In this paper, we study optimal decentralized power allocation strategies for multiple access channels with additive white generalized Gaussian noise (AWGGN). The generalized Gaussian (GG) noise can model a wide variety of noise distributions. The Gaussian and Laplacian distributions are special cases of the GG distribution. First, we provide tight lower and upper bounds on the sum-rate of the generalized Gaussian MAC (GG-MAC) with per user peak transmit power and average transmit power constraints. The problem of optimizing transmit power allocation which maximizes this lower bound on the sum rate is formulated as a convex optimization problem. Next, we prove that the greedy power allocation policy is asymptotically optimal as the number of transmitters increases. Here, the greedy power allocation policy is to transmit with the peak power when the channel gain is a above a fixed threshold; this threshold is chosen such that the average transmit power constraint is met. The computation of the greedy power allocation policy does not require the channel state information (CSI) of other users and, hence, it allows for a completely decentralized power allocation strategy. More importantly, we rigorously show that the greedy power allocation policy also becomes optimal forfinite, but sufficiently large, number of users. An upper bound on this minimum number of users beyond which the greedy power allocation policy becomes optimal is also provided. From simulations, we see that this finite threshold number of users is within practical limits.

We study the problem of decentralized power allocation in a multi-access channel (MAC) with non-cooperative users, additive noise of arbitrary distribution and a generalized power constraint, i.e., the transmit power constraint is modeled by an upper bound on$\mathrm{E}[\phi(\vert S\vert)$, where$S$is the transmit signal and$\phi(.)$is some non-negative, increasing and bounded function. The generalized power constraint captures the notion of power for different wireless signals such as RF, optical, acoustic, etc. We show that the optimal power allocation policy takes a simple closed form when there a large number of users. Further, once the number of users in the MAC crosses a finite threshold, this optimal power allocation policy remains invariant irrespective of the actual number of users. We derive the above results under the condition that the entropy power of the MAC,$e^{2h(S)+c}$, is strictly convex, where$h(S)$is the maximum achievable entropy of the transmit signal and$c$is a finite constant corresponding to the entropy of the additive noise.

Indoor optical wireless communication (IOWC) is a key technology complementing radio communication in 6G. Typically, the downlink of IOWC is implemented using visible light and uplink is implemented using infrared. Though the downlink of IOWC has been studied well, the uplink of multiple-access IOWC requires further research. In this paper, power allocation strategies in the uplink of IOWC with multiple non-cooperative users is studied. Optimizing power allocation in IOWC that maximize sum-rate with power constraints becomes a non-convex problem. First, this paper proposes an alternating maximization algorithm to compute near optimal power allocation policies for small number of users. Next, a phenomenon of invariance is observed when the number of users in the system is large. Under invariance, the optimal power allocation policy of a user becomes independent of other users, thereby facilitating fast and decentralized computation of power allocation strategies. From simulations, it was observed that the invariance can occur even with five to ten users. For the first time, a rigorous analysis of the invariance phenomenon is provided. An algorithm to compute the optimal power allocation at invariance is proposed; this algorithm is proven to achieve the global maxima in sum-rate without the channel knowledge of other users.

In this paper, we present an iterative best-response algorithm to compute the Nash equilibrium of a power allocation game in a multiple access channel (MAC), where each user greedily chooses a power allocation policy to maximize their average transmission rate. We consider a MAC where the fading channel gains are assumed to be stationary and ergodic processes, taking values from a finite set. The receiver decodes the message of a user by assuming the messages of the rest of the users as noise. The Shannon capacity of a user is considered to be the achievable rate of that user. A user transmits with a transmit power chosen from a finite set of power values, in a selfish manner, such that their average rate of transmission is maximized. We show that the Nash equilibrium of this game is unique, provided the number of users in the system is sufficiently large, but finite. Under this condition, we also show that the equilibrium policy does not change with more number of users coming into the system. We propose a simple greedy algorithm to compute the Nash equilibrium when the number of users is sufficiently large, but finite. The proposed algorithm does not depend upon the parameters of other users and hence, can be computed without any feedback or side-information from other users. We also present numerical results to illustrate the performance of the proposed algorithm.

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.