Wireless vs. Wireline

Wireless technology is playing a profound role in networking and communications, even though wireline technologies such as fiber have inherent capacity advantages. Relative to wireless networks, wireline networks have had greater capacity and historically have delivered faster throughput rates.

While wireless networks can provide a largely equivalent broadband experience for many applications, for ones that are extremely data intensive, wireline connections will remain a better choice for the foreseeable future. For example, users streaming Netflix movies in high definition consume about 5 Mbps. Typical LTE deployments use 10 MHz radio channels on the downlink and have a spectral efficiency of 1.4 bps/Hertz (Hz), providing LTE an average sector capacity of 14 Mbps. Thus, just three Netflix viewers could exceed sector capacity. In the United States, there are approximately 1,100 subscribers, on average, per cell site8, hence about 360 for each of the three sectors commonly deployed in a cell site. 

In dense urban deployments, the number of subscribers can be significantly higher. Therefore, just a small percentage of subscribers can overwhelm network capacity. For Blu-ray video quality that operates at around 16 Mbps or Netflix 4K streaming that runs at 15.6 Mbps, an LTE cell sector could support only one user.

Even if mobile users are not streaming full-length movies in high definition, video is finding its way into many applications, including education, social networking, video conferencing, business collaboration, field service, and telemedicine.

Over time, wireless networks will gain substantial additional capacity through  the methods discussed in the next section, but they will never catch up to wireline. One can understand this from a relatively simplistic physics analysis:

q  Wireline access to the premises or to nearby nodes uses fiber-optic cable.

q  Capacity is based on available bandwidth of electromagnetic radiation. The infra- red frequencies used in fiber-optic communications have far greater bandwidth than radio.

q  The result is that just one fiber-optic strand has greater bandwidth than the entire usable radio spectrum to 100 GHz, as illustrated in Figure 1.

Figure 1: RF Capacity vs. Fiber-Optic Cable Capacity

A dilemma of mobile broadband is that it can provide a broadband experience similar to wireline, but it cannot do so for all subscribers in a coverage area at the same time. Hence, operators must carefully manage capacity, demand, policies, pricing plans, and user expectations. Similarly, application developers must become more conscious of the inherent constraints of wireless networks.

Mobile broadband networks are best thought of as providing access to higher-capacity wireline networks. The key to improving per-subscriber performance and bandwidth is reducing the size of cells and minimizing the radio path to the wireline network, thus improving signal quality and decreasing the number of people active in each cell. These are the motivations for Wi-Fi offload and small-cell architectures.

GENERALIZED WIRELESS PACKET SCHEDULING

In this section, we consider the general problem of packet scheduling in wireless networks. For later sections it will set the stage for defining the classification/framework for scheduling algorithms in WiMAX. The fundamental characteristics of packet-networks operating over wireless channels include 

(1) time-varying wireless channel capacity, 

(2) location-dependent channel errors and traffic that is bursty in nature, 

(3) contention among mobile hosts, 

(4) mobile hosts do not have a global channel state 

(5) proper type of scheduler required for both UL and DL flows, and 

(6) mobile hosts often have limited battery and processing power. 

The above-mentioned factors need to be considered very carefully, while designing schedulers for wireless networks. Otherwise, the performance will not be optimal.

Two basic performance measures used in the literature for packet schedulers are throughput and fairness. Throughput usually refers to the amount of data transferred from the BS to the SS, in its own traffic class; whereas fairness refers to, ideally, equal allocation of allotted bandwidth to all SSs for a particular traffic class. If an SS lies in a bad channel state, then there should not be bandwidth allocation to that particular SS. The concept of fairness is discussed in more detail in the latter part of this section.

In wired networks, the retransmitted packets can be excluded from throughput computation to give another performance measure, known as goodput. Similarly, in WiMAX networks, either throughput or goodput can be used as measures of data transferred from BS to SS and vice versa. It should be noted that customers in various traffic classes will try to maximize their throughputs, which may lead to classical selfish game-theoretic behavior, which needs to be policed by mechanisms at the SSs or at the BSs. In such a competitive environment, each user will be trying to maximize its own utility function.

While taking into account the temporal characteristics of channels, a wireless packet scheduler should have the following essential features:

§  Efficient utilization of wireless channel bandwidth: The wireless scheduling algorithm should utilize the channel efficiently and should avoid wasting resources on links operating in bad state. An efficient service discipline will be able to meet the end-to-end performance guarantees for various service classes under all load conditions.

§  Throughput bound: For each service class, the scheduler should be able to provide a short-term throughput bound for flow with a clean channel and a long-term throughput bound for all flows including those in an error state in the channel.

§  Short-term and long-term fairness: The scheduler should be able to provide fair allocation of bandwidth to all flows, from various traffic classes, within a good channel state as well as to those lying within a bad state.

§  Delay bound: The scheduling algorithm should be able to provide a guaranteed delay bound on various traffic classes.

§  Implementational complexity: The scheduling algorithm should be simple and have a low time complexity to select and forward a packet from the queues of various classes. Generally, fairness and delay bound requirements collide with the complexity of the scheduling algorithm. Schedulers having good fairness and strict delay bounds are harder to implement, whereas algorithms are simplest but provide poor fairness and delay bounds.

§  Graceful degradation of service: The scheduler should be able to compensate for backlogged flows, which have not received service due to bad channel conditions, at the expense of those flows which have received extra service due to good channel conditions. This corrective reduction in the service allocation of certain flows belonging to wireless channel in a good state should be smooth and gradual.

§  Protection against misbehaving flows: The scheduling algorithm should be able to protect service guarantees for various classes and eliminate the effects of misbehaving flows, network load fluctuations, and best effort uncontrolled traffic flows (such as in the case of denial-of-service (DOS) attacks).

§  Decoupling between delay and bandwidth: The scheduler should be able to decouple bandwidth and delay and thus should support both delay sensitive and error sensitive flows. Usually, the classes having higher reserved data rate also have low delay requirements; however, some high bandwidth applications can work well even with larger delays, such as web browsing.

§  Flexibility and scalability: The scheduling algorithm should be flexible enough to cope with the vast number of different current types of IP traffic, as well for future traffic characteristics. Also, it should perform well when there is an increase in the number of connections for each traffic class.

§  Power efficiency: The scheduling algorithm should be power efficient. It is especially important for the mobile subscriber’s equipment, wherein currently available batteries have a limited (charged) life.