In this paper, we study the problem of estimating rank indicator (RI) and channel quality indicator (CQI) under 5G new radio (NR) specification. The main challenge for estimating RI and CQI comes from the fact that their estimation result is affected by many factors, e.g., delay spread, Doppler spread, etc. We use a shallow reinforcement learning technique to estimate the expected spectral efficiency for different RIs and CQIs. In particular, Q-learning is applied to solve the problem with a neural network consisting of a single hidden layer. To update the neural network output when the channel behavior changes, we develop an online adaptation (OA) algorithm working on top of the trained neural network. As a result, the combined algorithm adaptively controls the reported RI and CQI values to satisfy a target block error rate (BLER). We show through simulation results that the proposed algorithm adapts to different channel conditions and maintains the BLER to be around a target value of 10%. The proposed algorithm also significantly outperforms a scheme of fixed CQI and fixed RI when channel varies slowly.

We consider an energy harvesting transmitter sending status updates regarding a physical phenomenon it observes to a receiver. Different from the existing literature, we consider a scenario where the status updates carry information about an independent message. The transmitter encodes this message into the timings of the status updates. The receiver needs to extract this encoded information, as well as update the status of the observed phenomenon. The timings of the status updates, therefore, determine both the age of information (AoI) and the message rate (rate). We study the tradeoff between the achievable message rate and the achievable average AoI. We propose several achievable schemes and compare their rate-AoI performances.

We consider a two-user energy harvesting multiple access channel where the temperatures of the nodes are affected by the electromagnetic waves caused by the data transmission. To protect the nodes from excess heat, power allocation policies should take into consideration the temperature of all the nodes. In this paper, we study the optimal power allocations when the temperatures of the nodes are subject to peak temperature constraints. We first study the general case where each node has a different peak temperature requirement and the nodes have different temperature parameters. For this case, we show that the capacity region of the single energy arrival case is a single pentagon. We also show that the optimal policy for the multiple energy arrivals can be obtained by using generalized water-filling. We then study the temperature limited case where the transmitters have abundant energy and the only binding constraints are the temperature constraints. We study the optimal power allocation in this case and we derive sufficient conditions under which the rate region collapses to a single pentagon.

We consider online transmission policies for a two-user multiple access channel, where both users harvest energy from nature. The energy harvests are independent and identically distributed (i.i.d.) over time, but can be arbitrarily correlated between the two users. The transmitters are equipped with arbitrary but finite-sized batteries. We focus on the online case where the transmitters know the energy arrivals only causally as they happen. The users do not know the probability distribution of the energy arrivals; each user knows only its own average recharge rate. We consider the most general case of arbitrarily distributed energy arrivals with arbitrary correlation between the users. In order to study this general case, we first study a special case for the energy arrivals, namely, we first consider the special case of synchronized (i.e., fully-correlated) Bernoulli energy arrivals at the two users. Even though the energy arrivals are fully-correlated, average recharge rates at the users are different due to the different battery sizes. For this case, we determine the exactly optimal policies that achieve the boundary of the long-term average capacity region. We show that the optimal power allocation policy is decreasing within the renewal interval, and that the long-term average capacity region is a single pentagon. We then propose a distributed fractional power (DFP) policy, which users implement distributedly with no knowledge of the other user's energy arrival or battery state. We develop a lower bound on the performance of the DFP for synchronized Bernoulli energy arrivals. We then consider the case of two arbitrarily correlated asynchronous Bernoulli energy arrivals under the assumption of equal normalized average recharge rates. We show that extreme correlation between the energy sources hurts the achievable rate by showing that the throughput with asynchronous Bernoulli energy arrivals is larger than the throughput with the corresponding perfectly synchronized Bernoulli energy arrivals. We then show that under the DFP policy, the performance of Bernoulli energy arrivals forms a lower bound on the performance of any arbitrary energy arrivals. We also develop a universal upper bound on the performance of all online policies, and show that the proposed DFP is near-optimal in that it yields rates which are within a constant gap of the derived lower and upper bounds, and hence, of the optimal policy, for all system parameters.

We consider power scheduling policies for single-user energy harvesting communication systems, where the goal is to characterize online policies that maximize the long term average utility, for general concave and monotonically increasing utility functions. The transmitter relies on energy harvested from nature to send its messages to the receiver, and is equipped with a finite-sized battery to store its harvested energy. Energy packets are independent and identically distributed (i.i.d.) over time slots, and are revealed causally to the transmitter. We first characterize the optimal solution for the case of Bernoulli arrivals. Then, for general i.i.d. arrivals, we first show that fixed fraction policies, in which a fixed fraction of the battery state is consumed in each time slot, are within a constant multiplicative gap from the optimal solution for all energy arrivals and battery sizes. We then derive a set of sufficient conditions on the utility function to guarantee that fixed fraction policies are within a constant additive gap as well from the optimal solution. We then apply these results to a specific scenario where a sensor node collects samples from a Gaussian source and sends them to a destination node over a Gaussian channel, and the goal is to minimize the long term average distortion of the source samples received at the destination. We study two problem settings for this case: the first is when sampling is cost-free, and the second is when there is a sampling cost incurred whenever samples are collected. For the problem with sampling costs, the transmission policy can be bursty; the sensor may collect samples and transmit for only a portion of the time. Finally, we present an alternative analysis approach that is more tailored to these distortion problems to show that fixed fraction policies achieve an additive gap that is independent of the sampling cost.

With a motivation to understand the effects of temperature sensitivity on wireless data transmission performance, we consider an energy harvesting communication system, where the temperature dynamics are governed by the transmission power policy. Different from the previous work, we consider a discrete time system where transmission power is kept constant in each slot. We consider two models that capture different effects of temperature. In the first model, the temperature is constrained to be below a critical temperature at all time instants; we coin this the explicit temperature constrained model. We investigate throughput optimal power allocation for multiple energy arrivals under general, as well as temperature and energy limited regimes. We show that the optimal power allocation for the temperature limited case is monotone decreasing. In the second model, we consider the effect of the temperature on the channel quality via its influence on additive noise power; we coin this the implicit temperature constrained model. In this model, the change in the variance of the additive noise due to previous transmissions is non-negligible. In particular, transmitted signals contribute as interference for all subsequent slots and thus affect the signal to interference plus noise ratio (SINR). In this case, we investigate throughput optimal power allocation under general, as well as low and high SINR regimes. We show in the low SINR regime that the optimal allocation dictates the transmitter to save its harvested energy till the last slot. In the high SINR regime, we show that the optimal power sequence is monotone increasing. Finally, we consider the case in which implicit and explicit temperature constraints are simultaneously active and we show under certain conditions that the optimal power sequence is monotone decreasing.

We consider an energy harvesting communication system where the temperature dynamics is governed by the transmission power policy. Different from the previous work, we consider a discrete time system where transmission power is kept constant in each slot. We consider two models that capture different effects of temperature. In the first model, the temperature is constrained to be below a critical temperature at all time instants; we coin this model as explicit temperature constraint model. We investigate throughput optimal power allocation for multiple energy arrivals under general, as well as temperature and energy limited regimes. In the second model, we consider the effect of the temperature on the channel quality; we coin this model as implicit temperature constraint model. As the dynamic range of the temperature changes significantly, the change in the thermal noise becomes non-negligible, affecting the signal to interference plus noise ratio (SINR). In effect, transmitted signals contribute as interference for all subsequent slots. In this case, we investigate throughput optimal power allocation under general, as well as low and high SINR regimes.

We consider online scheduling policies for single-user energy harvesting communication systems, where the goal is to characterize online policies that maximize the long term average utility, for some general concave and monotonically increasing utility function. In our setting, the transmitter relies on energy harvested from nature to send its messages to the receiver, and is equipped with a finite-sized battery to store its energy. Energy packets are independent and identically distributed (i.i.d.) over time slots, and are revealed causally to the transmitter. Only the average arrival rate is known a priori. We first characterize the optimal solution for the case of Bernoulli arrivals. Then, for general i.i.d. arrivals, we first show that fixed fraction policies [1] are within a constant multiplicative gap from the optimal solution for all energy arrivals and battery sizes. We then derive a set of sufficient conditions on the utility function to guarantee that fixed fraction policies are within a constant additive gap as well from the optimal solution.

We consider a single-user channel in which the transmitter is equipped with finite-sized data and energy buffers. The transmitter receives energy and data packets randomly and intermittently over time and stores them in the finite-sized buffers. The arrival amounts are known only causally as they happen. We study the online power allocation problem, in which the transmitter relies only on the causal arrival (energy and data) information. We focus on the special case when the energy and data arrivals are fully-correlated. We first study the case when the arrivals are Bernoulli. For this case, we determine the optimal policy. Inspired by this policy and in order to study the case of general fully-correlated arrivals, we propose a structured policy and bound its performance by a multiplicative gap from the optimal. We then show that this policy is optimal when the energy arrivals dominate the data arrivals, and is within a constant additive gap from the optimal policy when the data arrivals dominate the energy arrivals.

We consider scheduling for a single-user energy harvesting channel in which the transmitter incurs processing cost per unit time it is on. The presence of processing costs forces the transmitter to operate in a bursty mode. We consider online transmission scheduling where the transmitter knows the energy harvests only causally as they arrive, and needs to determine the optimum transmit power and the optimum burst duration on the fly. We first consider the case of independent and identically distributed (i.i.d.) Bernoulli energy arrivals, and then extend it to the case of general i.i.d. energy arrivals. We determine the exactly optimum online policy for Bernoulli arrivals and propose a nearly optimum online policy for general arrivals. The proposed policy is near-optimum in that it performs within a constant gap from the optimum policy for all energy arrivals and battery-sizes.

We consider an energy harvesting two-way channel with decoding costs. In this system, each node spends energy to transmit data to the other user, and also to decode data coming from the other user; that is, each user divides its harvested energy for transmission and reception. The power needed for decoding the incoming data is a function of the incoming data rate. We determine the optimal offline power scheduling policies for both users that maximize the sum throughput of the system by a given deadline. We first consider the case with a single energy arrival at each user. We show that the transmission is limited by the user with the smaller energy. In this case, the user with larger energy may not consume all of its energy. We next consider the case with multiple energy arrivals at both users. We show that the optimal power allocations are non-decreasing over time, and they increase synchronously at both users. We then develop an iterative algorithm based on two-slot updates to obtain the optimal power allocations for both users.

We consider online transmission scheduling for an energy harvesting broadcast channel with a finite-sized battery. The energy harvests are independent and identically distributed (i.i.d.) in time, and the transmitter gets to know them only causally as they happen. We first consider the case of Bernoulli energy arrivals, and determine the optimum online strategy that allocates power over time and between users optimally. We then consider the case of general i.i.d. energy arrivals, and propose a sub-optimum strategy coined fractional power constant cut-off (FPCC) policy. We develop a lower bound for the performance of the proposed FPCC policy, and a universal upper bound for the capacity region of the energy harvesting broadcast channel. We show that the proposed FPCC policy is near-optimal in that it yields rates that are within a constant gap from the optimum online policy, for all system parameters.

We consider online transmission policies for the two-user multiple access channel, where both users harvest energy from a common source. The transmitters are equipped with arbitrary but finite-sized batteries. The energy harvests are independent and identically distributed (i.i.d.) over time, and synchronized at the two users due to their common source. The transmitters know the energy arrivals only causally. We first consider the special case of Bernoulli energy arrivals. For this case, we determine the optimal policies that achieve the boundary of the capacity region. We show that the optimal power allocation decreases in time, and that the capacity region is a pentagon. We then consider general i.i.d. energy arrivals, and propose a distributed fractional power (DFP) policy. We develop lower and upper bounds on the performance of the proposed DFP policy for general i.i.d. energy arrivals, and show that the proposed DFP is near-optimal in that it yields rates which are within a constant gap of the derived lower and upper bounds.

We consider a two-way communication channel in which both users rely solely on energy harvested from nature. Each user incurs a processing cost per unit time as long as it communicates; that is, each user's energy consumption includes energy spent for transmission and energy spent for processing. We maximize the sum throughput by a given deadline subject to energy causality constraints. We first show that the optimal power policy is bursty; the two users communicate only during a portion of the time that is uniquely determined by their available energies and processing costs. We show that it is optimal for the two users to be fully synchronized; they turn on and exchange data during the same portion of time, and then turn off together. We first solve the single energy arrival case, and then extend it to solve the multiple energy arrival throughput maximization problem. We show that it is optimal for the users to communicate in a deferred fashion; users postpone their energy consumption to utilize later time slots first. We present an algorithm that gives the optimal deferred policy by iteratively applying a modified version of the single energy arrival result in a backward manner.

We consider an energy harvesting broadcast channel where a transmitter powered by energy harvested from the environment serves data to two receivers on the downlink. Energy harvests occur randomly over time as an independent and identically distributed (i.i.d.) random process; the battery at the transmitter in which the harvested energy is stored is of finite size. We focus on online transmission schemes where the transmitter knows the energy arrivals only causally as they happen. We first consider the case where the energy arrivals follow a Bernoulli distribution, where the incoming energy is either zero or it fills the battery completely. For this case, we determine the optimum online strategy that allocates power over time and between users optimally. We note that the optimum total transmit power is not equal to the optimum single-user transmit power as it depends on the receiver noise variance; this is unlike the offline problem, where the optimum total transmit power in the broadcast channel equals the optimum single-user transmit power. We then consider the case of general i.i.d. energy arrivals, and propose a sub-optimum strategy coined fractional power constant cut-off (FPCC) policy. We develop a lower bound for the performance of the proposed FPCC policy. In addition, we develop a universal upper bound for the broadcast channel capacity region that depends only on the average recharge rate. We show that the FPCC policy is near-optimal in that it yields rates that are within a constant gap from the developed upper bound, and therefore, from the actual capacity region, for all system parameters.