Mobile-edge computation offloading (MECO) offloads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer problem. To solve this challenging problem and characterize its policy structure, a low-complexity sub-optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance in simulation.

How the brain selects one action from among multiple options is unknown. A main tenet of signal detection theory (SDT) is that sensory stimuli are represented as noisy information channels. Therefore, the accuracy of selection might be predicted by how well neuronal activity representing alternatives can be distinguished. Here, we apply an SDT framework to a motor system by recording from superior colliculus (SC) neurons during performance of a color, oddball selection task. We recorded from sets of four neurons simultaneously, each of the four representing one of the four possible targets. Because the electrode placement constrained the position of the stimuli in the visual field, the stimulus arrangement varied across experiments. This variability in stimulus arrangement led to variability in choices allowing us to explore the relationship between SC neuronal activity and performance accuracy. SC target neurons had higher levels of discharge than SC distractor neurons in subsets of trials when selection performance was very accurate. In subsets of trials when performance was poor, the discharge level decreased in target neurons and increased in distractor neurons. Accurate performance was associated with larger separations between neuronal activity from targets and distractors as quantified by the receiver operating characteristic (ROC) area and d (an index of discriminability). Poorer performance was associated with less separation of target and distractor neuronal activity. ROC area and d scaled approximately linearly with performance accuracy. Furthermore, ROC area and d increased as saccade onset approached. Together, the results indicate that SC buildup neuronal activity signals the saccadic eye movement decision.

Gravity may provide a ubiquitous allocentric reference to the brain’s spatial orientation circuits. Here we describe neurons in the macaque anterior thalamus tuned to pitch and roll orientation relative to gravity, independent of visual landmarks. We show that individual cells exhibit two-dimensional tuning curves, with peak firing rates at a preferred vertical orientation. These results identify a thalamic pathway for gravity cues to influence perception, action and spatial cognition.

Brain regions involved in transforming sensory signals into movement commands are the likely sites where decisions are formed. Once formed, a decision must be read out from the activity of populations of neurons to produce a choice of action. How this occurs remains unresolved. We recorded from four superior colliculus neurons simultaneously while monkeys performed a target selection task. We implemented three models to gain insight into the computational principles underlying population coding of action selection. We compared the population vector average (PVA)/optimal linear estimator (OLE) and winner-takes-all (WTA) models and a Bayesian model, maximum a posteriori estimate (MAP), to determine which predicted choices most often. The probabilistic model predicted more trials correctly than both the WTA and the PVA. The MAP model predicted 81.88%, whereas WTA predicted 71.11% and PVA/OLE predicted the least number of trials at 55.71 and 69.47%. Recovering MAP estimates using simulated, nonuniform priors that correlated with monkeys' choice performance, improved the accuracy of the model by 2.88%. A dynamic analysis revealed that the MAP estimate evolved over time and the posterior probability of the saccade choice reached a maximum at the time of the saccade. MAP estimates also scaled with choice performance accuracy. Although there was overlap in the prediction abilities of all the models, we conclude that movement choice from populations of neurons may be best understood by considering frameworks based on probability.

Interference alignment (IA) is a joint-transmission technique that achieves the maximum degrees-offreedom (DoF) of the interference channel, which provides linear scaling of the capacity with the number of users for high signal-to-noise ratios (SNRs). Most prior work on IA is based on the impractical assumption that perfect and global channel-state information (CSI) is available at all transmitters. To implement IA, each receiver has to feed back CSI to all interferers, resulting in overwhelming feedback overhead. In particular, the sum feedback rate of each receiver scales quadratically with the number of users even if the quantized CSI is fed back. To substantially suppress feedback overhead, this paper focuses on designing efficient arrangements of feedback links, called feedback topologies, under the IA constraint. For the multiple-input-multiple-output (MIMO) K-user interference channel, we propose the feedback topology that supports sequential CSI exchange (feedback and feedforward) between transmitters and receivers so as to achieve IA progressively. This feedback topology is shown to reduce the network feedback overhead from a quadratic function of K to a linear one. To reduce the delay in the sequential CSI exchange, an alternative feedback topology is designed for supporting two-hop feedback via a control station, which also achieves the linear feedback scaling with K. Next, given the proposed feedback topologies, the feedback-bit allocation algorithm is designed for allocating feedback bits by each receiver to different feedback links so as to regulate the residual interference caused by the finite-rate feedback. Simulation results demonstrate that the proposed bit allocation leads to significant throughput gains especially in strong interference environments.