To meet the increasing demand of high-data-rate services of high-speed railway (HSR) passengers, cloud radio access network (C-RAN) is a promising technique in mobile communication system of HSR. In this paper, we focus on the energy-efficient power allocation problem for providing the high- data- rate services in HSR scenario. Based on the predicted path loss characteristic of HSR, the power allocation is formulated as a non-linear fractional programming problem. The service pro- cessing delay of baseband unit (BBU) pool and radio transmission delay at radio remote units (RRUs) are considered in the services transmission delay constraint. The formulated problem is a non- convex problem, an equivalent convex problem is reformulated firstly, and then propose an iterative algorithm to obtain the optimal power allocation solution. Furthermore, we analyze the effect of small-scale fading on energy-efficiency (EE) performance of the proposed power allocation scheme. Simulation results confirm that the proposed energy-efficient power allocation policy can meet the QoS of passengers and obtain the optimal EE performance.

In this paper, we investigate the delay-aware dynamic resource management problem for multi- service transmission in high-speed railway wireless communications, with a focus on resource allocation among the services and power control along the time. By taking account of average delay requirements and power constraints, the considered problem is formulated into a stochastic optimization problem, rather than pursuing the traditional convex optimization means. Inspired by Lyapunov optimization theory, the intractable stochastic optimization problem is transformed into a tractable deterministic optimization problem, which is a mixed-integer resource management problem. By exploiting the specific problem structure, the mixed-integer resource management problem is equivalently transformed into a single variable problem, which can be effectively solved by the golden section search method with guaranteed global optimality. Finally, we propose a dynamic resource management algorithm to solve the original stochastic optimization problem. Simulation results show the advantage of the proposed dynamic algorithm and reveal that there exists a fundamental tradeoff between delay requirements and power consumption.

This paper considers an energy-efficient packet scheduling problem over quasi-static block fading channels. The goal is to minimize the total energy for transmitting a sequence of data packets under the first-in-first-out rule and strict delay constraints. Conventionally, such a design problem is studied under the assumption that the packet transmission rate can be characterized by the classical Shannon capacity formula, which, however, may provide inaccurate energy consumption estimation, especially when the code blocklength is finite. In this paper, we formulate a new energy-efficient packet scheduling problem by adopting a recently developed channel capacity formula for finite blocklength codes. The newly formulated problem is fundamentally more challenging to solve than the traditional one, because the transmission energy function under the new channel capacity formula neither can be expressed in closed form nor possesses desirable monotonicity and convexity in general. We analyze conditions on the code blocklength for which the transmission energy function is monotonic and convex. Based on these properties, we develop efficient offline packet scheduling algorithms as well as a rolling-window-based online algorithm for real-time packet scheduling. Simulation results demonstrate not only the efficacy of the proposed algorithms but also the fact that the traditional design using the Shannon capacity formula can considerably underestimate the transmission energy for reliable communications.

This paper considers an energy-efficient packet scheduling problem over green data networks, aiming at minimizing the transmission energy subject to the First-In-First-Out and strict delay constraints. Traditionally, such a problem is studied based on the classical Shannon capacity formula. However, Shannon capacity is valid only when the code blocklength approaches infinity and therefore is not practical for some applications in 5G system which allow short delays only. In this paper, we formulate the packet scheduling problem using the recently developed channel capacity formula for the finite blocklength code. It turns out that the newly formulated problem is much more challenging to solve than the traditional ones. Nevertheless, we analytically show that our scheduling problem can possess certain desirable monotonic and convex properties. Based on these properties, by applying a successive upper bound minimization (SUM) method, an iterative packet scheduling algorithm is proposed to efficiently solve the considered problem. Simulation results show that, compared with the proposed design using the finite blocklength channel capacity, the traditional design based on Shannon capacity will seriously underestimate the required transmission energy for reliable communications.

Dense deployment of femtocells can cause serious intra-tier interference. In this paper, we propose a new cooperative approach for allowing the femtocell user equipments (FUEs) to merge into cooperative groups, i.e., coalitions, for the uplink transmissions in a femtocell network, so as to reduce the intra-tier interference and improve the system performance, while taking the power cost for cooperation into account. We formulate this cooperation problem as a coalitional game in partition form with externalities due to the interference and the cost. Then, we propose a novel algorithm, which takes advantage of the characteristics of the femtocell network and allows the FUEs to interact and individually decide on which coalitions to participate in. Simulations are conducted to illustrate the behavior and the performance of the proposed coalition formation algorithm among FUEs, relative to the non-cooperative approach. Results show that the proposed algorithm outperforms the non-cooperative case in terms of the aggregate system utility.

With the rapid development of high-speed railway (HSR) system, there is an increasing demand on providing high throughput and continuous multimedia (CM) services for HSR passengers. In this paper, we investigate the downlink resource allocation problem for on-demand CM services in HSR OFDMA systems with a cellular/infostation integrated network architecture. The resource allocation problem is formulated as a two-stage optimization programming, which aims at maximizing the total reward of delivered services then minimizing the weighted total number of cumulative discontinuity packets over the trip of the train. An equivalent one-stage programming is proposed to resolve the difficulty of multi-stage optimization. The resultant mixed integer programming (MIP) is NP-hard in general, we thus reformulate it as a sparse ℓ0-minimization problem and then relax it to a linear programming (LP). Furthermore, a reweighted ℓ1-minimization technique is applied to improve the system performance. Simulation results are provided to validate the proposed algorithms.

With the rapid development of high-speed railway (HSR), how to provide the passengers with multimedia services has attracted increasing attention. A key issue is to develop an effective scheduling algorithm for multiple services with different quality of service (QoS) requirements. In this paper, we investigate the downlink service scheduling problem in HSR network taking account of end-to-end deadline constraints and successfully packet delivery ratio requirements. Firstly, by exploiting the deterministic high-speed train trajectory, we present a time-distance mapping in order to obtain the highly dynamic link capacity effectively. Next, a novel service model is developed for deadline constrained services with delivery ratio requirements, which enables us to turn the delivery ratio requirement into a single queue stability problem. Based on the Lyapunov drift, the optimal scheduling problem is formulated and the corresponding scheduling service algorithm is proposed by stochastic network optimization approach. Simulation results show that the proposed algorithm outperforms the conventional schemes in terms of QoS requirements.

Interference alignment (IA) technique has shown great promise in many theoretical studies. However, the performance improvement brought by IA in the MIMO interference network has not been investigated well when combined with power allocation. This paper proposed a joint IA-based beamforming design and power allocation method with an objective of optimizing the overall throughput for a MIMO interference network consisting of K transmitter-receiver pairs. Since the joint optimization problem is not convex, we invoke a iterative approach. Based on the minimum interference leakage criterion, the general minimum leakage distributed IA algorithm without the limitation of average power allocation is presented. Then we deal with a network-wide power allocation problem by modified iterative water-filling algorithm, while taking into consideration both the interstream interference and interlink interference. Finally, simulation results show that the proposed method outperforms the minimum leakage distributed IA algorithm in terms of improving throughput and reducing total interference power of the whole MIMO interference network.