, we also compute the minimum required C_P₁ for which the forward key capacity is upper bounded within a given tolerance. The achievable key agreement code is designed based on a generalized version of the Dirty Paper Coding (DPC) in which transmitted signals are correlated with the CSI. The correlation coefficient is to be determined by C_P₁. In contrast to the DM model, the LB on the forward key capacity of a Gaussian model is a strictly increasing function of C_P₁ according to our simulations. This fact is an essential difference between this model and the DM model. For C_P₁=0 and a fixed Λ, the forward key capacity of the Gaussian model exceeds the main channel capacity of the G-SWC in the low SIR regime. By simulations, we show that the interference enhances key generation in the low SIR regime. In this regime, we also justify that the positive effect of the interference on the (forward) key capacity is generally more than its positive effect on the secrecy capacity of the G-SWC, while the interference has no influence on the main channel capacity of the G-SWC.

Nanoscale communication is appealing domain in nanotechnology. There are many existing nanoscale communication methods. In addition to these, novel techniques can be derived depending on the naturally existing phenomena such as molecular communication. It uses molecules as an information carrier such as molecular motors, pheromones and neurotransmitters for neuro-spike communication. Among them, neuro-spike communication is a vastly unexplored area. The ultimate goal of this thesis is to accurately investigate it by obtaining a realistic physical channel model. This model can be exploited in different disciplines. Furthermore, the model can help designing novel artificial nanoscale communication paradigms. The modeled channel is analyzed regarding the error probability of detecting spikes depending on channel parameters. Moreover, channel delay is characterized and information theoretical analysis of packet release mechanism in the channel is performed. The modeled channel is extended to multi-input single output terminal. In this case, input neurons can simultaneously send information through the same synapse leading to interference. However, there is an interference repressing technique in these synapses called automatic gain control. It decreases the interference level observed on weaker signal. The first aim for this case is to define the interference channel at synapse having automatic gain control. The second aim is to analyze the achievable rate region of this channel. The analysis shows that gain control mechanism prevents the decrease in achievable rate region because of the weaker signal. Moreover, power, firing rate and number of stronger inputs do not affect the achievable rate region.

Η διερεύνηση της περιοχής χωρητικότητας και της περιοχής επιτεύξιμων ρυθμών μετάδοσης καναλιών αποτελεί βασικό αντικείμενο της Θεωρίας Πληροφορίας. Η Ευθυγράμμιση Παρεμβολών είναι μια καινούρια ιδέα που δίνει μια εναλλακτική οπτική στο αντικείμενο αυτό, μέσω της διαφορετικής λογικής που εισάγει σχετικά με την κωδικοποίηση και τη μετάδοση της πληροφορίας. Σε πρόσφατες δημοσιεύσεις έχουν προταθεί μοντέλα που επιτρέπουν την εφαρμογή της θεωρίας της Ευθυγράμμισης Παρεμβολών και τη χρήση της σε πρακτικά συστήματα επικοινωνιών και καταδεικνύουν την υπεροχή της σε σχέση με συμβατικές μεθόδους. Παράλληλα, παρόλο που προς το παρόν έχει δοθεί έμφαση στην εδραίωση της Ευθυγράμμισης Παρεμβολών στις επικοινωνίες, η μαθηματική της βάση καθιστά δυνατή την εφαρμογή της σε αντικείμενα που ανήκουν σε άλλους τομείς. Σκοπός της παρούσας εργασίας είναι η μελέτη και η εφαρμογή της Αναδρομικής Ευθυγράμμισης Παρεμβολών για μετάδοση πληροφορίας σε Συστήματα Επικοινωνιών, καθώς και η διερεύνηση της απόδοσης της μεθόδου σε πρακτικά συστήματα. / The study of the channel capacity region and the achievable rate region is one of the main topics of Information Theory. Interference Alignment is a new idea that provides new insights through the introduction of a different viewpoint on data encoding and transmission. In recent publications, models have been proposed that allow the application of the theory of Interference Alignment to practical communication systems and demonstrate its superiority compared to traditional approaches. Furthermore, although for the time being emphasis has been put on establishing the use of Interference Alignment to communication systems, its mathematical formulation makes possible its use to other areas. The purpose of this thesis is to study and to apply Retrospective Interference Alignment to data transmission in communication systems and to evaluate the performance of the method in practical systems.

The security of information is an indispensable element of a communication system when transmitted signals are vulnerable to eavesdropping. This issue is a challenging problem in a wireless network as propagated signals can be easily captured by unauthorized receivers, and so achieving a perfectly secure communication is a desire in such a wiretap channel. On the other hand, cryptographic algorithms usually lack to attain this goal due to the following restrictive assumptions made for their design. First, wiretappers basically have limited computational power and time. Second, each authorized party has often access to a reasonably large sequence of uniform random bits concealed from wiretappers. To guarantee the security of information, Information Theory (IT) offers the following two approaches based on physical-layer security. First, IT suggests using wiretap (block) codes to securely and reliably transmit messages over a noisy wiretap channel. No confidential common key is usually required for the wiretap codes. The secrecy problem investigates an optimum wiretap code that achieves the secrecy capacity of a given wiretap channel. Second, IT introduces key agreement (block) codes to exchange keys between legitimate parties over a wiretap model. The agreed keys are to be reliable, secure, and (uniformly) random, at least in an asymptotic sense, such that they can be finally employed in symmetric key cryptography for data transmission. The key agreement problem investigates an optimum key agreement code that obtains the key capacity of a given wiretap model. In this thesis, we study the key agreement problem for two wiretap models: a Discrete Memoryless (DM) model and a Gaussian model. Each model consists of a wiretap channel paralleled with an authenticated public channel. The wiretap channel is from a transmitter, called Alice, to an authorized receiver, called Bob, and to a wiretapper, called Eve. The Probability Transition Function (PTF) of the wiretap channel is controlled by a random sequence of Channel State Information (CSI), which is assumed to be non-causally available at Alice. The capacity of the public channel is C_P₁∈[0,∞) in the forward direction from Alice to Bob and C_P₂∈[0,∞) in the backward direction from Bob to Alice. For each model, the key capacity as a function of the pair (C_P₁, C_P₂) is denoted by C_K(C_P₁, C_P₂). We investigate the forward key capacity of each model, i.e., C_K(C_P₁, 0) in this thesis. We also study the key generation over the Gaussian model when Eve's channel is less noisy than Bob's. In the DM model, the wiretap channel is a Discrete Memoryless State-dependent Wiretap Channel (DM-SWC) in which Bob and Eve each may also have access to a sequence of Side Information (SI) dependent on the CSI. We establish a Lower Bound (LB) and an Upper Bound (UB) on the forward key capacity of the DM model. When the model is less noisy in Bob's favor, another UB on the forward key capacity is derived. The achievable key agreement code is asymptotically optimum as C_P₁→ ∞. For any given DM model, there also exists a finite capacity C⁰_P₁, which is determined by the DM-SWC, such that the forward key capacity is achievable if C_P₁≥ C⁰_P₁. Moreover, the key generation is saturated at capacity C_P₁= C⁰_P₁, and thus increasing the public channel capacity beyond C⁰_P₁ makes no improvement on the forward key capacity of the DM model. If the CSI is fully known at Bob in addition to Alice, C⁰_P₁=0, and so the public channel has no contribution in key generation when the public channel is in the forward direction. The achievable key agreement code of the DM model exploits both a random generator and the CSI as resources for key generation at Alice. The randomness property of channel states can be employed for key generation, and so the agreed keys depend on the CSI in general. However, a message is independent of the CSI in a secrecy problem. Hence, we justify that the forward key capacity can exceed both the main channel capacity and the secrecy capacity of the DM-SWC. In the Gaussian model, the wiretap channel is a Gaussian State-dependent Wiretap Channel (G-SWC) with Additive White Gaussian Interference (AWGI) having average power Λ. For simplicity, no side information is assumed at Bob and Eve. Bob's channel and Eve's channel suffer from Additive White Gaussian Noise (AWGN), where the correlation coefficient between noise of Bob's channel and that of Eve's channel is given by ϱ. We prove that the forward key capacity of the Gaussian model is independent of ϱ. Moreover, we establish that the forward key capacity is positive unless Eve's channel is less noisy than Bob's. We also prove that the key capacity of the Gaussian model vanishes if the G-SWC is physically degraded in Eve's favor. However, we justify that obtaining a positive key capacity is feasible even if Eve's channel is less noisy than Bob's according to our achieved LB on the key capacity for case (C_P₁, C_P₂)→ (∞, ∞). Hence, the key capacity of the Gaussian model is a function of ϱ. In this thesis, an LB on the forward key capacity of the Gaussian model is achieved. For a fixed Λ, the achievable key agreement code is optimum for any C_P₁∈[0,∞) in both low Signal-to-Interference Ratio (SIR) and high SIR regimes. We show that the forward key capacity is asymptotically independent of C_P₁ and Λ as the SIR goes to infinity, and thus the public channel and the interference have negligible contributions in key generation in the high SIR regime. On the other hand, the forward key capacity is a function of C_P₁ and Λ in the low SIR regime. Contributions of the interference and the public channel in key generation are significant in the low SIR regime that will be illustrated by simulations. The proposed key agreement code asymptotically achieves the forward key capacity of the Gaussian model for any SIR as C_P₁→ ∞. Hence, C_K(∞,0) is calculated, and it is suggested as a UB on C_K(C_P₁,0). Using simulations, we also compute the minimum required C_P₁ for which the forward key capacity is upper bounded within a given tolerance. The achievable key agreement code is designed based on a generalized version of the Dirty Paper Coding (DPC) in which transmitted signals are correlated with the CSI. The correlation coefficient is to be determined by C_P₁. In contrast to the DM model, the LB on the forward key capacity of a Gaussian model is a strictly increasing function of C_P₁ according to our simulations. This fact is an essential difference between this model and the DM model. For C_P₁=0 and a fixed Λ, the forward key capacity of the Gaussian model exceeds the main channel capacity of the G-SWC in the low SIR regime. By simulations, we show that the interference enhances key generation in the low SIR regime. In this regime, we also justify that the positive effect of the interference on the (forward) key capacity is generally more than its positive effect on the secrecy capacity of the G-SWC, while the interference has no influence on the main channel capacity of the G-SWC.

In multiuser wireless communications, interference not only limits the performance of the system, but also allows users to eavesdrop on other users’ messages. Hence, interference management in multiuser wireless communication has received significant attention in the last decade, both in the academia and industry. The interference channel (IC) is one of the simplest information theoretic models to analyze the effect of interference on the throughput and secrecy of individual messages in a multiuser setup. In this thesis, the IC is studied under different settings with and without the secrecy constraint. The main contributions of the thesis are as follows: • The generalized degrees of freedom (GDOF) has emerged as a useful approximate measure of the potential throughput of a multiuser wireless system. Also, multiple antennas at the transmitter and receiver can provide additional dimension for signaling, which can in turn improve the GDOF performance of the IC. In the initial part of the thesis, a K-user MIMO Gaussian IC (GIC) is studied from an achievable GDOF perspective. An inner bound on GDOF is derived using a combination of techniques such as treating interference as noise, zero-forcing receiving, interference alignment (IA), and extending the Han-Kobayashi (HK) scheme to K users. Also, outer bounds on the sum rate of the K-user MIMO GIC are derived, under different assumptions of cooperation and providing side information to the receivers. The derived outer bounds are simplified to obtain outer bounds on the GDOF. The relative performance of these bounds yields insight into the performance limits of the multiuser MIMO GIC and the relative merits of different schemes for interference management. • Then, the problem of designing the precoding and receive filtering matrices for IA is explored for K-user MIMO (M × N) GIC. Two algorithms for designing the precoding and receive filtering matrices for IA in the block fading or constant MIMO IC with a finite number of symbol extensions are proposed. The first algorithm for IA is based on aligning a subset of the interfering signal streams at each receiver. As the first algorithm requires global channel knowledge at each node, a distributed algorithm is proposed which requires only limited channel knowledge at each node. A new performance metric is proposed, that captures the possible loss in signal dimension while designing the precoders. The performance of the algorithms are evaluated by comparing them with existing algorithms for IA precoder design. • In the later part of the thesis, a 2-user IC with limited-rate transmitter cooperation is studied, to investigate the role of cooperation in managing interference and ensuring secrecy. First, the problem is studied in the deterministic setting, and achievable schemes are proposed, which use a combination of interference cancelation, relaying of the other user’s data bits, time sharing, and transmission of random bits, depending on the rate of the cooperative link and the relative strengths of the signal and the interference. Outer bounds on the secrecy rate are derived, under different assumptions of providing side information to receivers and partitioning the encoded message/output depending on the relative strength of the signal and the interference. The achievable schemes and outer bounds are extended to the Gaussian case. For example, while obtaining outer bounds, for the Gaussian case, it is not possible to partition the encoded message or output as performed in the deterministic case, and the novelty lies in finding the analogous quantities for the Gaussian case. The proposed achievable scheme for the Gaussian case uses a combination of cooperative and stochastic encoding along with dummy message transmission. For both the models, one of the key techniques used in the achievable scheme is interference cancelation, which has two benefits: it cancels interference and ensures secrecy simultaneously. The results show that limited-rate transmitter cooperation can greatly facilitate secure communications over 2-user ICs.

We consider a wireless communication system in which there are N transmitter-receiver pairs and each transmitter wants to communicate with its corresponding receiver. This is modelled as an interference channel. We propose power allocation algorithms for increasing the sum rate of two and three user interference channels. The channels experience fast fading and there is an average power constraint on each transmitter. In this case receivers use successive decoding under strong interference, instead of treating interference as noise all the time. Next, we u se game theoretic approach for power allocation where each receiver treats interference as noise. Each transmitter-receiver 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 crosslink channel gains. Next, we assume that each user knows channel gains of only the links that are incident on its receiver. Finally, we assume that each use r knows only its own direct link channel gain. In all cases, we formulate the problem of finding the Nash equilibrium(NE) as a variational in equality problem. For the game with complete channel knowledge, we present an algorithm to solve the VI and we provide weaker sufficient conditions for uniqueness of the NE than the sufficient conditions available in the literature. Later, we present a novel heuristic for solving the VI under general channel conditions. We also provide a distributed algorithm to compute Pare to optimal solutions for the proposed games. We use Bayesian learning that guarantees convergence to an Ɛ-Nash equilibrium for the incomplete information game with direct link channel gain knowledge only, that does not require knowledge of the power policies of other users but requires feedback of the interference power values from a receiver to its corresponding transmitter. Later, we consider a more practical scenario in which each transmitter transmits data at a certain rate using a power that depends on the channel gain to its receiver. If a receiver can successfully receive the message, it sends an acknowledgement(ACK), else it sends a negative ACK(NACK). Each user aims to maximize its probability of successful transmission. We formulate this problem as a stochastic game and propose a fully distributed learning algorithm to find a correlated equilibrium(CE). In addition, we use a no regret algorithm to find a coarse correlated equilibrium(CCE) for our power allocation game. We also propose a fully distributed learning algorithm to find a Pareto optimal solution. In general Pareto points do not guarantee fairness among the users. Therefore we also propose an algorithm to compute a Nash bargaining solution which is Pareto optimal and provides fairness among the users. Finally, we extend these results when each transmitter sends data at multiple rates rather than at a fixed rate.