Abstract-We consider scheduling and resource allocation for the downlink of a cellular OFDM system, with various practical considerations including integer carrier allocations, different subchannelization schemes, a maximum SNR constraint per tone, and "self-noise" due to channel estimation errors and phase noise. During each time-slot a subset of users must be scheduled for transmission, and the available tones and transmission power must be allocated among the selected users. Employing a gradient-based scheduling scheme presented in earlier papers reduces this to an optimization problem to be solved in each time-slot. Using dual decomposition techniques, we give an optimal algorithm for this problem when multiple users can time-share each carrier. We then give several low complexity heuristics that enforce an integer constraint on the carrier allocation. Simulations show that the algorithms presented all achieve similar performance under a wide range of scenarios, and that the performance gap between the optimal and suboptimal algorithms widens when per user SNR constraints or channel estimation errors are considered.

Abstract-Orthogonal Frequency Division Multiplexing (OFDM) with dynamic scheduling and resource allocation is widely considered to be a key component of 4G cellular networks. However, scheduling and resource allocation in an OFDM system is complicated, especially in the uplink due to two reasons: (1) the discrete nature of channel assignments, and (2) the heterogeneity of the users' channel conditions, individual resource constraints and application requirements. We approach this problem using a gradient-based scheduling framework presented in previous work. Physical layer resources (bandwidth and power) are allocated to maximize the projection onto the gradient of a total system utility function which models application-layer Quality of Service (QoS). This is formulated as a convex optimization problem. We present an optimal solution using a dual decomposition. This solution has prohibitively high computational complexity but reveals guiding principles that we use to generate a family of lower complexity sub-optimal algorithms. We compare the performance of these algorithms via a realistic OFDM simulator.

When multiple processor (CPU) cores and a GPU integrated together on the same chip share the off-chip main memory , requests from the GPU can heavily interfere with requests from the CPU cores, leading to low system performance and starvation of CPU cores. Unfortunately, state-of-the-art application-aware memory scheduling algorithms are ineffective at solving this problem at low complexity due to the large amount of GPU traffic. A large and costly request buffer is needed to provide these algorithms with enough visibility across the global request stream, requiring relatively complex hardware implementations. This paper proposes a fundamentally new approach that de-couples the memory controller's three primary tasks into three significantly simpler structures that together improve system performance and fairness, especially in integrated CPU-GPU systems. Our three-stage memory controller first groups requests based on row-buffer locality. This grouping allows the second stage to focus only on inter-application request scheduling. These two stages enforce high-level policies regarding performance and fairness, and therefore the last stage consists of simple per-bank FIFO queues (no further command reordering within each bank) and straightforward logic that deals only with low-level DRAM commands and timing. We evaluate the design trade-offs involved in our Staged Memory Scheduler (SMS) and compare it against three state-of-the-art memory controller designs. Our evaluations show that SMS improves CPU performance without degrading GPU frame rate beyond a generally acceptable level, while being significantly less complex to implement than previous application-aware schedulers. Furthermore, SMS can be configured by the system software to prioritize the CPU or the GPU at varying levels to address different performance needs.

Abstract-Médard and Gallager recently showed that very large bandwidths on certain fading channels cannot be effectively used by direct sequence or related spread-spectrum systems. This paper complements the work of Médard and Gallager. First, it is shown that a key information-theoretic inequality of Médard and Gallager can be directly derived using the theory of capacity per unit cost, for a certain fourth-order cost function, called fourthegy. This provides insight into the tightness of the bound. Secondly, the bound is explored for a wide-sense-stationary uncorrelated scattering (WSSUS) fading channel, which entails mathematically defining such a channel. In this context, the fourthegy can be expressed using the ambiguity function of the input signal. Finally, numerical data and conclusions are presented for direct-sequence type input signals.Index Terms-Channel capacity, fading channels, spread spectrum, wide-sense-stationary uncorrelated scattering (WSSUS) fading channels.