the priority order every timeslot; and 3) WF-PERM, which assigns the subcarriers randomly (random priority). Figure 4 shows the comparison of the average ...
Subcarrier Allocation in OFDMA Systems: Beyond water-filling Somsak Kittipiyakul and Tara Javidi Department of Electrical Engineering University of Washington, Seattle, 98195-2500, USA {somsakk,tjavidi}@ee.washington.edu
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Abstract— This paper considers the issue of optimal subcarrier allocation in OFDMA. When including time-varying packet arrivals and channels, we show, via a counter example, that water-filling-based subcarrier allocation policies, contrary to conventional wisdom, fail to provide rate-stability for an otherwise stabilizable OFDMA system. We briefly discuss the long-run throughput optimality in OFDM as demonstrated in [13]. In particular under a simple channel model, we consider a non-idling policy which balances the queues and outperforms water-filling policies by achieving the minimum average delay. In this paper, we provide simple computation algorithms for implementing such policies and provide simulations for a numerical comparative study.
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I. I NTRODUCTION
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The problem of optimal real-time subcarrier allocation in OFDMA systems has been recently studied (e.g. [1][9]). Most of the papers in the literature ([1]-[3], [8]-[9]) provide solutions to the multi-user water-filling problem at each decision epoch (in this paper, we refer to this as “instantaneous throughput maximization”). We instead focus on a long-term average delay performance. We argue that maximizing instantaneous throughput (water-filling) is too myopic unless it is accompanied by a queue-aware allocation. Through the counter example in Figure 1, we show how a policy that maximizes the instantaneous throughput causes rate-instability (unbounded queue buildups) in an otherwise stabilizable system. By adaptively assigning subcarriers to the best users, water-filling achieves the instantaneous maximum throughput by taking advantage of channel diversity among users in different locations. Since it is unlikely that selective fading affects users on similar frequencies, there is an overall gain in selectively assigning subcarriers to users. This is known as multiuser diversity gain. When each user is assumed to have infinite bits in the buffer, water-filling is throughput optimal in both short- and long-term. However, when considering finite time-varying packet arrivals and time-varying channels, water-filling is not long-term throughput optimal. From dynamic programming [13], we know that instantaneous throughput maximization does not guarantee longterm throughput optimality in a stochastic environment. In general, an optimal long-term policy must trade off between two competing goals: on one end is the desire to get maximum throughput now and on the other end the desire to get the maximum throughput in the future. The second goal requires positioning the system to enjoy the highest
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Fig. 1. A counter-example showing that water-filling causes unbounded queue buildups. We assume that the packet arrival process and the channel connectivity process have periodic structures with period of four timeslots. (a) Packet arrivals for users 1 and 2 (b) Channel connectivity profile (white square means ON) (c) MTLB policy π ∗ (d) Maximal Instantaneous Throughput policy π (e) Queue occupancies over time: queue lengths for users U1 and U2 under π, (b1 , b2 ), and under π ∗ , (b∗1 , b∗2 ). Under policy π, queue of user U2 (b2 ) is building up over time.
multiuser diversity gain in the future. This can be generally achieved by avoiding the situations where some queues are empty while others are heavily backlogged. In other words, the optimal long-term policy must take advantage of the knowledge of the statistics of the channel and arrival processes. This knowledge is used to find the current action that optimizes both the expected future outcome and the current throughput. In this paper and [13], we focus on a simple special case of the arrival and channel processes. We assume a symmetric on-off channel model. This means that 1) users are assumed to be statistically homogeneous in term of their arrivals, channel responses, and priority; 2) a user’s channel condition (across sub-carriers) can be mapped to the number of transmitted packets at each subcarrier; and 3) when a subcarrier is ”ON” for a user, it can be used to transmit one packet during that timeslot. The transmit power of each user is assumed to be controlled as to compensate for nearfar problems and different path loss. The symmetric on-off channel model is valid in a scenario when there is only
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Subcarrier Allocation Problem.
one modulation and coding in the system. With the above assumptions, a subcarrier then can be considered as a server that is either connected or disconnected from users as shown in Figure 2. In [13], we proved that a maximum throughput (non-idling) load balancing (MTLB) policy minimizes the average total backlog over any arbitrary horizon time T. This policy not only provides optimal long-term throughput (rate-stability) for all admissible traffic but also minimizes average delay. The contribution of this paper is to extend the result in [13] on the construction of MTLB policy and to show the performance comparisons with simulation under a symmetric on-off channel model. However, for the readers’convenience we briefly provide problem statement and the result here. Detailed description is provided in [13] and [14]. Here we would like to emphasize that the load balancing in this work is shown to be essential in providing optimal throughput. This requirement is absolutely independent of fairness considerations. In other words, our proposed MTLB policy is not motivated by concerns regarding fairness, instead it demonstrates the following fundamental point: under realistic arrival patterns, load balancing is an essential element of any throughput optimal policies. II. P ROBLEM F ORMULATION In this paper, we consider a single-hop OFDMA system composed of one cell or cluster with one base station. There are N users and K sub-carriers. The sub-carriers are timeslotted. We assumed a centralized controller at the base that allocates subcarriers to users using perfect knowledge of channel state information and queue lengths. Channel state information is defined to be whether a subcarrier can be used to transmit a packet successfully or not. Under such assumptions, our problem is mapped to the following multiserver queuing problem (as a generalization of the problems studied in [10]). Problem (P) Consider a discrete-time model of N queues (Q1 , . . . , QN ) served by K servers (K > N ). At each time, each server can serve one packet from one queue; we allow for one queue to be served by multiple servers. At each time, a queue n is either available to be served by a server k (connected or ON) or it is not available (disconnected or OFF).
At each time, the connectivities of all queue/server pairs are known for that time. We allow for arrivals at each queue at each time and arrivals at a given time are assumed to occur after server allocations at that time. The statistics of arrival and connectivity processes are assumed to be symmetric across queues and independent across time. We wish to determine a server (subcarrier) allocation policy π that minimizes the average backlog over (finite or infinite) horizon T : ( T N ) XX 1 π JT = E bn (t) (1) T t=0 n=1
where bn (t) is the backlog of Qn . Definition 1: Considering ordering function ord : RN → RN to be such that for ∀x ∈ RN , y = ord(x) has the ordered elements of x in descending order i.e. yi ≥ yi+m , m > 0. Let x˜ = ord(x), y˜ = ord(y), and m = mini {{i : x˜i 6= y˜i } , N }. Then we say that x ≤LQO y (read x is smaller than y in term of Longest Queue Ordering or in another word x is more balanced than y) if and only if x˜m ≤ y˜m . We say x is strictly more balanced than y, x
