2015 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)
Coordinated Dynamic Spectrum Management of LTE-U and Wi-Fi Networks
Shweta Sagari*, Samuel Baysting*, Dala Saha t, Ivan Seskar*, Wade Trappe*, Dipankar Raychaudhuri* *WINLAB, Rutgers University, {shsagari, sbaysting, seskar, trappe, ray}@winlab.rutgers.edu tNEC Labs America, [email protected]
Abstract-This paper investigates the co-existence of Wi-Fi and LTE networks in emerging unlicensed frequency bands which are intended to accommodate multiple radio access technologies. Wi-Fi and LTE are the two most prominent wireless access technologies being deployed today, motivating further study of the inter-system interference arising in such shared spectrum scenarios as well as possible techniques for enabling improved co-existence. An analytical model for evaluating the baseline performance of co-existing Wi-Fi and LTE networks is developed and used to obtain baseline performance measures. The results show that both Wi-Fi and LTE networks cause significant interference to each other and that the degradation is dependent Fig. 1. Proposed spectrum ba nds for deployment of LTElWi-Fi small cells. on a number of factors such as power levels and physical topology. The model-based results are partially validated via and 5 GHz unlicensed bands for the proposed unlicensed experimental evaluations using USRP-based SDR platforms on LTE operation as a secondary LTE carrier [6]. These bands the ORBIT testbed. Further, inter-network coordination with are currently utilized by unlicensed technologies such as Wi logically centralized radio resource management across Wi-Fi Fi/Bluetooth. The 3.5 GHz band, which is currently utilized and LTE systems is proposed as a possible solution for improved co-existence. Numerical results are presented showing significant for military and satellite operations has also been proposed for gains in both Wi-Fi and LTE performance with the proposed small cell (Wi-FiILTE based) services [7]. Another possibility inter-network coordination approach. is the 60 GHz band (millimeter wave technology), which is well suited for short-distance communications including Gbps Keywords-Wi-Fi, LTE-U, dynamic spectrum management, Wi-Fi, 5G cellular and peer-to-peer communications [8]. In inter-network coordination, optimization addition, opportunistic spectrum access is also possible in TV white spaces for small cellibackhaul operations [9].
I. INTRODUCTION These emerging unlicensed band scenarios will lead to co-channel deployment of multiple radio access technologies Exponential growth in mobile data usage is driven by the (RATs) by multiple operators. These different RATs, designed fact that Internet applications of all kinds are rapidly migrating for specific purposes at different frequencies, must now coexist from wired PCs to mobile smartphones, tablets, MiFis and in the same frequency, time, and space. This causes increased other portable devices [1]. The wireless industry is already interference to each other and potential degradation of the gearing up for a rv 1000x increase in data capacity associated overall system performance due to the lack of inter-RAT com with the requirements of 5G mobile networks planned for 2020 patibility. Fig. 2 shows two such scenarios where two networks and beyond. The 5G vision is not just limited to matching the with different access technologies interfere with each other. increase in mobile data demand, but also includes an improved When a Wi-Fi Access Point (AP) is within the transmission overall service-oriented user experience with immersive ap zone of LTE, it senses the medium and postpones transmission plications such as high definition video streaming, real-time due to detection of co-channel LTE Home eNodeB's (HeNB) interactive games, wearable mobile devices, ubiquitous health transmission power as shown in Fig. 2(a). Consequently, the care, mobile cloud, etc. [2]-[4]. Such applications not only Wi-Fi throughput suffers in the presence of LTE transmission. demand the higher data rates but also require an improved The main reason for this disproportionate drop in the Wi Quality of Experience(QoE) as measured through parameters Fi throughput is due to the fact that LTE does not sense such as lower latency (round trip time), lower power con other transmissions before transmitting. In contrast, Wi-Fi sumption (longer battery life), better radio coverage (reliable is designed to coexist with other networks as it senses the services), cost-effective network, and support for mobility. channel before any transmission. However, when LTE works To meet such high data rate and QoE demands, three main in supplemental downlink-only mode and the DEs do not solutions are proposed [5]: (a) addition of more radio spectrum transmit, there may arise a scenario where a Wi-Fi AP can for mobile services (increase in MHz); (b) deployment of not sense LTE HeNB's transmission, thus causing interference 2 small cells (increase in bits/Hzlkm ); and (c) efficient spectrum to nearby UEs, as shown in Fig. 2(b). This problem also arises 2 utilization (increase in bits/second /Hzlkm ). Several spectrum in multiple Wi-Fi links that overlap in the collision domain, bands, as shown in Fig. 1, have been opened up for mobile but the network can recover packets quickly as (a) packets are and fixed wireless broadband services. These include the 2.4 transmitted for a very short duration in Wi-Fi, compared to
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requirement for each client [16], [17]. We also incorporate the proposed interference characterization of Wi-Fi and LTE co-channel deployment in the optimization framework which allows us to account for the specific requirements of each of the technologies. We adopt the geometric programming framework developed in [18] for the LTE-only networks and enhance it to accommodate Wi-Fi networks.
The major contributions of this work are as follows:
• We introduce an analytical model to characterize the interference between Wi-Fi and LTE networks when they coexist in time, frequency and space. The model 2. LTE Fig. Scenarios showing challenges of and Wi-Fi coexistence in the is also validated by performing experimental analysis same unlicensed spectrum. using USRP-based LTE nodes and commercial off the-shelf (COTS) IEEE 802.11g devices in the ORBIT longer frames in LTE and (b) all the nodes perform carrier testbed. sensing before transmission. Therefore, to fully utilize the • benefits of new spectrum bands and deployments of HetNets, We propose a coordination framework to facilitate efficient spectrum utilization needs to be provided by the dynamic spectrum management among multi-operator dynamic spectrum coordination framework and the supporting and multi-technology networks over a large geograph network architecture. ical area.
• It is reasonable to forecast that Wi-Fi and LTE will be We propose a logically centralized optmllzation among the dominant technologies used for radio access over framework that involves dynamic coordination be tween Wi-Fi and LTE networks by exploiting power the next few years. Thus, this paper focuses on coordinated coexistence between these two technologies. When LTE is control and time division channel access diversity. deployed in an unlicensed band, it is termed as LTE-U. It is • We evaluate the proposed optimization framework for suggested in 3GPP that LTE-U will be used for supplemental improved Wi-Fi and LTE networks coexistence. downlink while the uplink will continue to use licensed spec trum. This makes the deployment even more challenging as The rest of the paper is organized as follows. In §II, we the DE's do not transmitin unlicensed spectrum yet experience discuss previous work on this topic and distinguish our work interference from Wi-Fi transmissions. To alleviate these prob from the existing literature. In §III, we propose an analytical lems, we extend the interference characterization of co-channel model to characterize the interference between Wi-Fi and deployment of Wi-Fi and LTE using a simplistic but accurate LTE networks followed by partial experimental validation of analytical model [10]. We then validate this model through the model. In §IV, we propose an SDN-based inter-network experimental analysis of co-channel deployment in the 2.4 coordination architecture that can be used for transferring GHz band using the ORBIT testbed available at WINLAB. The control messages between the different entities in the network. ORBIT testbed consists of several radio platforms including In §VI, we use two approaches-power control and channel USRPs. access time sharing, to jointly optimize the spectrum sharing among Wi-Fi and LTE networks. §VII evaluates the proposed To support the co-existence of a multi-RAT network, we approaches. We conclude in §VIII. propose a dynamic spectrum coordination framework, which is enabled by a Software Defined Network (SDN) architecture. BACKGROUND ON CO-EXISTENCE SDN is technology-agnostic, can accommodate different radio II. WI-FI/LTE standards, and does not require change to existing standards or Coordination between multi-RAT networks with LTE and protocols. In contrast to existing technology-specific solutions, Wi-Fi is challenging due to differences in the medium access this is a desirable feature in view of the rapid development control (MAC) layer of the two technologies. of the upcoming technologies and the availability of new spectrum bands [11]-[13]. Furthermore, the proposed frame Wi-Fi is based on the distributed coordination function work takes advantage of the ubiquitous Internet connectivity (DCF) where each transmitter senses the channel energy for available at wireless devices. It also provides the ability to transmission opportunities and collision avoidance. In partic consider policy requirements in conjunction with improved ular, clear channel assessment (CCA) in Wi-Fi involves two visibility of each of the access technologies, spectrum bands, functions to detect any on-going transmissions [19], [20] - and clients/operators. Thus, it offers significant benefits for 1) Carrier sense: Defines the ability of the Wi-Fi node to spectrum allocation over radio-based control channels [14] or detect and decode other nodes' preambles, which most centralized spectrum servers [15]. likely announces an incoming transmission. In such cases, SDN-enabled inter-network cooperation can be achieved by Wi-Fi nodes are said to be in the CSMA range of each optimizing several spectrum usage parameters such as power other other. For the basic DCF with no RTS/CTS, the control, channel selection, rate allocation, duty cycle, etc. In Wi-Fi throughput can be accurately characterized using this paper, we focus on power control at both Wi-Fi and LTE the Markov chain analysis given in Bianchi's model [21], nodes to maximizes aggregate throughput across all clients in assuming a saturated traffic condition (at least 1 packet both Wi-Fi and LTE networks while considering throughput is waiting to be sent) at each node. Wi-Fi channel rates
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used in the [21] can be modeled as a function of Signal- experimental studies evaluating the coexistence performance to-Interference-plus-Noise ratio. Our throughput analysis of Wi-Fi and LTE. Thus, this paper focuses on these aspects given in the §III is based on the Bianchi’s model. to provide a complete evaluation. 2) Energy detection: Defines the ability of Wi-Fi to detect non-Wi-Fi (in this case, LTE) energy in the operating III. INTERFERENCE CHARACTERIZATION channel and back off the data transmission. If the in- A. Interference Characterization Model band signal energy crosses a certain threshold, the channel is detected as busy (no Wi-Fi transmission) until the We propose an analytical model to characterize the inter- channel energy is below the threshold. Thus, this func- ference between Wi-Fi and LTE, while considering the Wi- tion becomes the key parameter for characterizing Wi-Fi Fi sensing mechanism (clear channel assessment (CCA)) and throughput in the co-channel deployment with LTE. scheduled and persistent packet transmission at LTE. To illus- trate, we focus on a co-channel deployment involving a single LTE has both frequency division (FDD) and time division Wi-Fi and a single LTE cell, which involves disseminating (TDD) multiplexing modes to operate. But to operate in the interaction of both technologies in detail and establish a unlicensed spectrum, supplemental downlink and TDD access building block to study a complex co-channel deployment of is preferred. In either of the operations, data packets are sched- multiple Wi-Fis/LTEs. uled in the successive time frames. LTE is based on orthogonal frequency-division multiple access (OFDMA), where a subset In a downlink deployment scenario, a single client and of subcarriers can be assigned to multiple users for a certain a full buffer (saturated traffic condition) is assumed at each P ,i ∈ symbol time. This offers LTE an additional diversity in the time AP under no MIMO. Transmit powers are denoted as i {w, l} w l and frequency domain that Wi-Fi lacks, since Wi-Fi assigns where and are indices to denote Wi-Fi and LTE fixed bandwidth to a single user at any time. Further, due to links, respectively. We note that the maximum transmission current exclusive band operation, LTE does not employ any power of an LTE small cell is comparable to that of the Wi- sharing features in the channel access mechanisms. Thus, the Fi, and thus is consistent with regulations of unlicensed bands. coexistence performance of both Wi-Fi and LTE is largely The power received from a transmitter j at a receiver i unpredictable and may lead to unfair spectrum sharing or the is given by PjGij where Gij ≥ 0 represents a channel gain γ starvation of one of the technologies [22], [23]. which is inversely proportional to dij where dij is the distance i j γ G In the literature, spectrum management in shared fre- between and and is the path loss exponent. ij may also include antenna gain, cable loss and wall loss. Signal-to- quency bands has been discussed for multi-RAT heterogeneous i networks primarily focusing on IEEE 802.11/16 networks Interference-plus-Noise (SINR) of the link is given as [12]–[14]. For instance, Cognitive WiMAX achieves coop- PiGii Si = ,i,j∈{w, l},i= j (1) erative resource management in hierarchical networks with PjGij + Ni power/frequency assignment optimization, Listen-before-Talk where Ni is noise power for receiver i. For the case of a single (LBT), etc. along with guaranteed QoE [24], [25]. These i j principles need to be extended to Wi-Fi and LTE coexistence Wi-Fi and LTE, if represents the Wi-Fi link, then is the and modified specific to their protocols. Wi-Fi and LTE LTE link, and vice versa. coexistence has been studied in the context of TV white Throughput, Ri,i∈{w, l}, is represented as a function of space [26], in-device coexistence [27], and LTE unlicensed Si as (LTE-U) [28]–[30]. Studies [29]–[31] propose CSMA/sensing Ri = αiB log2(1 + βiSi),i∈{w, l}, (2) based modifications in LTE such as LBT, RTS/CTS protocol, where B is a channel bandwidth; βi is a factor associated with and slotted channel access. Other solustions such as blank α LTE subframes/LTE muting (feature in LTE Release 10/11) the modulation scheme. For LTE, l is a bandwidth efficiency [26], [32], carrier sensing adaptive transmission (CSAT) [29], due to factors adjacent channel leakage ratio and practical filter, cyclic prefix, pilot assisted channel estimation, signaling interference aware power control in LTE [33] require LTE α to transfer its resources to Wi-Fi. These schemes give Wi- overhead, etc. For Wi-Fi, w is the bandwidth efficiency of Fi transmission opportunities but also lead to performance CSMA/CA, which comes from the Markov chain analysis of tradeoffs for LTE. Further, time domain solutions often require CSMA/CA [21] with time synchronization between Wi-Fi and LTE and increase TE TS TC ηE = ,ηS = ,ηC = , (3) channel signaling. Frequency and LTE bandwidth diversities E[S] E[S] E[S] are explored in studies [29] and [34], respectively. where E[S] is the expected time per Wi-Fi packet transmission; In this paper, we propose Wi-Fi and LTE coordination algo- TE, TS, TC are the average times per E[S] that the channel is rithms based on optimization in power and frequency domain, empty due to random backoff, or busy due to the successful which does not require modifications to existing MAC layer transmission or packet collision (for multiple Wi-Fis in the protocols. Our time division channel access (TDCA) algorithm CSMA range), respectively. αw is mainly associated with ηS. resembles CSAT, but TDCA is a centralized approach with a The term {αi,βi} is approximated based on throughput joint consideration of Wi-Fi and LTE QoE requirements for models given in [10] so that Rl matches with throughput fairness. Furthermore, limited details of LTE-U co-existence achieved under variable channel quality index (CQI), and Rw mechanisms (adaptive duty cycle/switch-OFF) and interference matches throughput according to Bianchi’s CSMA/CA model. model are available in public domain [35]. Also previous studies have yet to consider dense Wi-Fi and LTE deployment 1Throughput the paper, LTE home-eNB (HeNB) is also referred as access scenarios in detail. Notably, in the literature, there are no point (AP) for the purpose of convenience
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1) Characterization of Wi-Fi Throughput: Assuming Ae is a threshold of CCA energy detection mechanism, if channel energy at the Wi-Fi node is higher than Ae, Wi-Fi would hold back the data transmission, otherwise it transmits at a data rate based on the SINR of the link. Wi-Fi throughput with and without LTE is given as
Modell: Wi-Fi Throughput Characterization
Data: Pw: Wi-Fi Tx power; Gw: channel gain of Wi-Fi link; �: LTE Tx power; Gwl: Pig. 3. Experimental scenario to evaluate the throughput performance of channel gain(LTE AP, Wi-Fi UE); No: Wi-Pi WI in the presence of interference (LTElother Wi-Pi/white noise) when noise power; Ee: channel energy at the both WI and interference operated on the same channel in 2.4 GHz Wi-Fi (LTE interference + No). Parameter: Ae: Wi-Fi CCA threshold Output : Rw: Wi-Fi throughput if No LTE then ( PwGw ) Rw=awBlog2 l+f3w . I � else When LTE is present if Ee > Ae then I No Wi-Fi transmission with Rw= 0 else ( PwGw ) I Rw =a wB log2 1 +f3w . PIGwl + No end end
Pig. 4. Comparative results analytical model and experiments to show the effect ofLTE on the throughput of Wi-Pi 802.11g when distance betweenLTE eNS and Wi-Pi link is varied. 2) Characterization of LTE Throughput: Due to CSMNCA, Wi-Fi is active for an average "Is fraction of time (Eq. (3». Assuming that LTE can instantaneously update B. Experimental Validation its transmission rate based on the Wi-Fi interference, its In this section, we experimentally validate proposed in throughput can be modeled as follows- terference characterization models using experiments involv ing the ORBIT testbed and USRP radio platforms available Model 2: LTE Throughput Characterization at WINLAB [36], [37]. An 802.11g Wi-Fi link is set up Data: PI: LTE Tx power; Gl: channel gain of using Atheros AR928X wireless network adapters [38] and LTE link; Pw: Wi-Fi Tx power; Glw: an AP implementation with hostapd [39]. For LTE, we use channel gain(Wi-Fi AP,LTE UE); No: OpenAirlnterJace, an open-source software implementation, noise power; Ee: channel energy at Wi-Fi which is fully compliant with 3GP P LTE standard (release (LTE interference + No); 8.6) and set in transmission mode 1 (SISO) [40]. Currently, Parameter: Ae: Wi-Fi CCA threshold OpenAirlnterJace is in the development mode for USRP based Output : RI: LTE throughput platforms with limited working LTE operation parameters. Due if No Wi-Fi then to limitations in the available setup, we perform experiments ( P�l ) in the 2.4 GHz spectrum. We note that, though channel R =a lBlog2 1 + f31 . fading characteristics differ in other spectrum bands, Wi-Fi I lnoW and LTE coexistence throughput behavior remains the same else When Wi-Fi is present with appropriately scaled distance. if Ee > Ae then No Wi-Fi transmission/interference In our experiment, depicted as the scenario shown in Fig. 3, we study the effect of interference on the Wi-Fi link Wl. For I RI=RI · no W link WI. the distance between the AP and client is fixed at 0.25 else m (very close so that the maximum throughput is guaranteed ( G ) when no interference is present. Experimentally, we observe RI =a lBlog2 1 + f31 1 � � a maximum throughput as 22.2 Mbps). The distance between P G No . the interfering AP and Wi-Fi AP is varied in the range of Using (3) and e = 0 (a single Wi-Fi) "I 1 to 20 m. The throughput of Wl is evaluated under two sources of interference - LTE and Wi-Fi, when both Wl and the RI = "IERI + "IsRI no W interference AP is operated on the same channel in the 2.4 GHz end spectrum band. These experiments are carried in the 20 m-by- end 20 m ORBIT room in WINLAB, which has an indoor Line-
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Fig. 6. Experimental scenario to evaluate the throughput performance of Wi-Fi WI in the presence of interference (LTElother Wi-Fi/white noise) when both WI and interference operated on the same channel in 2.4 GHz
Fig. 5. Comparative results analytical model and experiments to show the effect of LTE on the throughput of Wi-Fi 802.11g when distance between LTE HeNB (AP) and Wi-Fi link is varied. other Wi-Fi link. These results indicate a significant effect of inter-network interference on throughput in the baseline case without any coordination between networks. TABLE 1. NETWORK PARAMETERS OF WI-FIILTE DEPLOYMENT Parameter Value I Parameter I Value C. Motivational Example Scenario Downlink I Tx power I 20 dBm Spectrum band 2.4 GHz I Channel bandwidth I 20 MHz Traffic model Full buffer via saturated UDP flows We extend our interference model to complex scenarios in AP antenna height 10m I User antenna heIght I I m volving co-channel deployment of a single link Wi-Fi and LTE Path loss model 36.7IoglO(d[m]) + 22.7 + 26loglo(fIq [GHz]) for the detailed performance evaluation. As shown in Fig. 6, NOIse Floor -101 dBm, (-174 dBm thermal noise/Hz) UBi, associated APi and interfering APj, i,j E {w, l},i =I- j, Channel No shadowlRayleigh fading Wi-Fi S02.lIn: SISO are deployed in a horizontal alignment. The distance, dA, LTE FDD, Tx mode-I (SISO) between UBi and APi is varied between 0 and 100 m. At each value of dA, the distance between UBi and APj is varied in the range of -100 to 100 m. Assuming UBi is located at of-Sight (LoS) environment. For each source of interference, the origin (0,0), if APj is located on the negative X-axis then Wi-Fi throughput is averaged over 15 sets of experiments with the distance is denoted as -d[, otherwise as +dJ, where d[ is variable source locations and trajectories between interference an Euclidean norm IIUBi, APjll . In the shared band operation AP and WI. of Wi-Fi and LTE, due to the CCA sensing mechanism at the Wi-Fi node, the distance between Wi-Fi and LTE APs (under In the first experiment. we perform a comparison study no shadow fading effect in this study) decides the transmission to evaluate the effect of LTE interference on WI, observed or shutting off of Wi-Fi. Thus, the above distance convention is by experiments and computed by interference characterization adopted to embed the effect of distance between APi and AP j. model. In this case, LTE signal is lightly loaded on 5 MHz of Simulation parameters for this set of simulations are given in bandwidth mainly consist of control signals. Thus, the impact Table I. of such LTE signal over the Wi-Fi band is equivalent to the low power LTE transmission. Thus, we incorporate these LTE Fig. 7 shows the Wi-Fi performance in the presence of parameters in our analytical model. As shown in Fig. 4, we LTE interference. As shown in Fig. 7(a), the Wi-Fi throughput observe that both experimental and analytical values match is drastically deteriorated in the co-channel LTE operation, the trend very closely, though with some discrepancies. These leading to zero throughput for 80% of the cases and an av discrepancies are mainly due to the fixed indoor experiment en erage 91% of throughput degradation compared to standalone vironment and lack of a large number of experimental data sets. operation of Wi-Fi. Such degradation is explained by Fig. 7(b). Additionally, we note that even with the LTE control signal Region CCA-busy shows the shutting off of the Wi-Fi AP due (without any scheduled LTE data transmission), performance to the CCA mechanism, where high energy is sensed in the of Wi-Fi gets impacted drastically. Wi-Fi band. This region corresponds to cases when Wi-Fi and LTE APs are within '" 20m of each other. In the low SINR In the next set of experiments, we study the throughput region, the Wi-Fi link does not satisfy the minimum SINR of a single Wi-Fi link in the presence of different sources of requirement for data transmission, thus the Wi-Fi throughput interference - (1) Wi-Fi, (2) LTE operating at 5 MHz, and (3) is zero. High SINR depicts the data transmission region that LTE operating at 10 MHz, evaluating each case individually. satisfies SINR and CCA requirements and throughput is varied For this part, full-band occupied LTE is considered with the based on variable data rate/SINR. maximum power transmission of 100 mW. As shown in Fig. 5, when the Wi-Fi link operates in the presence of other Wi-Fi On the other hand, Fig. 8 depicts the LTE throughput in the links, they share channel according to the CSMNCA protocol presence of Wi-Fi interference. LTE throughput is observed to and throughput is reduced approximately by half. In the both be zero in the low SINR regions, which is 45% of the overall the cases of LTE operating at 5 and 10 MHz, due to lack area and the average throughput degradation is 65% compared of coordination, Wi-Fi throughput gets impacted by maximum to the standalone LTE operation. Under identical network. upto 90% compared to no interference Wi-Fi throughput and parameters, overall performance degradation for LTE is much 20 - 80% compared to Wi-Fi thorughput in the presence of lower compared to that of Wi-Fi in the previous example. The
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Fig. 7. Wi-Fi performance as a function of distance(Wi-Fi AP, associated Fig. 8. LTE performance as a function of distance(LTE AP, associated LTE Wi-Fi UE) dA and distance(Interfering LTE AP, Wi-Fi UE) dI UE) dA and distance(Interfering Wi-Fi AP, LTE UE) dI reasoning for such a behavior discrepancy is explained with respect to Fig. 8(b) and the Wi-Fi CCA mechanism. In the network state information and controls the flow of information CCA-busy region, Wi-Fi operation is shut off and LTE operates between RCs and databases based on authentication and other as if no Wi-Fi is present. In both LTE and the previous Wi- regulatory policies. Decisions at the GC are based on different Fi examples, low SINR represents the hidden node problem network modules, such as radio coverage maps, coordination where two APs do not detect each other’s presence and data algorithms, policy and network evaluation matrices. The RCs transmission at an UE suffers greatly. are limited to network management of specific geographic regions and the GC ensures that RCs have acquired local visibility needed for radio resource allocation at wireless IV. SYSTEM ARCHITECTURE devices. A Local Agent (LA) is a local controller, co-located In this section, we describe an architecture for coordinating with an access point or base-station. It receives frequent between multiple heterogeneous networks to improve spectrum spectrum usage updates from wireless clients, such as device utilization and facilitate co-existence [11]. Fig. 9 shows the location, frequency band, duty cycle, power level, and data proposed system, which is built on the principles of a Software rate. The signaling between RC and LAs are event-driven, Defined Networking (SDN) architecture to support logically- which occurs in scenarios like the non-fulfillment of quality- centralized dynamic spectrum management involving multiple of-service (QoS) requirements at wireless devices, request-for- autonomous networks. The basic design goal of this architec- update from an RC and radio access parameter updates from an ture is to support the seamless communication and informa- RC. The key feature of this architecture is that the frequency tion dissemination required for coordination of heterogeneous of signaling between the different network entities is less in networks. The system consists of two-tiered controllers: the higher tiers compared to lower tiers. RCs only control the Global Controller (GC) and Regional Controllers (RC), which regional messages and only wide-area network level signalling are mainly responsible for the control plane of the architecture. protocols are handled at the higher level, GC. Furthermore, this The GC, owned by any neutral/authorized organization, is the architecture allows adaptive coordination algorithms based on main decision making entity, which acquires and processes the geographic area and change in wireless device density and
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The throughput definition of the LTE link i, i ∈Lremains of a product of SINR at both Wi-Fi and LTE links. Power the same as in Section III-A: control optimization formulation is given by: ⎛ ⎞ Ri = αl log2(1 + βlSi),i∈L. β S aibiαw ⎝ β S αl ⎠ The SINR of the LTE link, i, ∀i, in the presence of Wi-Fi and maximize ( w i) ( l i) no Wi-Fi is described as i∈W j∈L ⎧ R ≥ R ,i∈W, PiGii subject to i i,min ⎪ , if no Wi-Fi; ⎨ P G N Ri ≥ Ri,min,i∈L, j∈L,j=i j ij + 0 Si = ⎪ PiGii PkGik + PjGij + N0 <λc,i∈ W, ⎩ , if Wi-Fi, k∈M b j∈L j∈L,j=i PjGij + k∈W akPkGik + N0 i (6) 0 216 2015 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) 100 100 100 60 60 50 50 50 50 50 40 40 [m] 0 I [m] 0 [m] 0 I I d d 30 d 30 20 -50 20 -50 -50 10 10 -100 20 40 60 80 100 -100 0 d [m] -100 0 20 40 60 80 100 A 20 40 60 80 100 d [m] d [m] A Infeasible Feasible A (a) A heat map of Wi-Fi throughput when joint (b) Feasibility region of joint power (c) A heat map of Wi-Fi throughput when time power Coordination (Mbps) Coordination division channel access coordination (Mbps) Fig. 10. Wi-Fi performance under joint Wi-Fi and LTE coordination (dA: dist(Wi-Fi AP, associated UE), dI : dist(Interfering LTE AP, Wi-Fi UE)) Wi-Fi and LTE, respectively, as VII. EVA L UAT I O N O F JOINT COORDINATION maximize Ri A. Single Link Co-channel Deployment i∈W We begin with the motivational example of co-channel subject to Ri ≥ Ri,min,i∈W deployment of one Wi-Fi and one LTE links, as described in (10) § 0 ≤ Pi ≤ Pmax,i∈W, III-C. Fig. 10 shows the heatmap of improved throughput of Wi-Fi link, when joint Wi-Fi and LTE coordination is provided PkGik + N0 <λc,i∈W. in comparison with the throughput with no coordination as b k∈Mi shown in Fig. 7 . Similarly, Fig. 11 shows the heatmap of improved throughput of LTE link, when joint coordination is and R provided in comparison with the throughput with no coordi- maximize i nation, as shown in Fig. 8. i∈L (11) subject to Ri ≥ Ri,min,i∈L For both the figures 10 and 11, in their respective scenar- ios, though joint power control improves the overall throughput 0 ≤ Pi ≤ Pmax,i∈L. for most of topological scenarios (see Fig. (a) of 10 and 11), Here, the objective function is equivalent to maximizing the it is not an adequate solution for topological combination product of SINRs at the networks i, i ∈{w, l}. The first and marked by infeasible region as given in Fig. (b) of 10 and 11. second constraints ensure that we meet the minimum SINR The infeasible region signifies the failure to attain the CCA and transmission power limits requirements at all links of i.In threshold at Wi-Fi AP and link SINR requirement when the this formulation, SINR at Wi-Fi and LTE respectively given UE and interfering AP are very close to each other. When we as apply time division channel access optimization for a given PiGii scenario, we do not observe any infeasible region, in fact Si = ,i∈W, N0 optimization achieves almost equal and fair throughput at both P G S i ii ,i∈L. Wi-Fi and LTE links, as shown in Fig. (c) of 10 and 11. On the i = downside, this optimization does not consider cases when Wi- j∈L,j=i PjGij + N0 Fi and LTE links can operate simultaneously without causing which are first cases in equations (5) and (6), respectively. severe interference to each other. In such cases, throughput at both Wi-Fi and LTE is degraded. 2) Joint time division channel access optimization: This is the joint optimization across both Wi-Fi and LTE networks Fig. 12 summarizes the performance of Wi-Fi and LTE which is formulated using max-min fairness optimization as links in terms of 10th percentile and mean throughput. We given below note that the 10th percentile throughput of both Wi-Fi and LTE is increased to 15 − 20 Mbps for time division coordination maximize min (ηRi∈W , (1 − η)Rj∈L) ∼ (12) compared to zero throughput for no and power coordination. subject to 0 ≤ η ≤ 1. We observe 200% and 350% Wi-Fi mean throughput gains due to power and time division channel access, respectively, Here, throughput values at all Wi-Fi and LTE nodes are compared to no coordination. For LTE, throughput gains considered as a constant, which is the output of the previous for both of these coordination is ∼ 25 − 30%. It appears step. The Time division channel access parameter η is opti- that time division channel access coordination does not offer mized so that it maximizes the minimum throughput across any additional advantage to LTE in comparison with power all UEs. coordination. But it brings the throughput fairness between 217 2015 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) 100 100 100 60 60 50 50 50 50 50 40 40 [m] 0 I [m] 0 [m] 0 I I d d 30 d 30 20 -50 20 -50 -50 10 10 -100 20 40 60 80 100 -100 0 d [m] -100 0 20 40 60 80 100 A 20 40 60 80 100 d [m] d [m] A Infeasible Feasible A (a) A heat map of LTE throughput when joint (b) Feasibility region of joint power (c) A heat map of LTE throughput when time power Coordination (Mbps) Coordination division channel access coordination (Mbps) Fig. 11. LTE performance under joint Wi-Fi and LTE coordination (dA: dist(LTE AP, associated UE), dI : dist(Interfering Wi-Fi AP, LTE UE)) 10 percentile throughput Mean throughput 10 percentile throughput Mean throughput 25 30 10 25 20 8 20 20 15 6 15 10 4 10 10 Throughput [Mbps] Throughput [Mbps] Throughput [Mbps] 5 2 Throughput [Mbps] 5 0 0 0 0 WiFi LTE WiFi LTE 2510 2510 No Coordination Pwr Control TimeDivCh Access No Coordination Pwr Control TimeDivCh Access (a) 10 percentile and mean Wi-Fi throughput for N = {2, 5, 10} Fig. 12. 10 percentile and mean LTE throughput for a single link Wi-Fi and LTE co-channel deployment 10 percentile throughput Mean throughput 20 50 40 Wi-Fi and LTE which is required for the co-existence in the 15 shared band. 30 10 B. Multiple Links Co-channel Deployment 20 5 Throughput [Mbps] Multiple overlapping Wi-Fi and LTE links are randomly Throughput [Mbps] 10 deployed in 200-by-200 sq. meter area which depicts the 0 0 typical deployment in residential or urban hotspot. The number 2510 2510 of APs of each Wi-Fi and LTE networks are varied between No Coordination Pwr Control TimeDivCh Access 2 to 10 where number of Wi-Fi and LTE links are assumed (b) 10 percentile and mean LTE throughput for N = {2, 5, 10} to be equal. For the simplicity purpose, we assume that only single client is connected at each AP and their association is predefined. The given formulation can be extended for multiple client scenarios. In the simulations, the carrier sense Fig. 13. Multi-link throughput performance under power control and time and interference range for Wi-Fi devices are set to 150 meters devision channel access optimization. N = no. of LTE links = no. of Wi-Fi and 210 meters, respectively. The hidden node interference links. parameter is set to 0.25. Figures 13(a) and 13(a) show the percentile and mean coordination. This is consistent with results from single link throughput values of Wi-Fi and LTE links, respectively, for simulations. With coordination, both joint power control and when number of links for each Wi-Fi and LTE networks is set time division channel access, we achieve a large improvement at N = {2, 5, 10}. The throughput performance is averaged in the 10th percentile throughput. Joint power control improves over 10 different deployment topologies of Wi-Fi and LTE mean Wi-Fi throughput by 15-20% for all N. On the other links. From Fig. 13(a), it is clear that 10 percentile Wi-Fi hand, time division channel access achieves throughput gain UEs get throughput starved due to LTE interference with no (40-60%) only at higher values of N = {5, 10}. 218 2015 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) Throughput performance of LTE, on the other hand, de- [6] “Summary of a workshop on LTE in unlicensed spectrum,” RP-140060, teriorates in the presence of coordination compared to when 3GPP TSG-RAN Meeting 63, 2014. no coordination is provided. This comes from the fact that, in [7] “Enabling innovative small cell use in 3.5 GHz band NPRM and order,” case of no coordination, LTE causes undue impact at Wi-Fi Federal Communications Commission 12-148, 2012. nodes causing them to hold off data transmission, while LTE [8] Ali Sadri, “mmWave technology evolution from WiGig to 5G small experiences no Wi-Fi interference. 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