Location and Service-Aware Downlink ... - Semantic Scholar

Location and Service-Aware Downlink Transmission Power Allocation in WCDMA-based Cellular Networks K.Tsagkaris(1), P.Demestichas(2), M.Theologou(1)

Abstract: Power constitutes an essential controllable resource for the improvement of the radio link performance in WCDMA-based cellular networks. In addition, complementary to traditional services, location based services (LBS) have emerged as a growing area for mobile service providers bringing in the foreground the potential for a locationaware resource management. To this effect, this paper addresses a location and service-aware, downlink transmission power allocation problem (DTPA), the solution of which is suitable for the management or control domain of multiservice, WCDMA-based cellular networks. Given a specific traffic load situation that includes location and required service information, the solution of the DTPA problem aims at finding the optimum feasible allocation of power to the set of downlink connections that should be supported by the system. The problem is concisely defined, mathematically formulated and solved by a computationally efficient algorithm. Simulation and numerical results are presented. Keywords: WCDMA, location based services, pixel, multi-service, QoS, power allocation

1. INTRODUCTION A principal research area is the efficient planning and management of the air-interface of third generation (3G) cellular systems [1,2]. 3G systems are based on Wideband Code Division Multiple Access (WCDMA)[3]. Radio Resource Management (RRM) is responsible for efficient utilization of radio resources. Third generation mobile communication systems have to meet the high demand of emerging wideband multimedia services such as web browsing and streaming [4].

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RRM techniques in these systems have to distribute the available resources between different services according to their QoS requirements and tradeoffs. In addition, location based services (LBS) have been a promising and growing area in the domain of modern mobile technologies. Services like mobile advertising, traffic and tourist information or tracking need personalization and knowledge of user’s physical location enabled by technologies such as COO (Cell of Origin), GPS (Global Positioning System) and A-GPS (Assisted-GPS), AOA (Angle of Arrival), TOA (Time of Arrival)[5] etc. Such technologies often need device modifications that raise the energy consumption, thus limiting stand-by time and battery life. Besides, in the downlink direction, the distribution of the base station power among users is much influenced by their location inside the cells. Therefore, the need for advanced power and interference handling, [6], according to service and location information, appears to be imperative so as to guarantee the quality requirements for each service, maximize the system throughput and make the network cost effective. This paper addresses a problem, called Downlink Transmission Power Allocation (DTPA), which can be used for enhancing the performance of a WCDMA-based air-interface with much respect to service and location information. The general problem statement is: “Given the service area layout, the system description, the services and their requirements, and a demand pattern, find an optimum feasible allocation of power to the downlink connections”. The allocation is obtained by assuming fixed base station assignment. The optimisation of downlink power allocation can be assured by the minimisation of the aggregate transmission power in the system. Equivalently, the term “optimum”, here, is equivalent to “minimum”. The contribution of this paper is the concise statement and computationally efficient solution of a problem-version appropriate for the planning management and control processes in multi-service, WCDMA-based cellular networks.

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The herewith, proposed DTPA scheme introduces the novel concept of pixel approach. A pixel represents a specific geographical entity such as a road, a building or an office with particular properties, namely, geographical coordinates, serving cell etc. This novelty assists the provision of LBS according to pixel information and enables the efficient location and service-aware planning and management of the downlink power. Our work is relevant to various broad areas. The first is network planning tools [7]. The second area is the development of management functionality for legacy and 3G infrastructures [8,9]. The input and output are at a level of detail adequate for complementing legacy design processes. A low complexity solution is proposed. This can enable fast adaptations to traffic variations at the management domain. Moreover, the existence of positioning technologies, ([5]), make the problem's output useful in the control domain [10,11]. References [12,13,14,15] survey relevant work on power control and base station assignment. The pixel based DTPA scheme is implemented in a centralized manner. Nevertheless, it can be used to give bounds on the performance of the available, distributed power control algorithms [16]. The rest of this paper is organized as follows. The DTPA problem is formally described in section 2. The formulation and solution are given in sections 3,4. Finally, sections 5 and 6 include results and conclusions. 2. PROBLEM STATEMENT The input provides information on the service area layout, the propagation conditions, the services, the system description, and the demand pattern. Service area layout: The service area is split into a set of pixels, P . Each pixel p corresponds to a small part of the service area. A cell comprises several pixels and is split into zones around the

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NodeB (base station). The zones are determined according to the distance of the pixels from the respective NodeB. The set of cells is V . Set Pv ( v ∈ V ) provides the pixels of cell v . The location of the NodeB of v is given by function l (v ) , and one NodeB in the center of each cell is assumed. Likewise, a function, c( p ) , provides the cell to which a pixel p belongs. Propagation conditions: The attenuation factor for a downlink transmission that originates from the controlling NodeB of an arbitrary cell v and terminates at a pixel p ∈ Pv is denoted as Al (v ), p or also as Al (c ( p )), p . Service characteristics: The set of services is S . The QoS requirements of s ∈ S are expressed through the minimum signal-to-interference ratio, SIR s =

Rs W

⎛E ⎞ ⋅ ⎜⎜ b ⎟⎟ ⋅ a s , where Rs is the ⎝ Io ⎠s

⎛E ⎞

user bit rate, W is the chip rate, ⎜⎜ b ⎟⎟ is the minimum required "energy per bit divided by the ⎝ Io ⎠s

interference spectral density" and a s is the service activity factor. General system characteristics: The orthogonality factor in cell v is given by o d (v ) [3]. It takes values in the interval [0,1] where the orthogonality of 1 denotes perfectly orthogonal users. Apparently, the term (1 − o d (v )) provides this part of intracell interference in v that cannot be cancelled in the WCDMA despreading process. Finally, the equipment capabilities are expressed through the maximum transmission power of a NodeB, Qmax . ~ Demand pattern: The demand pattern is given by vector D = {d s , p ∀(s, p ) ∈ (S × P )} . Each

element, d s , p , provides the number of downlink transmissions of service s , terminating at pixel p.

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~ The DTPA solution is an allocation A = {tp(s, p ) ∀(s, p ) ∈ (S × P )} . Each element, tp(s, p ) ,

denotes the transmission power that should be allocated to a downlink transmission of service s ~ that terminates at p . Allocation A should maintain the QoS levels required by the transmissions,

ensure that the assigned powers are compatible with the Node-B capabilities, and minimise an

()

~ objective function ( OF A ) associated with the aggregate transmission power allocated. Function

( )

~ ATPd A, v provides the aggregate transmission power that will be used by the NodeB of cell v ~ according to allocation A .

3. PROBLEM FORMULATION The DTPA formulation is:

( ) ∑ ATP (A~, v )

~ OF A =

Minimise

(1)

d

v∈V

Subject to,

( ) ∑∑ d

~ ATPd A, v =

s, p

⋅ tp(s, p )

∀( v , p, s) ∈ (V × Pv × S )

(2)

SIRs 1 + (1 − od (v )) ⋅ SIRs

∀( v , p, s) ∈ (V × Pv × S )

(3)

s∈S p∈Pv

tp (s, p ) ⋅ Al (v ), p Qtot ( p )

≥ SIR s' =

( )

~ ATPd A, v ≤ Qmax

∀v ∈V

( )

~ Qtot ( p ) = (1 − od (v )) ⋅ ATPd A, v ⋅ Al (v ), p + I ext ( p ) + N 0 ⋅ W I ext ( p) =

ATP (A, w)⋅ A ( ) ∑ ( ) ~

d

l w ,p

(4)

∀(v, p ) ∈ (V × Pv )

(5)

∀(v, p ) ∈ (V × Pv )

(6)

w∈ V − v

The objective function in relation (1) arises from the summation, over all the cells, of the

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aggregate transmitted powers of the respective Node-Bs and is the function that has to be properly minimised. Relations (3) and (4) preserve the QoS requirements and equipment capabilities, respectively. The proof of relation (3) is given by lemma 1 in the Appendix. The quantity Qtot ( p ) in relation (5) gives the total effective power that each terminal, located in pixel p , receives and I ext ( p ) in relation (6) provides the external interference that each pixel p senses due to downlink connections in its neighboring cells. The thermal noise power is given by N 0 ⋅ W . 4. SOLUTION

The minimisation of (1) is achieved if constraints (3) stand with equality. Therefore: tp (s, p ) ⋅ Al (v ), p SIRs'

∀( v , p, s) ∈ (V × Pv × S ) (3a)

= Qtot ( p )

Besides, if we define Qd ( p ) as the amount of the effective power that should be dedicated by the Nodeb transmitter to pixel p , relation (7) is also obtained:

tp (s, p ) Qtot ( p ) = = Qd ( p ) Al (v ), p SIRs'

∀( v , p, s) ∈ (V × Pv × S ) (7)

~ Relation (7) shows that the DTPA solution (allocation A ) can be derived from the Qd ( p ) values.

Equation (8), that follows, can be obtained from (3a), (5) and (7). Correspondingly, equation (9) can be obtained from (6). Lemmas 2 and 3 in the Appendix give the detailed poof: ⎛

Qd ( p ) =

(1 − od (v )) ⋅ ∑∑ d s ,q ⋅ SIRs' ⋅ ⎜⎜ I ext s∈S q∈Pv

1 − (1 − o d (v )) ⋅

∑∑

(q ) + N 0 ⋅ W ⎞⎟

Al (v ),q ⎝ d s ,q ⋅ SIRs'

⎟ I ( p) + N ⋅W ⎠ + ext 0 Al ( v ), p

s∈S q∈Pv

∀(v, p ) ∈ (V × Pv )

(8)

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I ext ( p ) =

∑ ∑∑ d ( )

s ,q

∀(v, p ) ∈ (V × Pv )

⋅ Qd (q ) ⋅ SIR s' ⋅ Al ( w ), p

(9)

w∈ V − v q∈Pw s∈S

In the above system of P linear equations an iterative method is applied as follows: ⎛

Qdi ( p ) =

i −1

(1 − od (v )) ⋅ ∑∑ d s ,q ⋅ SIRs' ⋅ ⎜⎜ I ext s∈S q∈Pv

1 − (1 − o d (v )) ⋅

∑∑

(q ) + N 0 ⋅ W ⎞⎟

Al (v ),q ⎝ d s ,q ⋅ SIRs'

⎟ I i −1 ( p ) + N ⋅ W ⎠ + ext 0 Al ( v ), p

s∈S q∈Pv

i −1 ( p) = I ext

∑ ∑∑ d ( )

s ,q

⋅ Qdi −1 (q ) ⋅ SIR s' ⋅ Al (w ), p

∀(v, p ) ∈ (V × Pv )

(8a)

∀(v, p ) ∈ (V × Pv )

(9a)

w∈ V − v q∈Pw s∈S

The detailed derivation of the method is based on the classical Jacobi [17] iterative method and is omitted here for brevity. An iterative algorithm can be based on relations (8a), (9a), (3), (7) and (4). Figure 1 describes the general structure of the algorithm. Its formal description is as follows. Algorithm for the DTPA problem Step 1: Initialisation. Initialise the algorithm iteration counter, i = 1, and the initial values. For

each p ∈ P set Qd0 ( p ) = 0. i −1 Step 2: Computations. Compute for all p ∈ P the I ext ( p ) quantities through formulas (9a).

Compute for all p ∈ P the Qdi ( p ) quantities through formulas (8a). Step 3: Feasibility check. Compute the tp(s, p ) values by using the Qd ( p ) values and relation (7).

( )

~ Compute for all v ∈V the values ATPd A, v by using the relation (2). Check whether a constraint of set (4) is violated. In case of violation the algorithm fails and a transition to step 5 is conducted.

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Step 4: Convergence check. Evaluate whether the termination criteria are satisfied. The algorithm

should terminate when there is minor improvement to the solution over a series of

(

)

iterations, namely, Qdi ( p ) − Qdi −1 ( p ) Qdi −1 ( p ) < ε , ( ε