The volume of mobile traffic is exploding, driven by a proliferation of connected devices. The high bandwidth required by multimedia applications is severely stretching the available wireless spectrum. Consumers influenced by fixed access broadband services expect almost instant file uploads/downloads and transfers between terminals (laptops, tablets, and smart TVs), IPTV, and HD video. The challenge for service providers is therefore to extend the capability of wireless access to maintain performance characteristics in the transition to predominantly wireless service provision. The entertainment industry will be just the first adopter to drive and inspire other futuristic scenarios in connected health, social and community interaction, and education. The 60 GHz band, with up to four 2.16 GHz channels, supports high-data-rate short-range line-of-sight (LOS) directional transmissions. Millimeter-wave (mmWave) communication technologies such as IEEE 802.15.3c-2009 [1] and IEEE 802.11ad [2] are uniquely positioned due to their ability to deliver the gigabit-per-second throughput envisaged for the fifth generation (5G). Despite the adverse propagation characteristics of mmWave technologies (a result of high propagation loss due to oxygen absorption and atmospheric attenuation), the fact that waves are confined within walls makes it ideal for personal communications: promoting security and privacy. This also promotes more efficient frequency reuse, therefore creating very high bandwidth hubs that are ideal for the delivery of gigabitper-second applications. A major question, however, is how to combat the impact of blockage of the LOS link by human shadowing or obstacles, which severely impacts the transmission. In order to support the high volume of highquality services, optimized resource allocation is required. Optimizing resource allocation in a multi-user multimedia gigabit scenario poses interesting challenges. Most traditional optimization techniques have been used to solve convex optimization problems. However, real-time multimedia applications introduce non-convex utility functions in which the quality perceived by the user does not increase gracefully with an increase in throughput. A re-think of resource allocation optimization techniques is necessary in this context. Relatively new approaches, such as particle swarm optimization (PSO), are ideal candidates to solve non-convex problems. Our proposal compares PSO to other techniques and presents channel-time allocation PSO (CTA-PSO) as a solution to optimize channel time allocation (resource) even when blockage occurs in the mmWave network. IEEE 802.15.3c [1] and IEEE 802.11ad [2] provide mechanisms at the medium access control (MAC) layer to allocate resource (in the form of channel access time) to multiple users. A period of contention allows for different terminals to request access time while allocated channel time slots provide dedicated data transmission time. The advantage of scheduled access is to provide a dedicated time slot for communication, thus supporting guaranteed quality of service (QoS) for an application. This guarantee is not possible when all network devices compete for bandwidth using random access techniques. The allocation process is described here and illustrated in Fig. 1 . ENABLING MMWAVE TECHNOLOGIES The 5G network infrastructure is driven by the evolution of today's most demanding applications. Already, multimedia applications such as on-demand HD video and IPTV require gigabit-per-second throughput and low delay, while future technologies include ultra HDTV and machine-to-machine communication. Mm-Wave technologies such as IEEE 802.15.3c and IEEE 802.11ad are ideal candidates to deliver high throughput to multiple users demanding differentiated QoS. Optimization is often used as a methodology to meet throughput and delay constraints. However, traditional optimization techniques are not suited to a mixed set of multimedia applications. Particle swarm optimization (PSO) is shown as a promising technique in this context. Channel-time allocation PSO (CTA-PSO) is successfully shown here to allocate resource even in scenarios where blockage of the 60 GHz signal poses significant challenges. An 802.15.3c network, or piconet, is a wireless ad hoc data communications system in which a number of independent devices communicate with each other in a peer-to-peer fashion [1] . In the 802.15.3c protocol, medium access is controlled by a piconet controller (PNC). This role is usually held by the first member of the network. The superframe is initiated by a beacon from the PNC. In order to achieve the required signal range at 60 GHz, a directional beacon is employed, but in order to reach all potential neighbors, the beacon must be transmitted in all directions. A quasi-omni beacon is therefore used. The area around the PNC is divided into sectors, and the PNC transmits a directional beam beacon sector by sector until all sectors have been covered. A contention access period (CAP) follows the beacon phase. The S-CAP is sectorized, so in the association S-CAP, all devices in a sector have the opportunity to contend to send association request commands and for the PNC to send immediate acknowledgments. The regular S-CAP and CAP are then available for command and data exchanges. The CAP is a carrier sense multiple access with collision avoidance (CSMA/CA) phase. In CSMA/CA, a device first senses the channel prior to transmission. If the channel is free, the device transmits its frame. Otherwise, if the channel is sensed busy, the device generates a random backoff counter and defers its transmission until the backoff counter expires. In 802.15.3c, devices compete during the CAP to transmit a request for dedicated channel time during which the application data can be transmitted. The device time allocation is called a channel time allocation (CTA) and occurs during the channel time allocation period (CTAP), which follows the CAP. The CTAP operates with time-division multiple access (TDMA). In this phase, a time slot/CTA is assigned to any devices that have requested an access period, provided the time is available. In order to improve the MAC efficiency, frame aggregation and block acknowledgment are supported by the standard. These techniques are also illustrated in Fig. 1 . Frame aggregation involves mapping MAC service data units (MSDUs) into multiple subframe payloads. This increases the data to header ratio in the frame thus increasing throughput. A block acknowledgment (Blk-Ack) is sent for the aggregated frame. The benefit of these mechanisms is improved efficiency by reducing frame/Ack overhead. Accommodating the constraints of the mmWave frequency band and supporting the requirements of high-data-rate applications, IEEE 802.11ad supplements and extends earlier versions of IEEE 802.11 MAC. It operates in a similar way to 802.15.3c with periods of contention-based and scheduled access. However, the allocation of  these transmission periods differs from the 802.15.3c approach, as illustrated in Fig. 1 . Channel access time in IEEE 802.11ad is divided into beacon intervals (BIs). Each BI consists of the beacon transmission interval (BTI), association beamforming training (A-BFT), announcement transmission interval (ATI), and data transfer interval (DTI). During the BTI, the control/access point transmits directional beacons to each sector of the sectorized network. Request-response-based transmission allocations of scheduled service periods (SPs) and contention-based access periods (CBAPs) take place in the ATI. Finally, the DTI consists of a series of SPs and CBAPs during which application data transmissions occur. The importance of the compatibility of IEEE 802.11ad with legacy 802.11 standards is clear. A transition will be possible between the lower frequency band (2.4/5 GHz) for long-range communication and the higher frequency band (60 GHz) for short-range communication [3] . It is this heterogeneous network style that will underpin the 5G infrastructure. While both protocols described here combine CSMA/CA for contention-based medium access and TDMA for scheduled service, they differ in the order of allocation. In IEEE 802.15.3c, the CTA requests are sent during an initial contention-based period (CAP), and the CTAP consumes the majority of the superframe with individual time slots (CTAs) allocated for application data transmission. In contrast, contentionbased access periods (CBAPs) and scheduled service periods (SPs) are alternated in IEEE 802.11ad to support compatibility with legacy 802.11 standards. The objective of both methods is to maximize network throughput. Optimizing resource allocation is key to maximizing network throughput. One approach to resource allocation for multiple applications is the assignment of packets to queues, where each queue has a priority level for accessing the wireless medium. For example, the packets of a real-time low-latency application would receive the highest priority in accessing the available bandwidth. However, although this achieves prioritization between application types, it does not consider the specific (and variable) requirements of individual devices within an application type and how throughput/QoS might be optimized by appropriate scheduling of these transmissions. It is our belief that both the application type and the specific individual application requirements must be considered for optimal resource allocation. In our consideration of the channel time allocation optimization problem, we exploit the MAC layer structure of IEEE 802.15.3c and IEEE 802.11ad. The contention periods are used for the exchange of time slot request-response messages. The contention-free/scheduled service periods are dedicated to application data transmission with defined QoS. Optimization involves finding the best solution to a problem. Mathematically, this involves maximization/minimization of an objective/utility function, which may be unconstrained or subject to certain constraints on the variable(s) of the function. The nature of the utility function for multimedia applications determines the type of optimization problem [4] . Classically, resource management solutions handle video as an isolated application in the network, thus solving a convex optimization problem. For example, the higher the data rate at which the video can be transmitted, the better the quality perceived by the user and hence the higher the utility for the user. In a practical network, however, mixed multimedia applications (e.g., IPTV, VoIP) must be served along with video. The utility functions to describe such applications are non-convex. The non-convexity arises from the inelasticity of realtime applications, meaning that the application does not improve or degrade gracefully in response to an increase or a reduction in allocated transmission rate [5] . Rather, a reduction in data rate below a certain threshold results in a significant drop in QoS. The challenge for 5G is therefore a combination of resolving the non-convex optimization problem (resulting from multiple applications) within the constraints of the mmWave network. This includes the practical consideration of the execution time of the algorithm. Framing resource allocation as a network utility maximization (NUM) problem, the implementation of four popular optimization techniques are compared here for their solution potential. Techniques suitable for convex optimization problems only are included in order to illustrate the difference in attributes of each technique, specifically with respect to speed of execution of the algorithm. The problem is defined in Eq. 1. (1)
An accurate description and comparative analysis of both protocols can be found in REF .
Multimedia resource allocation in mmwave 5G networks
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